ArticleTomography (Ann Arbor, Mich.)2021
Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer.
Article in Tomography (Ann Arbor, Mich.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 27 citations in OpenAlex.
- Tumor location, genomic alterations, and radiomic features as predictors of survival in glioblastoma: a Multi-Modal analysis.Neuroradiology · 2025Article
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- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- Development, validation, and visualization of a novel nomogram for predicting clinical outcomes of radiotherapy combined with chemotherapy in locally advanced cervical cancer.Frontiers in oncology · 2025Article
- Research progress of artificial intelligence in the early screening, diagnosis, precise treatment and prognosis prediction of three central gynecological malignancies.Frontiers in oncology · 2025Review
- Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.Tomography (Ann Arbor, Mich.) · 2024Review
- MRI Radiomics Data Analysis for Differentiation between Malignant Mixed Müllerian Tumors and Endometrial Carcinoma.Cancers · 2024Article
- Pre-treatment T2-weighted magnetic resonance radiomics for prediction of loco-regional recurrence after image-guided adaptive brachytherapy for locally advanced cervical cancer.Journal of contemporary brachytherapy · 2024Article
- Development and validation of a radiomics model based on T2-weighted imaging for predicting the efficacy of high intensity focused ultrasound ablation in uterine fibroids.Quantitative imaging in medicine and surgery · 2024Article
- Automated Prediction of Neoadjuvant Chemoradiotherapy Response in Locally Advanced Cervical Cancer Using Hybrid Model-Based MRI Radiomics.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.Frontiers in oncology · 2022Article
- Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques.Computational and mathematical methods in medicine · 2022Article
- Stability of Liver Radiomics across Different 3D ROI Sizes-An MRI In Vivo Study.Tomography (Ann Arbor, Mich.) · 2021Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
Abstract
objectivesTo explore the potential of Radiomics alone and in combination with a diffusion-weighted derived quantitative parameter, namely the apparent diffusion co-efficient (ADC), using supervised classification algorithms in the prediction of outcomes and prognosis. MATERIALS AND
methodsRetrospective evaluation of the imaging was conducted for a study cohort of uterine cervical cancer, candidates for radical treatment with chemo radiation. ADC values were calculated from the darkest part of the tumor, both before (labeled preADC) and post treatment (labeled postADC) with chemo radiation. Post extraction of 851 Radiomics features and feature selection analysis-by taking the union of the features that had Pearson correlation >0.35 for recurrence, >0.49 for lymph node and >0.40 for metastasis-was performed to predict clinical outcomes.
resultsThe study enrolled 52 patients who presented with variable FIGO stages in the age range of 28-79 (Median = 53 years) with a median follow-up of 26.5 months (range: 7-76 months). Disease recurrence occurred in 12 patients (23%). Metastasis occurred in 15 patients (28%). A model generated with 24 radiomics features and preADC using a monotone multi-layer perceptron neural network to predict the recurrence yields an AUC of 0.80 and a Kappa value of 0.55 and shows that the addition of radiomics features to ADC values improves the statistical metrics by approximately 40% for AUC and approximately 223% for Kappa. Similarly, the neural network model for prediction of metastasis returns an AUC value of 0.84 and a Kappa value of 0.65, thus exceeding performance expectations by approximately 25% for AUC and approximately 140% for Kappa. There was a significant input of GLSZM features (SALGLE and LGLZE) and GLDM features (SDLGLE and DE) in correlation with clinical outcomes of recurrence and metastasis.
conclusionsThe study is an effort to bridge the unmet need of translational predictive biomarkers in the stratification of uterine cervical cancer patients based on prognosis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.