Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review

Introduction

Gynecological cancer is a group of malignancies affecting the female reproductive system, with endometrial cancer, ovarian cancer, and cervical cancer being the most common. According to the GLOBOCAN estimates, there were an estimated 1,473,427 new cases of gynecological cancer and 680,372 deaths worldwide in 2022 (1). Globally, cervical cancer is the most common gynecological cancer, whereas ovarian cancer is the most lethal (2). Each gynecological cancer has a distinct pathogenesis and thus different clinical presentations. Pathogenesis and treatments of gynecological cancer, such as endometrial cancer, have evolved significantly during the last decade (3). Although significant therapeutic advancements have been made, effective treatment strategies are still lacking (4). Gynecological cancer can significantly affect women’s quality of life and cause a substantial burden to the healthcare system (5,6). Early diagnosis and individualized treatment remain the key in the management of gynecological cancer.

Radiomics is a rapidly developing field in oncology that applies advanced computational techniques to extract and mine high-dimensional quantitative features from medical images [e.g., magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasonography (US)] (7). It has shown potential in the entire cancer patient journey: from early diagnosis to treatment decision support and prognosis prediction (8). Artificial intelligence (AI) refers to the ability of a computer to perform tasks that normally require human intelligence, such as image processing (9). The last decade has witnessed increasing development and implementation of AI-based methods in oncology imaging (10). As a combination of radiomics and AI techniques, AI radiomics has been increasingly developed to assist clinicians in clinical cancer care. The typical workflow of AI radiomics consists of imaging data collection and pre-processing, tumor segmentation, feature extraction and selection, and model construction and validation (Figure 1).

Figure 1 Typical workflow of AI radiomics. AI, artificial intelligence; CT, computed tomography; ICCs, intra-class correlation coefficients; LASSO, least absolute shrinkage and selection operator; LR, logistic regression; MRI, magnetic resonance imaging; PET-CT, positron emission tomography/computed tomography; SVM, support vector machine; US, ultrasonography.

Substantial progress has been made with AI radiomics in clinical oncology such as lung and breast cancers (11,12). In gynecological cancer, Shrestha et al. conducted a systematic review reporting the use of AI in gynecologic imaging in 2022 (13). In 2024, Moro et al. summarized the applications of AI to ultrasound in gynecology cancer (14). Leo et al. reviewed AI and MRI-based radiomics in risk stratification and prognostic prediction of endometrial cancer (15). Meanwhile, Di Donato et al. assessed the role of MRI radiomics analysis for the prediction of molecular or clinicopathological prognostic factors, including tumor grading, deep myometrial invasion (MI), lymphovascular space invasion (LVSI), and nodal metastasis, in endometrial cancer (16). Egemen et al. summarized AI-based image analysis in cervical cancer screening (17). Sadeghi et al. reviewed deep learning (DL) and various imaging modalities in ovarian cancer diagnosis (18). However, most of the previous reviews have focused on the diagnosis, treatment, or prognosis of gynecological cancer or specific subtypes or using specific imaging data. Herein, we conducted a comprehensive review of the available literature on the application of AI radiomics in the management of gynecological cancer. This study provides an overview of AI radiomics use in gynecological cancer, including current landscape, challenges, and future directions. We present this article in accordance with the Narrative Review reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-618/rc).

Methods

We searched the databases of PubMed, Embase, and Web of Science from January 2019 to September 2024 to explore the use of AI radiomics in gynecological cancer (Table 1). All identified studies were imported into EndNote software (Clarivate, Philadelphia, PA, USA) and screened according to the eligibility criteria. The inclusion criteria were studies involving AI radiomics in gynecological cancer (endometrial cancer, ovarian cancer, and cervical cancer) and including information related to tumor diagnosis (e.g., diagnosis and characterization), or treatment response or prognosis (recurrence, metastasis, or survival) prediction. Studies using AI radiomics for treatment plans of radiotherapy or using AI in field other than radiomics (such as colposcopy) were out of the scope of this review and therefore excluded. Other exclusion criteria were studies with article types other than original journal articles (e.g., case reports, letters, editorials, reviews), without full text (e.g., conference abstract only), or those not reported in English.

In total, 128 studies were included in the final analysis (Figure S1). The applications were grouped based on clinical scenarios in which they were used (Figure 2). Data were extracted for each purpose of use.

Figure 2 Overview of AI radiomics in gynecological oncology. AI radiomics is being developed for clinical application across the patient journey, in which we group its applications into three main categories: diagnosis and characterization, treatment response, and prognosis. AI, artificial intelligence; LNM, lymph node metastasis.

Tumor diagnosis and characterization

Tumor diagnosis

Accuracy in tumor diagnosis and characterization is undoubtedly a key issue in the management of gynecological cancer, which can sometimes be challenging when using conventional imaging techniques. Among 23 studies identified in tumor diagnosis, 15 (65.2%) were performed on ovarian cancer (Table 2).

Cervical cancer

Urushibara et al. constructed an MRI-based model, Xception, that showed diagnostic performance equivalent to that of experienced radiologists in cervical cancer, with an area under the curve (AUC) of 0.931 (19). Furthermore, Yang et al. developed an interpretable model based on clinical and ultrasound-radiomics data for the diagnosis of stage I cervical cancer. The combined model showed good discriminative performance, with AUCs of 0.837, 0.828, and 0.839 in the training, internal validation, and external validation sets, respectively (20).

Endometrial cancer

Six studies were identified in endometrial cancer (21-26). For example, an MRI-based DL model developed by Urushibara et al. using convolutional neural networks (CNNs) demonstrated diagnostic performance not inferior to that of the radiologists (23). Bi et al. evaluated the performance of multiparametric MRI-based radiomics models in the differential diagnosis of stage IA endometrial cancer from benign lesions. Nine machine learning (ML) algorithms were used to determine the optimal radiomics model. The nomogram based on clinical and optimal radiomics models showed excellent and stable performance in the internal and external validation sets (AUCs of 0.917 and 0.802, respectively), suggesting that multiparametric MRI-based radiomics models could differentiate stage IA endometrial cancer in a non-invasive way (22). Moreover, Virarkar et al. analyzed MRI-based radiomics data from patients with malignant mixed Müllerian tumors and endometrial cancer to identify distinguishing features for differential diagnosis of these tumors (25). In addition to MRI, radiomics models based on CT or ultrasound were also developed for differential diagnosis of endometrial cancer (21,26).

Ovarian cancer

Several studies have attempted AI radiomics methods based on CT, MRI, or ultrasound for the differential diagnosis of cervical cancer (27-41). Almost all of these studies were single-center and retrospective. Hu et al. combined CT radiomics and clinical data to differentiate between primary and secondary ovarian cancers achieving an AUC of 0.854 in the training cohort and 0.71 in the internal validation cohort (28). In a single-center retrospective study, Li et al. included 1,329 patients with ovarian tumors, and six ML-based CT radiomics models and a clinical-radiomics mixed model were developed to distinguish benign and malignant tumors. The mixed model showed differentiation performance superior to that of radiomics models and senior radiologists (AUC: 0.93–0.96 vs. 0.88–0.91 vs. 0.78 in the validation cohorts) (33). A multiple-instance learning method based on multimodal MRI, MAC-Net, was used to differentiate between malignant and borderline ovarian lesions in a multicenter retrospective study by Jian et al. The MAC-Net outperformed several known multiple-instance learning approaches (AUC: 0.878 vs. 0.817–0.863) (30). Regarding ultrasound, Du et al. developed an ultrasound-based DL radiomics nomogram that showed a diagnostic capability comparable to that of the ovarian-adnexal reporting and data system (O-RADS) (AUC: 0.928 vs. 0.960 in the testing set). Moreover, the nomogram also exhibited superior performance in risk classification of malignancy in O-RADS 4 and 5 ovarian tumors (AUC: 0.869 in the testing set) (37). Liu et al. included 1,080 patients with ovarian masses from two hospitals to develop and validate an ultrasound-based clinical-radiomics nomogram in the risk classification of ovarian lesions. The nomogram achieved a higher performance when compared with the junior radiologists in the external test cohort (AUC: 0.930 vs. 0.802). With the assistance of the nomogram, the diagnostic performance of the junior radiologists was significantly improved (the average AUC: 0.929) (41).

Tumor staging

Nine studies were identified to use AI radiomics for tumor staging; most were performed on cervical cancer (n=6, 67.7%) and used MRI radiomics data (n=6, 67.7%) (Table 2).

Cervical cancer

Among the six studies on cervical cancer (42-47), only one study by Cheng et al. included more than 100 patients. To address the challenge of small sample size in their study, a sequence enhancement strategy was introduced to diversify samples and mitigate overfitting. They developed a deformable multi-sequence guidance model (DMGM) based on a novel deformable ConvLSTM module that enables the model to adapt to varying data. External validation using the multi-modal data from BraTS 2019 demonstrated the effectiveness of the DMGM model in tumor staging (46). Additionally, Umutlu et al. used multiparametric PET/MR imaging to develop an ML model for predicting node and metastasis staging. The developed model showed high performance for non-invasive tumor phenotyping (44).

Endometrial cancer

Two studies were included for endometrial cancer staging. Mao et al. developed an automatic DL-based prediction model for early endometrial cancer on MRI images using the area ratio of the tumor region to the uterine region as a reference (48). Interestingly, Li et al. proposed a latent relation shared learning method to overcome the incomplete multi-modality imaging data (e.g., MRI, ultrasound, and contrast-enhanced ultrasound). By jointly exploiting the consistent and complementary information from multi-modality data, the new method outperformed the current representative approaches. The method performed well under varying imaging missing rates (49).

Ovarian cancer

The only study by Sadeghi et al. created three-dimensional (3D) CNN systems (the OCDA-Net) based on whole-body PET/CT for the classification and staging of ovarian cancer. The OCDAc-Net achieved an AUC of 0.990 for diagnostic classification and the OCDAs-Net achieved an AUC of 0.995 for tumor staging (50).

Histological subtyping

Five studies were identified to use MRI/CT-based AI radiomics for histological subtyping; all were conducted on epithelial ovarian cancer (51-55) (Table 2). Jian et al. constructed an ML model based on MRI to differentiate between type I and type II epithelial ovarian cancers. The combined model based on four MRI sequences showed differential performance superior to each of the single-parametric radiomics models in both internal and external validation cohorts (51). ML models based on CT-based radiomics data were developed for histological subtyping of epithelial ovarian cancers (53). Again, DL methods were used to differentiate type Ι and type ΙΙ epithelial ovarian cancers using MRI radiomics (54,55).

Lymph node metastases

Among 15 studies using AI radiomics for lymph node metastasis (LNM) prediction, most were performed on cervical cancer (n=12, 80%) (56-67), followed by endometrial cancer (n=2, 13%) and ovarian cancer (n=1, 7%) (Table 3). All studies were retrospective except one.

Cervical cancer

Eight studies used MRI or CT radiomics for model development in cervical cancer. Wu et al. developed an MRI-based DL model for LNM prediction in cervical cancer. The predictive performance of three MRI sequences and different regions (intratumoral and peritumoral) were explored to find the optimal model. The DL model based on contrast-enhanced T1-weighted imaging in both the intratumoral and peritumoral regions showed the best performance (AUCs of 0.894 and 0.844 in the training and validation cohorts, respectively). The predictive performance was further improved by combining the optimal DL model and MRI lymph node status in a hybrid model (AUCs of 0.963 and 0.933 in the training and validation cohorts, respectively) (57). Similarly, clinical and MRI radiomics data-integrated models were developed by Liu et al. (61) and Qin et al. (65) to predict LNM in cervical cancer. In addition, Liu et al. developed a CT-based radiomics model using an artificial neural network to predict normal-sized LNM in cervical cancer. The developed model exhibited better performance, with AUCs of 0.912, 0.859, and 0.800 in the training, internal validation, and external validation cohorts, respectively (58). Monthatip et al. showcased the performance of an ML model based on clinical and CT radiomics data to increase the accuracy in predicting pelvic LNM (63). In addition to MRI or CT radiomics, PET/CT radiomics was used by Zhang et al. to develop an ML model for predicting pelvic LNM (AUCs of 0.817 and 0.786 in the training and testing sets, respectively) (59). Notably, Lucia et al. combined clinical, PET/CT, and MRI radiomics data to develop an ML model (ComBat-combined model) for para-aortic LNM prediction (62).

Endometrial cancer

Two studies used AI radiomics for LNM prediction in endometrial cancer. In a prospective observational study, Yang et al. developed an ML model based on clinical and MRI-based radiomics, the RadSignature, that yielded an excellent predictive performance (AUC: 0.85) (68). Besides, the nomogram based on ultrasound radiomics and clinical features developed by Liu et al. provided good predictive performance (AUCs of 0.919 and 0.884 in the training and testing sets, respectively) (69).

Ovarian cancer

The only study by Yao et al. developed a residual neural network based on PET/CT to predict LNM in patients with primary ovarian cancer. The developed model could effectively predict LNM (AUCs of 0.947 and 0.926 in the training and testing sets, respectively) (70).

LVSI

We identified five studies using AI radiomics for LVSI prediction; all were retrospective and based on MRI radiomics data (71-75) (Table 3). Three studies were conducted on cervical cancer and two were on endometrial cancer.

Cervical cancer

Huang et al. developed a multi-parametric MRI-combined radiomics model achieving a significantly better performance than the clinical model or any single-layer model (AUC: 0.940 vs. 0.730 vs. 0.650–0.840) (71). Wu et al. developed a nomogram by integrating radiomics and clinical features, achieving high predictive performance (AUCs of 0.883 and 0.830 in the training and external test cohorts, respectively) (73). Additionally, Wang et al. developed a habitat-based MRI radiomics model for LVSI prediction (72).

Endometrial cancer

Luo et al. developed a predictive nomogram incorporating multiparametric MRI radiomics and clinical features. The nomogram achieved an AUC of 0.820 and 0.807 in the training and test cohorts, respectively (74). Yan et al. used multiparametric MRI radiomic features from intratumoral and peritumoral regions for LVSI prediction in endometrial adenocarcinoma, resulting in AUCs of 0.962 and 0.965 of the nomogram integrating clinical-tumor morphological features and radiomics in the training and validation cohorts, respectively (75).

MI

Six studies were identified to use AI radiomics for MI prediction in endometrial cancer; all used MRI-based radiomics data (76-81) (Table 3). Chen et al. conducted one of the largest studies on 530 patients. Using T2-weighted imaging-based MRI data, a DL model was developed and showed a competitive and time-efficient performance in determining deep MI compared to the radiologists (accuracy: 84.8% vs. 78.3%) (76). Lefebvre et al. introduced spherical harmonics (SPHARM) into an ML pipeline to develop an image descriptor based on multiparametric MRI for predicting deep MI and high-grade histology. The SPHARM descriptor yielded similar or even higher performance than radiomics (AUC: 0.94 vs. 0.92 for predicting deep MI; 0.81 vs. 0.72 for high-grade histology) (81).

Moreover, several studies have used AI radiomics for risk classification in other scenarios, such as cervical stromal invasion (82,83) and tumor budding (84,85) in cervical cancer. Finally, in addition to the studies focusing on specific risk factors, several reviewed studies used AI radiomics for risk classification of multiple factors (86-94) (Table 3). In particular, Wang et al. developed a multi-task DL model to predict LNM and LVSI based on MRI (94).

Other molecular or clinicopathological factors

AI radiomics has also been introduced to predict other molecular or clinicopathological factors. A total of 10 studies were identified in endometrial or ovarian cancer (95-104) (Table 4). Li et al. incorporated MRI-based radiomics and clinicopathological features to develop an ML model for predicting microsatellite instability (MSI) and programmed cell death ligand 1 (PD-L1) expression status. The combined model achieved AUCs of 0.848 and 0.764 for predicting MSI and PD-L1 status in the validation set, respectively (101). Jia et al. developed multiparametric MRI radiomics models using five ML methods [logistic regression (LR), k-nearest neighbors (KNN), Naive Bayes (NB), support vector machine (SVM), and random forest (RF)]. The SVM model outperformed the others and showed the best performance (AUC: 0.905, 0.875, and 0.862 in the training, internal, and external validation cohorts, respectively) (102). Wang et al. explored the efficacy of DL models based on diffusion-weighted imaging for MSI prediction. The AUCs of 0.989 and 0.885 were achieved in the optimal model (103). Other molecular phenotypes have been explored with AI radiomics, for example, hypoxia patterns and immunological characteristics (95), human papillomavirus status (97), tissue heterogeneity (98), breast cancer susceptibility gene (BRCA) or tumor protein 53 (TP53) mutations (96,104), and C-C motif chemokine receptor type 5 (CCR5) or lymphocyte cell-specific protein-tyrosine kinase (LCK) expression (99,100).

To sum up, numerous studies have focused on AI radiomics in the diagnosis and characterization of gynecological cancer. However, most of the studies are retrospective and involve only one or a few centers. Various AI radiomics-based models are available, with substantial heterogeneity in study methodology and results reporting. Prospective large-scale studies are required to validate the reliability and applicability of the methods reported using a rigorous evaluation system.

Treatment response

Chemotherapy response

Four studies used AI radiomics based on CT or MRI to predict response to neoadjuvant chemotherapy (neo-CT) in cervical or ovarian cancer (Table 5) (105-108).

Cervical cancer

Chiappa et al. used MRI radiomics and ML to predict response to neo-CT in locally advanced cervical cancer (LACC). The findings of their study support the adoption of an AI radiomic-based approach to predict the neo-CT response (105). Additionally, Zhang et al. constructed a multiparametric MRI-based DL radiomics nomogram in LACC. The nomogram was demonstrated to perform well in predicting the neo-CT response and could possibly aid in individualized treatment (106).

Ovarian cancer

Crispin-Ortuzar et al. integrated CT radiomics data to baseline clinical and blood data and showed a reduced response prediction error by 8% to neo-CT in patients with high-grade serous ovarian carcinoma (HGSOC) (107). Yin et al. developed a multitask DL model based on contrast-enhanced CT images and reported promising performances in Neo-CT response prediction and HGSOC classification (108).

Chemoradiotherapy response

Five studies used MRI radiomics to predict responses to neoadjuvant (neo) or concurrent chemoradiotherapy (CRT) in cervical cancer (109-113).

Cervical cancer

Fang et al. developed a radiomics model based on multiparametric MRI for predicting response to concurrent CRT in LACC. The multi-habitat model showed good predictive performance in the training and validation cohorts, with AUCs of 0.820 and 0.798, respectively, suggesting the ability of the radiomics model in treatment response prediction in LACC before concurrent CRT (113). Meanwhile, the studies by Yang et al. and Cai et al. developed and validated MRI-based DL models for predicting neo-CRT response in LACC (109,110). Furthermore, Jeong et al. compared DL and handcrafted radiomics models for predicting neo-CRT response in LACC. The results showed that both DL and handcrafted radiomics could predict neo-CRT responses (111).

Platinum resistance/sensitivity in ovarian cancer

Six studies used AI radiomics to predict platinum resistance/sensitivity in patients with ovarian cancer (114-119) (Table 5). Yi et al. developed an ML model by incorporating genomic data (SULF1 polymorphisms) with a CT radiomics model that could potentially predict platinum resistance in ovarian cancer (AUCs of 0.99 and 0.97 in the training and validation cohorts, respectively) (115). Lei et al. developed MRI-based DL models using the primary tumor or the whole abdomen as the volume of interest to predict platinum sensitivity in epithelial ovarian cancer. The whole-abdomen DL model showed satisfactory performance (AUCs of 0.97 and 0.98 in the training and validation cohorts, respectively) and might assist in optimal treatment-making (116). Na et al. established a radiomics framework by combining MRI radiomics and clinical data. The combined model showed the incremental benefits in predicting platinum sensitivity in advanced ovarian cancer (117). Additionally, Zhuang et al. developed a DL model based on multimodal PET/CT images to discriminate platinum resistance from platinum sensitivity, with an AUC of 0.93 using a five-fold cross-validation (119).

Available data showed the predictive value of AI radiomics in treatment response in patients with gynecological cancer. However, similar to the research on tumor diagnosis, the published studies are mainly exploratory, with a retrospective nature of study design and limited sample size without extensive external validation, as well as heterogeneous methodology. Besides, most of the studies did not include any potential underlying molecular mechanisms.

Recurrence and survival

Among 27 studies using AI radiomics for prognosis analysis, 15 (55.6%) were on cervical cancer, eight (29.6%) on ovarian cancer, and four (14.8%) on endometrial cancer (Table 6).

Local recurrence and/or distant metastasis

Cervical cancer

A total of 10 studies used AI radiomics to predict local recurrence or distant metastasis in cervical cancer (120-129). Zhou et al. used PET imaging and clinical features to construct a multi-objective SVM learning model for predicting locoregional and distant failure in patients receiving definitive chemoradiation. The clinical-imaging combined model achieved an AUC of 0.84 for locoregional failure and 0.75 for distant failure (121). Nakajo et al. compared six ML methods (RF, KNN, neural network, LR, NB, and SVM) in predicting disease progression following surgery and/or CRT or chemotherapy. The NB model showed the best performance with an AUC of 0.872. The 5-year progression-free survival (PFS) was 80.1% and 9.1% in patients with NB model-predicted non-progression and those with predicted progression, respectively (124). Similarly, Ai et al. used four ML methods [SVM, LR, the least absolute shrinkage and selection operator (LASSO), and extreme gradient boosting (XGboost)] to build MRI radiomics models for recurrence risk prediction. Postoperative adjuvant treatments were integrated in their study to improve the prediction performance in 212 patients who underwent surgery and adjuvant therapy (128). As for the DL approach, Shen et al. performed the first study using a DL model for assessing PET/CT in predicting local relapse and distant metastasis following definitive CRT in 2019. Their study showed the capability of the PET/CT-based DL model in treatment outcome prediction (120). Recently, Wu et al. developed a PET/CT-based DL model for early prediction of distant metastasis. The only PET/CT-based study included more than 100 patients (127).

In addition, MRI-based radiomics data were also used to address the problem of how to predict disease recurrence. Three studies used ML approaches based on MRI and showed potential for predicting disease recurrence (122,123,125). However, the number of patients included was small (<100) without external validation. A recent study by Zhang et al. included 225 patients from two centers to construct and validate a DL radiomics nomogram based on MRI and clinical features for recurrence prediction. The nomogram demonstrated satisfactory predictive performance in the internal and external validation cohorts (AUCs of 0.944 and 0.885, respectively) (129). Finally, Wang et al. investigated the capability of transformer networks based on CT and MRI in recurrence risk stratification (126). The multi-modality transformer network showed promising performance, outperforming that of the four conventional radiomics models (decision tree, NB, KNN, and SVM) and two DL networks (ResNet18 and MobileNetV1) in the testing cohort (0.819 vs. 0.680–0.777 vs. 0.733/0.743).

Endometrial cancer

The only study focused on recurrence risk prediction in endometrial cancer was a multicenter study performed by Lin et al. (130). The fusion model based on clinicopathological and MRI radiomics features showed better performance when compared with the clinicopathological or radiomics features-based models (AUC: 0.87 vs. 0.78 vs. 0.80 in the internal validation cohort; 0.85 vs. 0.75 vs. 0.78 in the external validation cohort). The predictive performance was found to decrease with the expansion of the peritumoral region, although higher AUCs were observed in all fusion models.

Ovarian cancer

Five studies investigated postoperative recurrence or metastasis using CT or MRI-based radiomics in ovarian cancer (131-135). Wang et al. proposed a CT-based DL method to extract prognostic biomarkers for recurrence prediction in patients with HGSOC (131). Yin et al. developed a multitask DL model for predicting peritoneal recurrence and disease-free survival (DFS) in advanced ovarian cancer using preoperative contrast-enhanced CT. The multitask CNN could predict peritoneal and overall recurrence, with AUCs of 0.88 and 0.83 in the internal validation cohort and 0.82 and 0.85 in the external validation cohort (135). Three studies used MRI-based radiomics for recurrence or metastasis prediction. For example, Wei et al. investigated the performance of a T2-weighted MRI radiomics model for predicting peritoneal metastasis using a DL method. An ensemble model combining DL, radiomics, and clinical models was constructed and outperformed their respective ones (AUC: 0.844 vs. 0.737 vs. 0.730 in the external validation set). The performance of the radiologists, especially those less experienced, was significantly improved with the assistance of the model (134).

Survival

Among the 11 studies on survival outcomes, five were conducted on cervical cancer (136-140), three were on endometrial cancer (141-143), and the remaining three were on ovarian cancer (144-146).

Cervical cancer

Two studies reported MRI-based radiomics for survival prediction in cervical cancer. Xin et al. compared six ML methods [LASSO, random survival forest (RSF), gradient boosting machine (GBM), XGboost, linear kernel SVM (SVM-lin), and polynomial kernel SVM (SVM-add)] for predicting DFS and overall survival (OS) after concurrent CRT. The RSF model combining tumor and peritumor radiomics showed the best performance for DFS prediction, whereas the GBM model performed best for OS prediction. The potential mechanisms of radiomics in progression and survival prediction were explored in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and suggested to be associated with Lys-Asp-Glu-Leu receptor 2 (KDELR2) and hexokinase 2 (HK2) (139). Two studies used ML methods based on PET/CT to predict survival. Ferreira et al. showed the added information of PET-CT-based ML models to the clinical data in terms of DFS, yet the data varied across the devices (136). By contrast, Liu et al. demonstrated the nomogram incorporating PET/CT radiomics and clinical features as a useful clinical tool for predicting PFS in LACC patients undergoing concurrent CRT (138). Additionally, Xu et al. established and validated a CT-based hybrid radiomics model to predict OS after concurrent CRT exhibiting favorable performance (AUC: 0.811, 0.713, and 0.730 for 1-, 3-, and 5-year OS in the test set, respectively) (137).

Endometrial cancer

All three studies used ML methods for postoperative progression or survival prediction in patients with endometrial cancer. As an example, Hoivik et al. integrated preoperative MRI with histologic-, transcriptomic-, and molecular biomarkers to develop a novel radiogenomics approach for prognostic profiling of endometrial cancer. An 11-gene high-risk signature was defined and showed prognostic role in orthologous validation cohorts (n=554), aligning with TCGA molecular class with poor survival (copy-number high/p53-altered) (142).

Ovarian cancer

Three studies used CT-based AI radiomics for postoperative survival prediction in ovarian cancer. Lu et al. extracted 657 mathematical descriptors from pretreatment CT images to discover a novel radiomics-based prognostic signature for prognostic and molecular phenotyping. By using ML, a novel non-invasive model based on four descriptors, radiomic prognostic vector (RPV), was obtained and showed reliable performance in identifying patients with a median OS of less than two years. The underlying mechanisms were explored by genetic, transcriptomic, and proteomic analyses using two independent datasets (144). Moreover, Boehm et al. integrated multi-modal data from hematoxylin and eosin staining and contrast-enhanced CT using ML to improve prognosis risk stratification of HGSOC. Histopathological and radiologic features were found to provide complementary prognostic information to one another and clinicogenomic features. By integrating these features, the authors demonstrated a promising path for risk stratification of cancer patients (145).

AI radiomics methods exhibited encouraging results in prognosis prediction of gynecological cancer. However, substantial heterogeneity in methodology and results existed and hampered the clinical application of models. Further validation in multicenter large datasets or randomized trials based on standardized protocol and transparent reporting guidelines is indispensable before translating AI radiomic analysis into clinical utility.

Discussion

Recent years have witnessed the rapid growth of AI radiomics use in gynecological cancer. Numerous studies have demonstrated the potential of AI radiomics in helping clinicians make precision diagnoses, support treatment decision-making, and evaluate the prognosis of patients. Despite the encouraging results reported, there are still many challenges to be solved.

Data scarcity and potential biases

Large high-quality data from across populations are essential for the development of AI-based methodology. However, obtaining such data can be difficult due to high costs, privacy concerns, and data-sharing regulations. Currently available research on AI radiomics in gynecological cancer is mainly exploratory and preliminary based on limited data from single- or few-center retrospective studies. Although external validation was reported in some studies, it was mainly performed on data from one or a few other centers. Although easier to collect, a small dataset may lead to potential biases, a high risk of overfitting, and therefore poor reliability and generalizability of such models. Further validations in large-scale datasets are warranted to evaluate the actual performance of models. This challenge calls for obtaining and sharing robust medical data according to the necessary rules governing medical data privacy [e.g., the Health Insurance Portability and Accountability Act (HIPAA)] (147).

Heterogeneity and reproducibility

Numerous AI radiomics-based models are available for given tasks in gynecological cancer. However, in addition to different data sources, different protocols in research conduction and heterogeneity in results reporting may also affect the reproducibility of results. Some studies still lack clear documentation on validation cohort selection and methodological development. The recently updated CLAIM checklist may offer guidance for improving transparent reporting of AI in medical imaging (148,149). Besides, guidelines such as SPIRIT-AI (150) and CONSORT-AI (151) now include AI-specific recommendations for clinical trial protocols and reporting. As the field evolves, following these guidelines is mandatory for future clinical trials.

Interpretability and explainability

One major hurdle for AI radiomics applications is the lack of trust by physicians and patients in a somewhat “black box” decision-making process. Most studies included in this review did not provide any interpretation or explanation of the models and predictions. Several breakthroughs have been made in visualizing the process and providing insights into underlying decision-making mechanisms. For instance, explainable AI (xAI) is an emerging field that helps physicians to gain mechanistic insights into the complex decision-making process (152). In addition, xAI enables us to enhance biological discovery, for example, revealing underlying molecular mechanisms.

Study limitations

This review has some methodological limitations that may have led to bias. Primarily, due to the considerable heterogeneity among the included studies, we did not compare model performance across studies. However, this is compensated by a narrative synthesis that illustrates common pitfalls and inconsistency of the studies. Some included studies developed multiple models based on multimodal data from different sources separately and together or multiple methods. Therefore, we could only present the summarized performance of a multi-modality fusion model or the one that had the best performance in the tables. In addition, some relevant studies published after the time frame of publication search were not included in the review.

Conclusions

In this review, we confirmed an increasing application of AI radiomics in the management of gynecological cancer. AI radiomics has shown potential value in assisting clinicians with tumor diagnosis and treatment decision-making. Despite the significant advances, however, the development of AI radiomics is still hindered by retrospective study design using data mainly from a single center, diverse methodologies, and opaque decision-making. More compelling evidence is required to develop a reliable and interpretable AI-radiomics tool for managing gynecological cancer.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-618/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-618/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-618/coif). S.H reports that he is an employee of Jiangsu Hengrui Pharmaceuticals Co., Ltd. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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(English Language Editor: J. Jones)