Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, PR China; National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha 410008, PR China.
Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, PR China; Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410008, PR China; Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha 410008, PR China; National Clinical Research Center for Geriatric Disorders, Changsha 410008, PR China.
Biomed Pharmacother. 2021 Jan;133:111013. doi: 10.1016/j.biopha.2020.111013. Epub 2020 Nov 20.
Early detection of platinum resistance for ovarian cancer treatment remains challenging. This study aims to develop a machine learning model incorporating genomic data such as Single-Nucleotide Polymorphisms (SNPs) of Human Sulfatase 1 (SULF1) with a CT radiomic model based on pre-treatment CT images, to predict platinum resistance for ovarian cancer (OC) treatment.
A cohort of 102 patients with pathologically confirmed OC was retrospectively enrolled into this study from January 2006 to February 2018. All patients had platinum-based chemotherapy after maximal cyto-reductive surgery. This cohort was separated into two groups according to treatment response, i.e., the group with platinum-resistant disease (PR group) and the group with platinum-sensitive disease (PS group). We genotyped 12 SNPs of SULF1 for all OC patients using Mass Array Method. Radiomic features, SNP data and clinicopathological data of the 102 patients were used to build the differentiation models. The study participants were divided into two cohorts: the training cohort (n = 71) and the validation cohort (n = 31). Feature selection and predictive modeling were performed using least absolute shrinkage and selection operator (LASSO), Random Forest Classifier and Support Vector Machine methods. Model performance for predicting platinum resistance was assessed with respect to its calibration, discrimination, and clinical application.
For prediction of platinum resistance, the approach combining the radiomics, clinicopathological data and SNP data demonstrated higher classification efficiency, with an AUC value of 0.993 (95 % CI: 0.83 to 0.98) in the training cohort and 0.967 (95 % CI: 0.83 to 0.98) in validation cohort, than the performance with only the SNPs of SULF1 model (AUC: training, 0.843 [95 %CI: 0.738-0.948]; validation, 0.815 [0.601-1.000]), or with only the radiomic model (AUC: training, 0.874 [95 %CI: 0.789-0.960]; validation, 0.832 [95 %CI: 0.687-0.976]). This integrated approach also showed good calibration and favorable clinical utility.
A predictive model combining pretreatment CT radiomics with genomic data such as SNPs of SULF1 could potentially help to predict platinum resistance in ovarian cancer treatment.
卵巢癌治疗中铂类耐药的早期检测仍然具有挑战性。本研究旨在开发一种机器学习模型，该模型将人类磺基转移酶 1（SULF1）的单核苷酸多态性（SNP）等基因组数据与基于治疗前 CT 图像的 CT 放射组学模型相结合，以预测卵巢癌（OC）治疗中的铂类耐药。
回顾性纳入 2006 年 1 月至 2018 年 2 月期间经病理证实的 102 例 OC 患者。所有患者在最大细胞减灭术后均接受铂类化疗。根据治疗反应将该队列分为铂耐药组（PR 组）和铂敏感组（PS 组）。我们使用 MassArray 方法对所有 OC 患者的 12 个 SULF1 SNP 进行了基因分型。对 102 例患者的放射组学特征、SNP 数据和临床病理数据进行建模。研究参与者分为两个队列：训练队列（n=71）和验证队列（n=31）。使用最小绝对值收缩和选择算子（LASSO）、随机森林分类器和支持向量机方法进行特征选择和预测建模。通过校准、区分和临床应用来评估预测铂耐药的模型性能。
对于铂耐药的预测，结合放射组学、临床病理数据和 SNP 数据的方法表现出更高的分类效率，在训练队列中的 AUC 值为 0.993（95%CI：0.83 至 0.98），在验证队列中的 AUC 值为 0.967（95%CI：0.83 至 0.98），优于仅基于 SULF1 SNP 的模型（AUC：训练，0.843[95%CI：0.738-0.948]；验证，0.815[0.601-1.000]），或仅基于放射组学模型（AUC：训练，0.874[95%CI：0.789-0.960]；验证，0.832[95%CI：0.687-0.976]）。这种综合方法也表现出良好的校准和良好的临床实用性。
结合预处理 CT 放射组学和 SULF1 SNP 等基因组数据的预测模型可能有助于预测卵巢癌治疗中的铂类耐药。
