Paripooranan Nadesalingam, Nirasha Warnakulasuriya Buddhini, Perera H R P, Vijithananda Sahan M, Hewavithana P Badra, Sherminie Lahanda Purage Givanthika, Jayatilake Mohan L
Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka.
Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka.
Front Oncol. 2025 Jun 3;15:1588787. doi: 10.3389/fonc.2025.1588787. eCollection 2025.
Breast cancer is considered one of the most lethal diseases among women worldwide. Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC) are the two most prominent subtypes of breast cancer. They differ in epidemiology, molecular alterations, and clinicopathological features. Patient treatment and management also differ due to these variations.
The study aimed to develop a predictive model to differentiate IDC and ILC using machine learning techniques based on the morphological features of the contralateral breast. Methods- 143 magnetic resonance imaging (MRI) images were sourced from the "DUKE Breast-Cancer" collection on the Cancer Imaging Archive website. Regions of interest were drawn on each slice to compute the morphological features of the contralateral breast using the 3D Slicer application. Supervised learning methods were applied to the morphological features to build a predictive model incorporating a Random Forest Classifier to differentiate IDC and ILC. Hyperparameters were tuned to optimize the model.
The model was able to differentiate IDC and ILC with an accuracy of 79% and an Area Under the Curve of 0.851 on the Receiver Operating Characteristic Curve. Among the morphological features, the total volume of the contralateral breast, surface area of the contralateral breast, breast density, and the ratio of the total volume of the contralateral breast to its surface area had higher F-scores, indicating that the dimensions of the contralateral breast could be an important factor in differentiating IDC and ILC.
This study successfully developed and optimized a predictive model based on breast morphological features to differentiate IDC and ILC using machine learning methods.
乳腺癌被认为是全球女性中最致命的疾病之一。浸润性导管癌（IDC）和浸润性小叶癌（ILC）是乳腺癌的两种最主要亚型。它们在流行病学、分子改变和临床病理特征方面存在差异。由于这些差异，患者的治疗和管理也有所不同。
本研究旨在基于对侧乳房的形态特征，利用机器学习技术开发一种预测模型，以区分IDC和ILC。方法：从癌症影像存档网站上的“杜克乳腺癌”数据集中获取了143张磁共振成像（MRI）图像。使用3D Slicer应用程序在每个切片上绘制感兴趣区域，以计算对侧乳房的形态特征。将监督学习方法应用于这些形态特征，构建一个包含随机森林分类器的预测模型，以区分IDC和ILC。对超参数进行调整以优化模型。
该模型能够区分IDC和ILC，在受试者工作特征曲线上的准确率为79%，曲线下面积为0.851。在形态特征中，对侧乳房的总体积、对侧乳房的表面积、乳房密度以及对侧乳房总体积与其表面积的比值具有较高的F值，这表明对侧乳房的尺寸可能是区分IDC和ILC的一个重要因素。
本研究成功开发并优化了一种基于乳房形态特征的预测模型，利用机器学习方法区分IDC和ILC。
