Chiappa Valentina, Bogani Giorgio, Interlenghi Matteo, Vittori Antisari Giulia, Salvatore Christian, Zanchi Lucia, Ludovisi Manuela, Leone Roberti Maggiore Umberto, Calareso Giuseppina, Haeusler Edward, Raspagliesi Francesco, Castiglioni Isabella
Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale Tumori di Milano, 20133 Milan, Italy.
DeepTrace Technologies S.R.L., 20126 Milan, Italy.
Diagnostics (Basel). 2023 Oct 6;13(19):3139. doi: 10.3390/diagnostics13193139.
Neoadjuvant chemotherapy plus radical surgery could be a safe alternative to chemo-radiation in cervical cancer patients who are not willing to receive radiotherapy. The response to neoadjuvant chemotherapy is the main factor influencing the need for adjunctive treatments and survival. In the present paper we aim to develop a machine learning model based on cervix magnetic resonance imaging (MRI) images to stratify the single-subject risk of cervical cancer. We collected MRI images from 72 subjects. Among these subjects, 28 patients (38.9%) belonged to the "Not completely responding" class and 44 patients (61.1%) belonged to the 'Completely responding' class according to their response to treatment. This image set was used for the training and cross-validation of different machine learning models. A robust radiomic approach was applied, under the hypothesis that the radiomic features could be able to capture the disease heterogeneity among the two groups. Three models consisting of three ensembles of machine learning classifiers (random forests, support vector machines, and k-nearest neighbor classifiers) were developed for the binary classification task of interest ("Not completely responding" vs. "Completely responding"), based on supervised learning, using response to treatment as the reference standard. The best model showed an ROC-AUC (%) of 83 (majority vote), 82.3 (mean) [79.9-84.6], an accuracy (%) of 74, 74.1 [72.1-76.1], a sensitivity (%) of 71, 73.8 [68.7-78.9], and a specificity (%) of 75, 74.2 [71-77.5]. In conclusion, our preliminary data support the adoption of a radiomic-based approach to predict the response to neoadjuvant chemotherapy.
对于不愿意接受放疗的宫颈癌患者，新辅助化疗联合根治性手术可能是一种安全的替代放化疗的方法。对新辅助化疗的反应是影响辅助治疗需求和生存的主要因素。在本文中，我们旨在开发一种基于宫颈磁共振成像（MRI）图像的机器学习模型，以对宫颈癌患者的个体风险进行分层。我们收集了72名受试者的MRI图像。在这些受试者中，根据治疗反应，28例患者（38.9%）属于“未完全缓解”组，44例患者（61.1%）属于“完全缓解”组。该图像集用于不同机器学习模型的训练和交叉验证。在放射组学特征能够捕捉两组疾病异质性的假设下，应用了一种稳健的放射组学方法。基于监督学习，以治疗反应作为参考标准，开发了由三个机器学习分类器集合（随机森林、支持向量机和k近邻分类器）组成的三个模型，用于感兴趣的二元分类任务（“未完全缓解”与“完全缓解”）。最佳模型的受试者工作特征曲线下面积（ROC-AUC）（%）为83（多数投票）、82.3（均值）[79.9 - 84.6]，准确率（%）为74、74.1[72.1 - 76.1]，灵敏度（%）为71、73.8[68.7 - 78.9]，特异度（%）为75、74.2[71 - 77.5]。总之，我们的初步数据支持采用基于放射组学的方法来预测新辅助化疗的反应。
