Recurrence risk stratification for locally advanced cervical cancer using multi-modality transformer network

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

Background and Objective

Gynecological cancer is the most common cancer that affects women's quality of life and well-being. Artificial intelligence (AI) technology enables us to exploit high-dimensional imaging data for precision oncology. Tremendous progress has been made with AI radiomics in cancers such as lung and breast cancers. Herein, we performed a literature review on AI radiomics in the management of gynecological cancer.

Methods

A search was performed in the databases of PubMed, Embase, and Web of Science for original articles written in English up to 10 September 2024, using the terms "gynecological cancer", "cervical cancer", "endometrial cancer", "ovarian cancer", AND "artificial intelligence", "AI", AND "radiomics". The included studies mainly focused on the current landscape of AI radiomics in the diagnosis, treatment, and prognosis of gynecological cancer.

Key Content and Findings

A total of 128 studies were included, with 86 studies focusing on tumor diagnosis (n=23) and characterization (n=63), 15 on treatment response prediction, and 27 on recurrence and survival prediction. AI radiomics has shown potential value in tumor diagnosis and characterization [tumor staging, histological subtyping, lymph node metastasis (LNM), lymphovascular space invasion (LVSI), myometrial invasion (MI), and other molecular or clinicopathological factors], chemotherapy or chemoradiotherapy response evaluation, and prognosis (disease recurrence or metastasis, and survival) prediction. However, most included studies were single-center and retrospective. There was substantial heterogeneity in methodology and results reporting.

Conclusions

AI radiomics has been increasingly adopted in the management of gynecological cancer. Further validation in large-scale datasets is needed before clinical translation.

Pre-treatment T2-weighted magnetic resonance radiomics for prediction of loco-regional recurrence after image-guided adaptive brachytherapy for locally advanced cervical cancer

Purpose

The aim of this study was to investigate the predictive value of radiomic features of pre-treatment T2-weighted magnetic resonance images (MRI) for clinical outcomes of radiotherapy in cervical cancer patients.

Material and methods

Ninety cervical cancer patients with stage IB-IVA were retrospectively analyzed. All patients received definitive radiotherapy with or without concurrent chemotherapy. Radiomic features were extracted from gross tumor volume (GTV) on pre-treatment T2-weighted MRI. The association between radiomic features and loco-regional recurrence (LRR) was analyzed with Student's t test, and false discovery rate was controlled using Storey method. Multivariate analysis with significant radiomic features with p-value < 0.01 and known clinical prognostic factors was performed using Cox proportional hazard model.

Results

The majority of patients were stage IIIB (47.8%) and stage IIB (36.7%), and the most common histology was squamous cell carcinoma (74.5%). The median GTV volume was 37.5 ml (IQR, 16.3-93.1). The median dose of D90 received by high-risk clinical target volume (HR-CTV) was 86.2 Gy (IQR, 67.2-94.2). In a median follow-up time of 29.2 months, 12 of the 90 patients (13.3%) developed LRR. Eighty radiomic features were collected. There were four radiomic features, which showed significant correlation with LRR: Maximum intensity (p = 0.0002), Correlation135 GLCM (p = 0.0014), Correlation90 (p = 0.0015), and Correlation45 (p = 0.0034). Cox regression analysis yielded a significant hazard ratio for the maximum intensity (p = 0.038) and Correlation135 GLCM (p = 0.013) features. There was no statistically significant association for overall survival with any radiomic features.

Conclusions

The maximum intensity and Correlation135 GLCM radiomic features of the pre-treatment T2-weighted MR images are predictive of loco-regional recurrence in cervical cancer patients after definitive radiotherapy with 3D-IGABT.

A New Automated Prognostic Prediction Method Based on Multi-Sequence Magnetic Resonance Imaging for Hepatic Resection of Colorectal Cancer Liver Metastases

Colorectal cancer is a prevalent and life-threatening disease, where colorectal cancer liver metastasis (CRLM) exhibits the highest mortality rate. Currently, surgery stands as the most effective curative option for eligible patients. However, due to the insufficient performance of traditional methods and the lack of multi-modality MRI feature complementarity in existing deep learning methods, the prognosis of CRLM surgical resection has not been fully explored. This paper proposes a new method, multi-modal guided complementary network (MGCNet), which employs multi-sequence MRI to predict 1-year recurrence and recurrence-free survival in patients after CRLM resection. In light of the complexity and redundancy of features in the liver region, we designed the multi-modal guided local feature fusion module to utilize the tumor features to guide the dynamic fusion of prognostically relevant local features within the liver. On the other hand, to solve the loss of spatial information during multi-sequence MRI fusion, the cross-modal complementary external attention module designed an external mask branch to establish inter-layer correlation. The results show that the model has accuracy (ACC) of 0.79, the area under the curve (AUC) of 0.84, C-Index of 0.73, and hazard ratio (HR) of 4.0, which is a significant improvement over state-of-the-art methods. Additionally, MGCNet exhibits good interpretability.

Sarcopenic Obesity in Cervical Carcinoma: A Strong and Independent Prognostic Factor beyond the Conventional Predictors (ESTHER Study-AFRAID Project)

Locally advanced cervical cancer represents a significant treatment challenge. Body composition parameters such as body mass index, sarcopenia, and sarcopenic obesity, defined by sarcopenia and BMI ≥ 30 kg/m2, have been identified as potential prognostic factors, yet their overall impact remains underexplored. This study assessed the relationship between these anthropometric parameters alongside clinical prognostic factors on the prognosis of 173 cervical cancer patients. Survival outcomes in terms of local control (LC), distant metastasis-free survival (DMFS), disease-free survival (DFS), and overall survival (OS) were analyzed using Kaplan regression methods-Meier and Cox. Older age, lower hemoglobin levels, higher FIGO (International Federation of Gynecology and Obstetrics) stages, and lower total radiation doses were significantly associated with worse outcomes. Univariate analysis showed a significant correlation between BMI and the outcomes examined, revealing that normal-weight patients show higher survival rates, which was not confirmed by the multivariate analysis. Sarcopenia was not correlated with any of the outcomes considered, while sarcopenic obesity was identified as an independent negative predictor of DFS (HR: 5.289, 95% CI: 1.298-21.546, p = 0.020) and OS (HR: 2.645, 95% CI: 1.275-5.488, p = 0.009). This study highlights the potential of sarcopenic obesity as an independent predictor of clinical outcomes. These results support their inclusion in prognostic assessments and treatment planning for patients with advanced cervical cancer.

Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT

Gynecological malignancies, particularly lymph node metastasis, have presented a diagnostic challenge, even with traditional imaging techniques such as CT, MRI, and PET/CT. This study was conceived to explore and, subsequently, to bridge this diagnostic gap through a more holistic and innovative approach. By developing a comprehensive framework that integrates both non-image data and detailed MRI image analyses, this study harnessed the capabilities of a multimodal federated-learning model. Employing a composite neural network within a federated-learning environment, this study adeptly merged diverse data sources to enhance prediction accuracy. This was further complemented by a sophisticated deep convolutional neural network with an enhanced U-NET architecture for meticulous MRI image processing. Traditional imaging yielded sensitivities ranging from 32.63% to 57.69%. In contrast, the federated-learning model, without incorporating image data, achieved an impressive sensitivity of approximately 0.9231, which soared to 0.9412 with the integration of MRI data. Such advancements underscore the significant potential of this approach, suggesting that federated learning, especially when combined with MRI assessment data, can revolutionize lymph-node-metastasis detection in gynecological malignancies. This paves the way for more precise patient care, potentially transforming the current diagnostic paradigm and resulting in improved patient outcomes.