Contribution of Tata Memorial Centre, India, to cervical cancer care

PURPOSE This survey was conducted to assess the current research practices among the 14 members of the Federation of Asian Organizations for Radiation Oncology (FARO) committee, to inform measures for research capacity building in these nations. MATERIALS AND METHODS A 19-item electronic survey was sent to two research committee members from the 14 representative national radiation oncology organizations (N = 28) that are a part of FARO. RESULTS Thirteen of the 14 member organizations (93%) and 20 of 28 members (71.5%) responded to the questionnaire. Only 50% of the members stated that an active research environment existed in their country. Retrospective audits (80%) and observational studies (75%) were the most common type of research conducted in these centers. Lack of time (80%), lack of funding (75%), and limited training in research methodology (40%) were cited as the most common hindrances in conducting research. To promote research initiatives in the collaborative setting, 95% of the members agreed to the creation of site-specific groups, with head and neck (45%) and gynecological cancers (25%) being the most preferred disease sites. Projects focused on advanced external beam radiotherapy implementation (40%), and cost-effectiveness studies (35%) were cited as some of the potential areas for future collaboration. On the basis of the survey results, after result discussion and the FARO officers meeting, an action plan for the research committee has been created. CONCLUSION The results from the survey and the initial policy structure may allow facilitation of radiation oncology research in the collaborative setting. Centralization of research activities, funding support, and research-directed training are underway to help foster a successful research environment in the FARO region.

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An MRI-Based Deep Learning Radiomics Nomogram Analysis for Prediction of Postoperative Lymph Node Metastasis in Patients with Locally Advanced Cervical Cancer Receiving Neoadjuvant Therapy

Wu,

Xie,

Zhou

2024

Preprint

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Objective The aim of this study was to investigate the value of combining radiomics features and deep learning features to construct Nomogram for non-invasive prediction of postoperative lymph node metastasis (LNM) by magnetic resonance imaging (MRI) prior to neoadjuvant chemotherapy (NACT) for cervical cancer, to assist clinical decision-making and diagnosis. Method Two hundred and sixty-five cervical cancer patients were divided into training (n = 212) and test (n = 53) sets in an 8:2 ratio. Radiomics features were extracted from Axial Liver Acquisition with Volume Acceleration plus Contrast enhancement (Ax-LAVA + C) sequences of MRI, and deep learning (DL) features were extracted using the Inception V3 model. The features selected by LASSO were combined with four machine learning algorithms to construct models to evaluate the predictive value of the radiomics features and DL features for postoperative LNM in cervical cancer patients operated after NACT. A Nomogram was constructed based on Logistic Regression model combining radiomics score (rad-score) and DL-score. Results In the radiomics model, the Multilayer Perceptron (MLP) outperforms other machine learning algorithms, with AUCs of 0.79,0.80 in the training set and test set, respectively. In the DL model, Support Vector Machine (SVM) outperforms other machine learning algorithms with AUCs of 0.78,0.78 in the training set and test set, respectively. The Nomogram constructed by combining radiomics features and deep learning features has an AUC of 0.93,0.89 in the training set and test set, respectively. It outperforms the radiomics model and the DL model. The decision curve analysis (DCA) shows that the Nomogram has good clinical benefits. Conclusion Radiomics models and deep learning models can effectively predict LNM status after NACT in cervical cancer patients. The Nomogram constructed by combining radiomics and DL features has better prediction performance compared with the radiomics model and DL model.

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Contribution of Tata Memorial Centre, India, to cervical cancer care

Cited by 4 publications

References 68 publications

Gynaecological Oncology in India: Past, Present and Future

Gynaecological Oncology in India: Past, Present and Future

Radiation Oncology Research in Asia: Current Status and a Peep Into the Future From the Federation of Asian Organizations for Radiation Oncology

An MRI-Based Deep Learning Radiomics Nomogram Analysis for Prediction of Postoperative Lymph Node Metastasis in Patients with Locally Advanced Cervical Cancer Receiving Neoadjuvant Therapy

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