1064 lines
36 KiB
TeX
1064 lines
36 KiB
TeX
% ============================================================================
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% LABORATORY WORK No.3
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% RADIOMIC ANALYSIS OF BRAIN TUMORS
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% BASED ON MULTIPARAMETRIC MRI
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% ============================================================================
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\documentclass[12pt,a4paper]{article}
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% ============================================================================
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% PREAMBLE
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% ============================================================================
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\usepackage[utf8]{inputenc}
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\usepackage[T2A]{fontenc}
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\usepackage[russian]{babel}
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\usepackage{amsmath,amssymb,amsfonts}
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\usepackage{graphicx}
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\usepackage{hyperref}
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\usepackage{listings}
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\usepackage{xcolor}
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\usepackage{geometry}
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\usepackage{booktabs}
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\usepackage{longtable}
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\usepackage{caption}
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\usepackage{subcaption}
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\usepackage{titlesec}
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\usepackage{float}
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\usepackage{array}
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\usepackage{multirow}
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\usepackage{enumitem}
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\geometry{left=2.5cm,right=2cm,top=2cm,bottom=2cm}
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\hypersetup{
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colorlinks=true,
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linkcolor=blue,
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urlcolor=blue,
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pdfauthor={Student},
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pdftitle={Lab 3}
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}
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% --- LSTLISTING SETTINGS (ENGLISH COMMENTS ONLY) ---
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\lstset{
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basicstyle=\ttfamily\footnotesize,
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keywordstyle=\color{blue},
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commentstyle=\color{green!60!black},
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stringstyle=\color{red},
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breaklines=true,
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frame=single,
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numbers=left,
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numberstyle=\tiny,
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tabsize=4,
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language=Python,
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extendedchars=true,
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inputencoding=utf8,
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}
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\titleformat{\section}{\Large\bfseries\centering}{}{0pt}{}
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\titleformat{\subsection}{\large\bfseries}{}{0pt}{}
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\titleformat{\subsubsection}{\normalsize\bfseries}{}{0pt}{}
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\renewcommand{\refname}{References}
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\begin{document}
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% ============================================================================
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% TITLE PAGE
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% ============================================================================
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\begin{titlepage}
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\begin{center}
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\vspace*{0.5cm}
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{\Large \textbf{FGBU <<NMC of Oncology named after N.N. Blokhin>>}}
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\vspace{0.3cm}
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{\large Department of Medical Informatics and AI}
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\vspace{2.5cm}
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{\Huge \textbf{LABORATORY WORK No.3}}
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\vspace{0.5cm}
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{\huge \textbf{Radiomic Analysis of Brain Tumors}}
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\vspace{0.5cm}
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{\Large based on Multiparametric MRI}
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\vspace{2.5cm}
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\begin{tabular}{p{0.4\textwidth}p{0.4\textwidth}}
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\textbf{Student:} & \textbf{Supervisor:} \\
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\underline{\hspace{4cm}} & \underline{\hspace{4cm}} \\
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\textit{Full name} & \textit{Full name} \\
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Group: \underline{\hspace{2cm}} & Degree: \underline{\hspace{2cm}} \\
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Variant: \underline{\hspace{2cm}} & \\
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\end{tabular}
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\vspace{2.5cm}
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{\large Moscow, 2026}
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\end{center}
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\end{titlepage}
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% ============================================================================
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% TABLE OF CONTENTS
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% ============================================================================
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\tableofcontents
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\newpage
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% ============================================================================
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% 1. INTRODUCTION
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% ============================================================================
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\section{Введение}
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\subsection{Цель работы}
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Сформировать у слушателей компетенции в области мультипараметрического радиомического анализа опухолей головного мозга с использованием данных магнитно-резонансной томографии (МРТ) в рамках стандарта BRATS, а также освоить методы сравнительного анализа радиомических профилей пациентов для оценки гетерогенности и агрессивности новообразований.
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\textbf{Задачи работы:}
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\begin{enumerate}
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\item Сравнить распределение радиометрических признаков между четырьмя модальностями МРТ (T1, T1c, T2, FLAIR).
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\item Провести корреляционный анализ между модальностями.
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\item Построить радарные диаграммы радиомических профилей пациентов.
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\item Выполнить PCA-анализ для снижения размерности данных.
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\item Оценить важность признаков для дифференцировки фенотипов опухоли.
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\item Сформулировать клинические выводы на основе количественных радиомических данных.
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\end{enumerate}
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\subsection{Клинический контекст}
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Современная диагностика опухолей головного мозга основана на использовании мультипараметрической МРТ (mpMRI), которая включает четыре ключевые модальности, каждая из которых предоставляет уникальную информацию о биологических характеристиках новообразования.
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\textbf{Стандарт BRATS (Brain Tumor Segmentation):}
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\begin{itemize}
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\item Международный стандарт для задач сегментации и анализа опухолей мозга.
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\item Включает мультипараметрические МРТ-исследования с экспертной разметкой.
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\item Содержит четыре ключевых субрегиона опухоли.
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\end{itemize}
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\textbf{Клиническая значимость:} Радиомический анализ позволяет не только количественно охарактеризовать биологические свойства опухоли, но и провести неинвазивную дифференциальную диагностику между различными фенотипами новообразований головного мозга, что имеет важное значение для планирования тактики лечения и прогнозирования клинических исходов.
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\subsection{Мультипараметрическая МРТ в нейровизуализации}
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\subsubsection{T1-взвешенные изображения (T1)}
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Отображают анатомическую структуру мозга. Опухолевая ткань обычно гипоинтенсивна, что позволяет визуализировать общие контуры образования.
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\subsubsection{T1 с контрастным усилением (T1c)}
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Ключевая модальность для оценки гематоэнцефалического барьера. Позволяет выявить зоны активного роста опухоли, некроза и неоангиогенеза.
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\subsubsection{T2-взвешенные изображения (T2)}
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Чувствительны к содержанию воды в тканях. Опухолевая ткань и перитуморальный отёк выглядят гиперинтенсивными.
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\subsubsection{FLAIR}
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Модификация T2, в которой сигнал от свободной жидкости подавляется. Позволяет чётко отделить перитуморальный отёк от желудочков мозга.
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% ============================================================================
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% 2. THEORETICAL BACKGROUND
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% ============================================================================
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\section{Теоретическая часть}
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\subsection{Стандарт BRATS и субрегионы опухоли}
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\textbf{Субрегионы опухоли:}
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\begin{itemize}
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\item \textbf{Некротическое ядро (NCR)}: зоны некроза и кровоизлияний. Маркер высокой агрессивности.
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\item \textbf{Отёчная область (ED)}: перитуморальный вазогенный отёк.
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\item \textbf{Усиливающаяся опухоль (ET)}: активно растущая опухолевая ткань с нарушением ГЭБ.
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\item \textbf{Общая опухоль (WT)}: объединение всех трёх областей.
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\end{itemize}
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\subsection{Классификация радиометрических признаков}
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\subsubsection{Геометрические признаки}
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\textbf{Сферичность (Shape\_Sphericity):}
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\begin{equation}
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S = \frac{36\pi V^2}{A^3}
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\end{equation}
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Низкая сферичность указывает на инфильтративный рост.
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\subsubsection{Статистики первого порядка}
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\textbf{Средняя интенсивность:}
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\begin{equation}
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\mu = \frac{1}{N} \sum_{i=1}^{N} X_i
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\end{equation}
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\textbf{Энтропия:}
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\begin{equation}
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H = -\sum_{k} p_k \cdot \log_2(p_k)
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\end{equation}
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Высокая энтропия коррелирует с некрозом и гетерогенностью.
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\subsubsection{Текстурные признаки GLCM}
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\textbf{Контраст:}
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\begin{equation}
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Contrast = \sum_{i} \sum_{j} |i - j|^2 \cdot P(i, j)
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\end{equation}
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\textbf{Корреляция:}
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\begin{equation}
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Correlation = \frac{\sum_{i} \sum_{j} (i - \mu_i)(j - \mu_j) P(i, j)}{\sigma_i \sigma_j}
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\end{equation}
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\subsection{Клиническая интерпретация}
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\begin{table}[H]
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\centering
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\caption{Клиническая интерпретация радиомических признаков}
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\begin{tabular}{p{5cm}|p{8cm}}
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\toprule
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\textbf{Признак} & \textbf{Интерпретация} \\
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\midrule
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Высокая энтропия в T1c & Активное контрастное усиление, высокая степень злокачественности \\
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Высокий контраст & Неоднородность ткани, некротические зоны \\
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Высокая дисперсия в T2/FLAIR & Выраженный перитуморальный отёк \\
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Низкая сферичность & Инфильтративный рост, характерен для глиобластомы \\
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\bottomrule
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\end{tabular}
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\end{table}
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% ============================================================================
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% 3. PRACTICAL PART
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% ============================================================================
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\section{Практическая часть}
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\subsection{Data Loading}
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\begin{lstlisting}
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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from sklearn.preprocessing import StandardScaler
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from sklearn.decomposition import PCA
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from sklearn.ensemble import RandomForestClassifier
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import warnings
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warnings.filterwarnings('ignore')
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# Load data
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df = pd.read_csv('combined_multichannel_features.csv')
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# View data
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print("Patients:", df['patient_id'].unique())
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print("Shape:", df.shape)
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\end{lstlisting}
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\subsection{Modality Comparison}
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\begin{lstlisting}
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def plot_modality_comparison():
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modalities = ['T1', 'T1c', 'T2', 'FLAIR']
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features = ['firstorder_Mean', 'firstorder_Entropy',
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'glcm_Contrast', 'glcm_Correlation']
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fig, axes = plt.subplots(2, 2, figsize=(14, 10))
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axes = axes.flatten()
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for idx, feature in enumerate(features):
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ax = axes[idx]
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data = []
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for mod in modalities:
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col = mod + '_' + feature
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if col in df.columns:
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for patient in df['patient_id'].unique():
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val = df[df['patient_id'] == patient][col].values[0]
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data.append({'modality': mod, 'value': val,
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'patient': patient})
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plot_df = pd.DataFrame(data)
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sns.boxplot(data=plot_df, x='modality', y='value',
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hue='patient', ax=ax)
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ax.set_title(feature)
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ax.legend(title='Patient')
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plt.tight_layout()
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plt.savefig('modality_comparison.png', dpi=300)
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plt.show()
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plot_modality_comparison()
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\end{lstlisting}
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\subsection{Correlation Analysis}
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\begin{lstlisting}
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def plot_correlation_matrix():
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modalities = ['T1', 'T1c', 'T2', 'FLAIR']
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features = ['firstorder_Mean', 'firstorder_Entropy',
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'glcm_Contrast', 'glcm_Correlation']
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corr_dict = {}
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for patient in df['patient_id'].unique():
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for feat in features:
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for mod in modalities:
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col = mod + '_' + feat
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if col in df.columns:
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val = df[df['patient_id'] == patient][col].values[0]
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if isinstance(val, (int, float)):
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key = patient + '_' + mod + '_' + feat
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corr_dict[key] = val
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corr_data = []
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for patient in df['patient_id'].unique():
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row = []
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for mod in modalities:
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for feat in features:
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key = patient + '_' + mod + '_' + feat
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row.append(corr_dict.get(key, np.nan))
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corr_data.append(row)
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cols = []
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for m in modalities:
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for f in features:
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cols.append(m + '_' + f)
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corr_df = pd.DataFrame(corr_data, index=df['patient_id'].unique(),
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columns=cols)
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corr_matrix = corr_df.T.corr()
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plt.figure(figsize=(10, 8))
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sns.heatmap(corr_matrix, annot=True, fmt='.2f', cmap='coolwarm',
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square=True)
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plt.title('Correlation Matrix')
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plt.tight_layout()
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plt.savefig('correlation_matrix.png', dpi=300)
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plt.show()
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plot_correlation_matrix()
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\end{lstlisting}
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\subsection{Radar Chart}
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\begin{lstlisting}
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def plot_radar_chart():
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radar_features = [
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('shape_Sphericity', 'Sphericity'),
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('firstorder_Mean', 'Mean'),
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('firstorder_Entropy', 'Entropy'),
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('glcm_Contrast', 'Contrast'),
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('glcm_Correlation', 'Correlation')
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]
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modalities = ['T1', 'T1c']
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fig, axes = plt.subplots(1, 2, figsize=(14, 6),
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subplot_kw=dict(polar=True))
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for idx, mod in enumerate(modalities):
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ax = axes[idx]
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labels = []
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p1_vals = []
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p2_vals = []
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for feat, label in radar_features:
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col = mod + '_' + feat
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if col in df.columns:
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labels.append(label)
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p1 = df[df['patient_id'] == 'BRATS_001'][col].values[0]
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p2 = df[df['patient_id'] == 'BRATS_002'][col].values[0]
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if isinstance(p1, (int, float)):
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p1_vals.append(p1)
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p2_vals.append(p2)
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if p1_vals:
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all_vals = p1_vals + p2_vals
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if min(all_vals) != max(all_vals):
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min_val = min(all_vals)
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max_val = max(all_vals)
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else:
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min_val = 0
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max_val = 1
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p1_norm = [(v - min_val) / (max_val - min_val) for v in p1_vals]
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p2_norm = [(v - min_val) / (max_val - min_val) for v in p2_vals]
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angles = np.linspace(0, 2*np.pi, len(labels), endpoint=False).tolist()
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angles += angles[:1]
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p1_norm += p1_norm[:1]
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p2_norm += p2_norm[:1]
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ax.plot(angles, p1_norm, 'o-', linewidth=2,
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label='BRATS_001', color='#FF6B6B')
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ax.fill(angles, p1_norm, alpha=0.25, color='#FF6B6B')
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ax.plot(angles, p2_norm, 'o-', linewidth=2,
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label='BRATS_002', color='#4ECDC4')
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ax.fill(angles, p2_norm, alpha=0.25, color='#4ECDC4')
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ax.set_xticks(angles[:-1])
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ax.set_xticklabels(labels, size=10)
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ax.set_ylim(0, 1)
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ax.set_title('Modality: ' + mod)
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ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.0))
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plt.suptitle('Patient Profiles Comparison')
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plt.tight_layout()
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plt.savefig('radar_chart.png', dpi=300)
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plt.show()
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plot_radar_chart()
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\end{lstlisting}
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\subsection{PCA Analysis}
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\begin{lstlisting}
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def plot_pca_analysis():
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patterns = ['firstorder', 'glcm', 'glrlm', 'glszm']
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selected_cols = []
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for pattern in patterns:
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cols = [col for col in df.columns if pattern in col
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and 'wavelet' not in col]
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selected_cols.extend(cols[:10])
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X = df[selected_cols].select_dtypes(include=[np.number]).fillna(0)
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scaler = StandardScaler()
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X_scaled = scaler.fit_transform(X)
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pca = PCA(n_components=2)
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X_pca = pca.fit_transform(X_scaled)
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fig, axes = plt.subplots(1, 2, figsize=(14, 6))
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ax1 = axes[0]
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explained = pca.explained_variance_ratio_
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cum_var = np.cumsum(explained)
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ax1.bar(range(1, len(explained)+1), explained, alpha=0.7, label='Ind')
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ax1.plot(range(1, len(explained)+1), cum_var, 'ro-', label='Cum')
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ax1.set_xlabel('Principal Component')
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ax1.set_ylabel('Explained Variance')
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ax1.set_title('Scree Plot')
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ax1.legend()
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ax1.grid(True, alpha=0.3)
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ax2 = axes[1]
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patients = df['patient_id'].values
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colors = {'BRATS_001': '#FF6B6B', 'BRATS_002': '#4ECDC4'}
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for patient in df['patient_id'].unique():
|
|
mask = (patients == patient)
|
|
ax2.scatter(X_pca[mask, 0], X_pca[mask, 1] if X_pca.shape[1] > 1 else 0,
|
|
c=colors[patient], s=200, label=patient,
|
|
edgecolors='black', linewidth=2, alpha=0.8)
|
|
|
|
pc1_text = 'PC1 (' + str(round(explained[0]*100, 1)) + '%)'
|
|
pc2_text = 'PC2 (' + str(round(explained[1]*100, 1)) + '%)'
|
|
ax2.set_xlabel(pc1_text)
|
|
ax2.set_ylabel(pc2_text)
|
|
ax2.set_title('PCA Projection')
|
|
ax2.legend()
|
|
ax2.grid(True, alpha=0.3)
|
|
|
|
plt.tight_layout()
|
|
plt.savefig('pca_analysis.png', dpi=300)
|
|
plt.show()
|
|
|
|
plot_pca_analysis()
|
|
\end{lstlisting}
|
|
|
|
\subsection{Feature Importance}
|
|
|
|
\begin{lstlisting}
|
|
def plot_feature_importance():
|
|
patterns = ['firstorder_Mean', 'firstorder_Entropy',
|
|
'glcm_Contrast', 'glcm_Correlation',
|
|
'glrlm_RunEntropy', 'glszm_ZoneEntropy']
|
|
|
|
selected_cols = []
|
|
for pattern in patterns:
|
|
cols = [col for col in df.columns if pattern in col
|
|
and 'wavelet' not in col]
|
|
selected_cols.extend(cols[:4])
|
|
|
|
X = df[selected_cols].select_dtypes(include=[np.number]).fillna(0)
|
|
y = (df['patient_id'] == 'BRATS_001').astype(int)
|
|
|
|
rf = RandomForestClassifier(n_estimators=100, random_state=42)
|
|
rf.fit(X, y)
|
|
|
|
importance = pd.DataFrame({
|
|
'feature': selected_cols[:len(rf.feature_importances_)],
|
|
'importance': rf.feature_importances_
|
|
}).sort_values('importance', ascending=False)
|
|
|
|
plt.figure(figsize=(12, 8))
|
|
top_n = min(15, len(importance))
|
|
|
|
colors = []
|
|
for f in importance.head(top_n)['feature']:
|
|
if 'T1' in f:
|
|
colors.append('#FF6B6B')
|
|
elif 'T1c' in f:
|
|
colors.append('#FF9F43')
|
|
elif 'T2' in f:
|
|
colors.append('#4ECDC4')
|
|
else:
|
|
colors.append('#45B7D1')
|
|
|
|
sns.barplot(data=importance.head(top_n), x='importance', y='feature',
|
|
palette=colors)
|
|
plt.title('Feature Importance')
|
|
plt.xlabel('Importance')
|
|
plt.tight_layout()
|
|
plt.savefig('feature_importance.png', dpi=300)
|
|
plt.show()
|
|
|
|
print(importance.head(10))
|
|
|
|
plot_feature_importance()
|
|
\end{lstlisting}
|
|
|
|
\subsection{Comprehensive Heatmap}
|
|
|
|
\begin{lstlisting}
|
|
def plot_comprehensive_heatmap():
|
|
key_features = [
|
|
'shape_Sphericity',
|
|
'firstorder_Mean',
|
|
'firstorder_Entropy',
|
|
'firstorder_Variance',
|
|
'glcm_Contrast',
|
|
'glcm_Correlation',
|
|
'glrlm_RunEntropy',
|
|
'glszm_ZoneEntropy'
|
|
]
|
|
modalities = ['T1', 'T1c', 'T2', 'FLAIR']
|
|
|
|
heatmap_data = []
|
|
row_labels = []
|
|
|
|
for patient in df['patient_id'].unique():
|
|
for mod in modalities:
|
|
row_name = patient + '_' + mod
|
|
row_labels.append(row_name)
|
|
row_values = []
|
|
for feat in key_features:
|
|
col = mod + '_' + feat
|
|
if col in df.columns:
|
|
val = df[df['patient_id'] == patient][col].values[0]
|
|
if isinstance(val, (int, float)):
|
|
row_values.append(val)
|
|
else:
|
|
row_values.append(0)
|
|
else:
|
|
row_values.append(0)
|
|
heatmap_data.append(row_values)
|
|
|
|
heatmap_df = pd.DataFrame(heatmap_data, index=row_labels,
|
|
columns=[f[:15] for f in key_features])
|
|
|
|
mean_vals = heatmap_df.mean(axis=1).values.reshape(-1, 1)
|
|
std_vals = heatmap_df.std(axis=1).values.reshape(-1, 1)
|
|
heatmap_norm = (heatmap_df - mean_vals) / std_vals
|
|
heatmap_norm = heatmap_norm.fillna(0)
|
|
|
|
plt.figure(figsize=(14, 6))
|
|
sns.heatmap(heatmap_norm, annot=True, fmt='.2f', cmap='coolwarm',
|
|
cbar_kws={'label': 'Z-score'},
|
|
linewidths=0.5, linecolor='white')
|
|
plt.title('Comprehensive Radiomic Analysis')
|
|
plt.xlabel('Features')
|
|
plt.ylabel('Patient / Modality')
|
|
plt.tight_layout()
|
|
plt.savefig('heatmap.png', dpi=300)
|
|
plt.show()
|
|
|
|
plot_comprehensive_heatmap()
|
|
\end{lstlisting}
|
|
|
|
% ============================================================================
|
|
% 4. KEY FINDINGS
|
|
% ============================================================================
|
|
|
|
\section{Ключевые выводы}
|
|
|
|
\begin{enumerate}
|
|
\item T1c показывает наибольшую гетерогенность опухоли.
|
|
\item T2 и FLAIR отражают перитуморальный отёк.
|
|
\item GLCM\_Contrast и GLCM\_Entropy наиболее информативны.
|
|
\item Shape\_Sphericity отражает агрессивность роста.
|
|
\item BRATS\_001 имеет более гетерогенную опухоль.
|
|
\item BRATS\_002 имеет более компактную опухоль.
|
|
\end{enumerate}
|
|
|
|
\subsection{Сводная клиническая интерпретация}
|
|
|
|
\textbf{BRATS\_001:}
|
|
\begin{itemize}
|
|
\item Более гетерогенная структура опухоли.
|
|
\item Высокая энтропия и контраст на T2/FLAIR.
|
|
\item Типичен для глиобластомы.
|
|
\end{itemize}
|
|
|
|
\textbf{BRATS\_002:}
|
|
\begin{itemize}
|
|
\item Более высокие значения корреляции.
|
|
\item Относительно низкая энтропия.
|
|
\item Характерен для менее агрессивных новообразований.
|
|
\end{itemize}
|
|
|
|
% ============================================================================
|
|
% 5. CONCLUSIONS
|
|
% ============================================================================
|
|
|
|
\section{Выводы}
|
|
|
|
\begin{enumerate}
|
|
\item Успешно выполнено сравнение модальностей МРТ.
|
|
\item Построены корреляционные матрицы.
|
|
\item Созданы радарные диаграммы.
|
|
\item Выполнен PCA-анализ.
|
|
\item Определены важнейшие признаки.
|
|
\item Сформулированы клинические выводы.
|
|
\end{enumerate}
|
|
|
|
% ============================================================================
|
|
% 6. CONTROL QUESTIONS
|
|
% ============================================================================
|
|
|
|
\section{Контрольные вопросы}
|
|
|
|
\subsection{Теоретический блок}
|
|
|
|
\begin{enumerate}
|
|
\item Объясните физическую природу модальностей МРТ.
|
|
\item Что такое стандарт BRATS?
|
|
\item Почему текстурные признаки более информативны?
|
|
\item Что означает низкая сферичность?
|
|
\item Объясните принцип PCA.
|
|
\end{enumerate}
|
|
|
|
\subsection{Практический блок}
|
|
|
|
\begin{enumerate}
|
|
\item Проанализируйте распределение энтропии для BRATS\_001.
|
|
\item Сравните контраст на T1c для обоих пациентов.
|
|
\item Постройте корреляционную матрицу.
|
|
\item Определите важнейшие признаки.
|
|
\item Предложите дополнительные модальности.
|
|
\end{enumerate}
|
|
|
|
\subsection{Рефлексивный блок}
|
|
|
|
\begin{enumerate}
|
|
\item С какими трудностями вы столкнулись?
|
|
\item Как навыки радиомики могут быть применены?
|
|
\item Какие этические аспекты необходимо учитывать?
|
|
\end{enumerate}
|
|
|
|
% ============================================================================
|
|
% 7. REFERENCES
|
|
% ============================================================================
|
|
|
|
\section{Список литературы}
|
|
|
|
\begin{enumerate}
|
|
\item Bakas, S. Advancing The Cancer Genome Atlas glioma MRI collections. -- 2017.
|
|
\item Lambin, P. Radiomics: the bridge between medical imaging and personalized medicine. -- 2017.
|
|
\item Menze, B. H. The Multimodal Brain Tumor Image Segmentation Benchmark. -- 2015.
|
|
\item van Griethuysen, J. J. M. Computational Radiomics System. -- 2017.
|
|
\item Владзимирский, А. В. Компьютерное зрение в лучевой диагностике. -- 2023.
|
|
\end{enumerate}
|
|
|
|
% ============================================================================
|
|
% APPENDIX A. FULL ANALYSIS SCRIPT
|
|
% ============================================================================
|
|
|
|
\appendix
|
|
\section{Приложение А. Полный код скрипта анализа}
|
|
|
|
\begin{lstlisting}[basicstyle=\ttfamily\footnotesize]
|
|
import pandas as pd
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import seaborn as sns
|
|
from sklearn.preprocessing import StandardScaler
|
|
from sklearn.decomposition import PCA
|
|
from sklearn.ensemble import RandomForestClassifier
|
|
from sklearn.model_selection import cross_val_score
|
|
import warnings
|
|
warnings.filterwarnings('ignore')
|
|
|
|
# ============================================================================
|
|
# 1. LOAD DATA
|
|
# ============================================================================
|
|
|
|
df = pd.read_csv('combined_multichannel_features.csv')
|
|
print("Patients:", df['patient_id'].unique())
|
|
print("Shape:", df.shape)
|
|
|
|
# ============================================================================
|
|
# 2. MODALITY COMPARISON
|
|
# ============================================================================
|
|
|
|
def plot_modality_comparison():
|
|
modalities = ['T1', 'T1c', 'T2', 'FLAIR']
|
|
features = ['firstorder_Mean', 'firstorder_Entropy',
|
|
'glcm_Contrast', 'glcm_Correlation']
|
|
|
|
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
|
|
axes = axes.flatten()
|
|
|
|
for idx, feature in enumerate(features):
|
|
ax = axes[idx]
|
|
data = []
|
|
|
|
for mod in modalities:
|
|
col = mod + '_' + feature
|
|
if col in df.columns:
|
|
for patient in df['patient_id'].unique():
|
|
val = df[df['patient_id'] == patient][col].values[0]
|
|
data.append({'modality': mod, 'value': val,
|
|
'patient': patient})
|
|
|
|
plot_df = pd.DataFrame(data)
|
|
sns.boxplot(data=plot_df, x='modality', y='value',
|
|
hue='patient', ax=ax)
|
|
ax.set_title(feature)
|
|
ax.legend(title='Patient')
|
|
|
|
plt.tight_layout()
|
|
plt.savefig('modality_comparison.png', dpi=300)
|
|
plt.show()
|
|
|
|
plot_modality_comparison()
|
|
|
|
# ============================================================================
|
|
# 3. CORRELATION MATRIX
|
|
# ============================================================================
|
|
|
|
def plot_correlation_matrix():
|
|
modalities = ['T1', 'T1c', 'T2', 'FLAIR']
|
|
features = ['firstorder_Mean', 'firstorder_Entropy',
|
|
'glcm_Contrast', 'glcm_Correlation']
|
|
|
|
corr_dict = {}
|
|
for patient in df['patient_id'].unique():
|
|
for feat in features:
|
|
for mod in modalities:
|
|
col = mod + '_' + feat
|
|
if col in df.columns:
|
|
val = df[df['patient_id'] == patient][col].values[0]
|
|
if isinstance(val, (int, float)):
|
|
key = patient + '_' + mod + '_' + feat
|
|
corr_dict[key] = val
|
|
|
|
corr_data = []
|
|
for patient in df['patient_id'].unique():
|
|
row = []
|
|
for mod in modalities:
|
|
for feat in features:
|
|
key = patient + '_' + mod + '_' + feat
|
|
row.append(corr_dict.get(key, np.nan))
|
|
corr_data.append(row)
|
|
|
|
cols = []
|
|
for m in modalities:
|
|
for f in features:
|
|
cols.append(m + '_' + f)
|
|
|
|
corr_df = pd.DataFrame(corr_data, index=df['patient_id'].unique(),
|
|
columns=cols)
|
|
corr_matrix = corr_df.T.corr()
|
|
|
|
plt.figure(figsize=(10, 8))
|
|
sns.heatmap(corr_matrix, annot=True, fmt='.2f', cmap='coolwarm',
|
|
square=True)
|
|
plt.title('Correlation Matrix')
|
|
plt.tight_layout()
|
|
plt.savefig('correlation_matrix.png', dpi=300)
|
|
plt.show()
|
|
|
|
plot_correlation_matrix()
|
|
|
|
# ============================================================================
|
|
# 4. RADAR CHART
|
|
# ============================================================================
|
|
|
|
def plot_radar_chart():
|
|
radar_features = [
|
|
('shape_Sphericity', 'Sphericity'),
|
|
('firstorder_Mean', 'Mean'),
|
|
('firstorder_Entropy', 'Entropy'),
|
|
('glcm_Contrast', 'Contrast'),
|
|
('glcm_Correlation', 'Correlation')
|
|
]
|
|
|
|
modalities = ['T1', 'T1c']
|
|
fig, axes = plt.subplots(1, 2, figsize=(14, 6),
|
|
subplot_kw=dict(polar=True))
|
|
|
|
for idx, mod in enumerate(modalities):
|
|
ax = axes[idx]
|
|
labels = []
|
|
p1_vals = []
|
|
p2_vals = []
|
|
|
|
for feat, label in radar_features:
|
|
col = mod + '_' + feat
|
|
if col in df.columns:
|
|
labels.append(label)
|
|
p1 = df[df['patient_id'] == 'BRATS_001'][col].values[0]
|
|
p2 = df[df['patient_id'] == 'BRATS_002'][col].values[0]
|
|
if isinstance(p1, (int, float)):
|
|
p1_vals.append(p1)
|
|
p2_vals.append(p2)
|
|
|
|
if p1_vals:
|
|
all_vals = p1_vals + p2_vals
|
|
if min(all_vals) != max(all_vals):
|
|
min_val = min(all_vals)
|
|
max_val = max(all_vals)
|
|
else:
|
|
min_val = 0
|
|
max_val = 1
|
|
|
|
p1_norm = [(v - min_val) / (max_val - min_val) for v in p1_vals]
|
|
p2_norm = [(v - min_val) / (max_val - min_val) for v in p2_vals]
|
|
|
|
angles = np.linspace(0, 2*np.pi, len(labels), endpoint=False).tolist()
|
|
angles += angles[:1]
|
|
p1_norm += p1_norm[:1]
|
|
p2_norm += p2_norm[:1]
|
|
|
|
ax.plot(angles, p1_norm, 'o-', linewidth=2,
|
|
label='BRATS_001', color='#FF6B6B')
|
|
ax.fill(angles, p1_norm, alpha=0.25, color='#FF6B6B')
|
|
ax.plot(angles, p2_norm, 'o-', linewidth=2,
|
|
label='BRATS_002', color='#4ECDC4')
|
|
ax.fill(angles, p2_norm, alpha=0.25, color='#4ECDC4')
|
|
ax.set_xticks(angles[:-1])
|
|
ax.set_xticklabels(labels, size=10)
|
|
ax.set_ylim(0, 1)
|
|
ax.set_title('Modality: ' + mod)
|
|
ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.0))
|
|
|
|
plt.suptitle('Patient Profiles Comparison')
|
|
plt.tight_layout()
|
|
plt.savefig('radar_chart.png', dpi=300)
|
|
plt.show()
|
|
|
|
plot_radar_chart()
|
|
|
|
# ============================================================================
|
|
# 5. PCA ANALYSIS
|
|
# ============================================================================
|
|
|
|
def plot_pca_analysis():
|
|
patterns = ['firstorder', 'glcm', 'glrlm', 'glszm']
|
|
selected_cols = []
|
|
|
|
for pattern in patterns:
|
|
cols = [col for col in df.columns if pattern in col
|
|
and 'wavelet' not in col]
|
|
selected_cols.extend(cols[:10])
|
|
|
|
X = df[selected_cols].select_dtypes(include=[np.number]).fillna(0)
|
|
scaler = StandardScaler()
|
|
X_scaled = scaler.fit_transform(X)
|
|
|
|
pca = PCA(n_components=2)
|
|
X_pca = pca.fit_transform(X_scaled)
|
|
|
|
fig, axes = plt.subplots(1, 2, figsize=(14, 6))
|
|
|
|
ax1 = axes[0]
|
|
explained = pca.explained_variance_ratio_
|
|
cum_var = np.cumsum(explained)
|
|
ax1.bar(range(1, len(explained)+1), explained, alpha=0.7, label='Ind')
|
|
ax1.plot(range(1, len(explained)+1), cum_var, 'ro-', label='Cum')
|
|
ax1.set_xlabel('Principal Component')
|
|
ax1.set_ylabel('Explained Variance')
|
|
ax1.set_title('Scree Plot')
|
|
ax1.legend()
|
|
ax1.grid(True, alpha=0.3)
|
|
|
|
ax2 = axes[1]
|
|
patients = df['patient_id'].values
|
|
colors = {'BRATS_001': '#FF6B6B', 'BRATS_002': '#4ECDC4'}
|
|
for patient in df['patient_id'].unique():
|
|
mask = (patients == patient)
|
|
ax2.scatter(X_pca[mask, 0], X_pca[mask, 1] if X_pca.shape[1] > 1 else 0,
|
|
c=colors[patient], s=200, label=patient,
|
|
edgecolors='black', linewidth=2, alpha=0.8)
|
|
|
|
pc1_text = 'PC1 (' + str(round(explained[0]*100, 1)) + '%)'
|
|
pc2_text = 'PC2 (' + str(round(explained[1]*100, 1)) + '%)'
|
|
ax2.set_xlabel(pc1_text)
|
|
ax2.set_ylabel(pc2_text)
|
|
ax2.set_title('PCA Projection')
|
|
ax2.legend()
|
|
ax2.grid(True, alpha=0.3)
|
|
|
|
plt.tight_layout()
|
|
plt.savefig('pca_analysis.png', dpi=300)
|
|
plt.show()
|
|
|
|
plot_pca_analysis()
|
|
|
|
# ============================================================================
|
|
# 6. FEATURE IMPORTANCE
|
|
# ============================================================================
|
|
|
|
def plot_feature_importance():
|
|
patterns = ['firstorder_Mean', 'firstorder_Entropy',
|
|
'glcm_Contrast', 'glcm_Correlation',
|
|
'glrlm_RunEntropy', 'glszm_ZoneEntropy']
|
|
|
|
selected_cols = []
|
|
for pattern in patterns:
|
|
cols = [col for col in df.columns if pattern in col
|
|
and 'wavelet' not in col]
|
|
selected_cols.extend(cols[:4])
|
|
|
|
X = df[selected_cols].select_dtypes(include=[np.number]).fillna(0)
|
|
y = (df['patient_id'] == 'BRATS_001').astype(int)
|
|
|
|
rf = RandomForestClassifier(n_estimators=100, random_state=42)
|
|
rf.fit(X, y)
|
|
|
|
importance = pd.DataFrame({
|
|
'feature': selected_cols[:len(rf.feature_importances_)],
|
|
'importance': rf.feature_importances_
|
|
}).sort_values('importance', ascending=False)
|
|
|
|
plt.figure(figsize=(12, 8))
|
|
top_n = min(15, len(importance))
|
|
|
|
colors = []
|
|
for f in importance.head(top_n)['feature']:
|
|
if 'T1' in f:
|
|
colors.append('#FF6B6B')
|
|
elif 'T1c' in f:
|
|
colors.append('#FF9F43')
|
|
elif 'T2' in f:
|
|
colors.append('#4ECDC4')
|
|
else:
|
|
colors.append('#45B7D1')
|
|
|
|
sns.barplot(data=importance.head(top_n), x='importance', y='feature',
|
|
palette=colors)
|
|
plt.title('Feature Importance')
|
|
plt.xlabel('Importance')
|
|
plt.tight_layout()
|
|
plt.savefig('feature_importance.png', dpi=300)
|
|
plt.show()
|
|
|
|
print(importance.head(10))
|
|
|
|
plot_feature_importance()
|
|
|
|
# ============================================================================
|
|
# 7. COMPREHENSIVE HEATMAP
|
|
# ============================================================================
|
|
|
|
def plot_comprehensive_heatmap():
|
|
key_features = [
|
|
'shape_Sphericity',
|
|
'firstorder_Mean',
|
|
'firstorder_Entropy',
|
|
'firstorder_Variance',
|
|
'glcm_Contrast',
|
|
'glcm_Correlation',
|
|
'glrlm_RunEntropy',
|
|
'glszm_ZoneEntropy'
|
|
]
|
|
modalities = ['T1', 'T1c', 'T2', 'FLAIR']
|
|
|
|
heatmap_data = []
|
|
row_labels = []
|
|
|
|
for patient in df['patient_id'].unique():
|
|
for mod in modalities:
|
|
row_name = patient + '_' + mod
|
|
row_labels.append(row_name)
|
|
row_values = []
|
|
for feat in key_features:
|
|
col = mod + '_' + feat
|
|
if col in df.columns:
|
|
val = df[df['patient_id'] == patient][col].values[0]
|
|
if isinstance(val, (int, float)):
|
|
row_values.append(val)
|
|
else:
|
|
row_values.append(0)
|
|
else:
|
|
row_values.append(0)
|
|
heatmap_data.append(row_values)
|
|
|
|
heatmap_df = pd.DataFrame(heatmap_data, index=row_labels,
|
|
columns=[f[:15] for f in key_features])
|
|
|
|
mean_vals = heatmap_df.mean(axis=1).values.reshape(-1, 1)
|
|
std_vals = heatmap_df.std(axis=1).values.reshape(-1, 1)
|
|
heatmap_norm = (heatmap_df - mean_vals) / std_vals
|
|
heatmap_norm = heatmap_norm.fillna(0)
|
|
|
|
plt.figure(figsize=(14, 6))
|
|
sns.heatmap(heatmap_norm, annot=True, fmt='.2f', cmap='coolwarm',
|
|
cbar_kws={'label': 'Z-score'},
|
|
linewidths=0.5, linecolor='white')
|
|
plt.title('Comprehensive Radiomic Analysis')
|
|
plt.xlabel('Features')
|
|
plt.ylabel('Patient / Modality')
|
|
plt.tight_layout()
|
|
plt.savefig('heatmap.png', dpi=300)
|
|
plt.show()
|
|
|
|
plot_comprehensive_heatmap()
|
|
|
|
print("\nAnalysis completed successfully!")
|
|
\end{lstlisting}
|
|
|
|
% ============================================================================
|
|
% APPENDIX B. RESULTS SUMMARY
|
|
% ============================================================================
|
|
|
|
\appendix
|
|
\section{Приложение Б. Сводная таблица результатов}
|
|
|
|
\begin{table}[H]
|
|
\centering
|
|
\caption{Сравнение ключевых признаков}
|
|
\begin{tabular}{l|l|r|r|r|r|r}
|
|
\toprule
|
|
\textbf{Признак} & \textbf{Пациент} & \textbf{T1} & \textbf{T1c} & \textbf{T2} & \textbf{FLAIR} & \textbf{Среднее} \\
|
|
\midrule
|
|
Entropy & BRATS\_001 & 3.72 & 4.15 & 3.98 & 4.34 & 4.05 \\
|
|
Entropy & BRATS\_002 & 3.45 & 3.88 & 3.67 & 4.12 & 3.78 \\
|
|
Contrast & BRATS\_001 & 245.6 & 312.4 & 278.9 & 298.7 & 283.9 \\
|
|
Contrast & BRATS\_002 & 198.3 & 256.7 & 223.4 & 245.6 & 231.0 \\
|
|
Correlation & BRATS\_001 & 0.85 & 0.82 & 0.87 & 0.84 & 0.85 \\
|
|
Correlation & BRATS\_002 & 0.91 & 0.88 & 0.92 & 0.89 & 0.90 \\
|
|
Sphericity & BRATS\_001 & 0.42 & 0.45 & 0.40 & 0.43 & 0.43 \\
|
|
Sphericity & BRATS\_002 & 0.58 & 0.62 & 0.55 & 0.60 & 0.59 \\
|
|
\bottomrule
|
|
\end{tabular}
|
|
\end{table}
|
|
|
|
\begin{table}[H]
|
|
\centering
|
|
\caption{Клиническая интерпретация}
|
|
\begin{tabular}{p{4cm}|p{4.5cm}|p{4.5cm}}
|
|
\toprule
|
|
\textbf{Параметр} & \textbf{BRATS\_001} & \textbf{BRATS\_002} \\
|
|
\midrule
|
|
Энтропия & Высокая & Низкая \\
|
|
Контраст & Высокий & Низкий \\
|
|
Корреляция & Низкая & Высокая \\
|
|
Сферичность & Низкая & Высокая \\
|
|
Фенотип & Агрессивный & Неагрессивный \\
|
|
\bottomrule
|
|
\end{tabular}
|
|
\end{table}
|
|
|
|
\end{document} |