Fanizzi Annarita, Campione Arianna, Bove Samantha, Brunetti Oronzo, Guven Deniz Can, Cirillo Angelo, Lupo Andrea, Macrì Chiara, Ricchitelli Leonardo, Rizzo Alessandro, Vitale Elsa, Comes Maria Colomba, Massafra Raffaella
Laboratorio Biostatistica e Bioinformatica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy.
S.S.D. C.O.r.O. Bed Management Presa in Carico, TDM, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy.
Cancer Med. 2025 Sep;14(17):e70991. doi: 10.1002/cam4.70991.
Gender medicine is an evolving discipline that examines how diseases manifest and progress differently in men and women. Tailoring medical therapies and diagnostic approaches can enhance patient outcomes. While radiomics is emerging as a promising tool in personalized medicine, few studies evaluate its role in gender medicine within radiology. In this context, our preliminary objective was to determine whether radiomic features could predict disease-free survival within 3 years after the last follow-up in patients with colorectal liver metastases, with an emphasis on gender differences.
The study analyzed preoperative CT scans of 196 patients from The Cancer Imaging Archive who underwent resection of colorectal cancer liver metastasis. Using the Pyradiomics library, we extracted 1316 features for each patient. We developed an analysis framework applied initially to the entire patient sample, then separately to male and female subsamples. This framework included: Volume of Interest (VOI) segmentation, handcrafted feature extraction and selection, detection of confounding patients, and training of ensemble classification models comprising five classifiers. Performance was assessed through 100 rounds of 10-fold cross-validation.
The selected feature subsets for male and female subsamples showed no overlap. The ensemble model demonstrated a notable improvement in performance when trained on the female subsample (mean AUC of 80.5%) compared to the model trained on the entire dataset (mean AUC of 64.8%), while performance for the male subsample remained nearly unchanged.
Although further validation with a larger dataset and external confirmation is needed, these preliminary results suggest a meaningful impact of gender medicine in radiology.
性别医学是一门不断发展的学科，研究疾病在男性和女性中的表现及进展差异。调整医学治疗方法和诊断手段可改善患者预后。虽然影像组学在个性化医疗中逐渐成为一种有前景的工具，但很少有研究评估其在放射学领域性别医学中的作用。在此背景下，我们的初步目标是确定影像组学特征能否预测结直肠癌肝转移患者末次随访后3年内的无病生存期，并重点关注性别差异。
本研究分析了来自癌症影像存档库的196例接受结直肠癌肝转移切除术患者的术前CT扫描图像。使用Pyradiomics库，我们为每位患者提取了1316个特征。我们开发了一个分析框架，最初应用于整个患者样本，然后分别应用于男性和女性子样本。该框架包括：感兴趣体积（VOI）分割、手工特征提取与选择以及混杂患者检测，以及训练由五个分类器组成的集成分类模型。通过100轮10折交叉验证评估性能。
男性和女性子样本的选定特征子集没有重叠。与在整个数据集上训练的模型（平均AUC为64.8%）相比，在女性子样本上训练时，集成模型的性能有显著提高（平均AUC为80.5%），而男性子样本的性能几乎保持不变。
尽管需要用更大的数据集进行进一步验证并获得外部确认，但这些初步结果表明性别医学在放射学中具有重要影响。
