Machine Learning-Based Models for Assessing Postoperative Risk Factors in Patients with Cervical Cancer.

RATIONALE AND OBJECTIVES: To investigate the value of machine learning-based radiomics, intravoxel incoherent motion (IVIM) diffusion-weighted imaging and its combined model in predicting the postoperative risk factors of parametrial infiltration (PI), lymph node metastasis (LNM), deep muscle invasion (DMI), lymph-vascular space invasion (LVSI), pathological type (PT), differentiation degree (DD), and Ki-67 expression level in patients with cervical cancer. MATERIALS AND METHODS: The data of 180 patients with cervical cancer were retrospectively analyzed and randomized 2:1 into a training and validation group. The IVIM-DWI and radiomics parameters of primary lesions were measured in all patients. Seven machine learning methods were used to calculate the optimal radiomics score (Rad-score), which was combined with IVIM-DWI and clinical parameters to construct nomograms for predicting the risk factors of cervical cancer, with internal and external validation. RESULTS: The diagnostic efficacy of the nomograms based on clinical and imaging parameters was significantly better than MRI assessment alone. The area under the curve (AUC) of nomograms and MRI for the assessment of PI, LNM, and DMI were 0.981 vs 0.868, 0.848 vs 0.639, and 0.896 vs 0.780, respectively. Nomograms also performed well in the assessment of LVSI, PT, DD, and Ki-67 expression levels, with AUC of 0.796, 0.854, 0.806, 0.839 and 0.840, 0.856, 0.810, 0.832 in the training and validation groups. CONCLUSION: Machine learning-based nomograms can serve as a useful tool for assessing postoperative risk factors in patients with cervical cancer.

Machine learning-based radiomics for predicting outcomes in cervical cancer patients undergoing concurrent chemoradiotherapy.

PURPOSES: To investigate the value of machine learning-based radiomics for predicting disease-free survival (DFS) and overall survival (OS) undergoing concurrent chemoradiotherapy (CCRT) for patients with locally advanced cervical cancer (LACC). MATERIALS AND METHODS: In this multicentre study, 700 patients with IB2-IVA cervical cancer who underwent CCRT with ongoing follow-up were retrospectively analyzed. Three-dimensional radiomics features of primary lesions and its surrounding 5 mm region in T2WI sequences were collected. Six machine learning methods were used to construct the optimal radiomics model for accurate prediction of DFS and OS after CCRT in LACC patients. Eventually, TCGA and GEO databases were used to explore the mechanisms of radiomics in predicting the progression and survival of cervical cancer. This study adhered CLEAR for reporting and its quality was assessed using RQS and METRICS. RESULTS: In the prediction of DFS, the RSF model combined tumor and peritumor radiomics demonstrated the best predictive efficacy, with the AUC for predicting 1-year, 3-year, and 5-year DFS in the training, validation, and test sets of 0.986, 0.989, 0.990, and 0.884, 0.838, 0.823, and 0.829, 0.809, 0.841, respectively. In the prediction of OS, the GBM model best performer, with AUC of 0.999, 0.995, 0.978, and 0.981, 0.975, 0.837, and 0.904, 0.860, 0.905. Differential genes in TCGA and GEO suggest that the prediction of radiomics model may be associated with KDELR2 and HK2. CONCLUSION: Machine learning-based radiomics models help to predict DFS and OS after CCRT in LACC patients, and the combination of tumor and peritumor information has higher predictive efficacy, which can provide a reliable basis for therapeutic decision-making in cervical cancer patients.