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Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer

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Department of Radiology, Rajiv Gandhi Cancer Institute and Research Centre, New Delhi 110085, India
2
Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi 110020, India
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Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
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Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Department of Radiation Oncology, Rajiv Gandhi Cancer Institute and Research Centre, New Delhi 110085, India
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Department of Laboratory & Histopathology, Rajiv Gandhi Cancer Institute, New Delhi 110085, India
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Department of Laboratory & Transfusion Services and Director Research, Rajiv Gandhi Cancer Institute, New Delhi 110085, India
*
Author to whom correspondence should be addressed.
Academic Editor: Michael Garwood
Tomography 2021, 7(3), 344-357; https://doi.org/10.3390/tomography7030031
Received: 18 June 2021 / Accepted: 2 August 2021 / Published: 5 August 2021
Objectives: To 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 Methods: Retrospective 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. Results: The 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. Conclusions: The study is an effort to bridge the unmet need of translational predictive biomarkers in the stratification of uterine cervical cancer patients based on prognosis. View Full-Text
Keywords: radiomics; diffusion-weighted; MRI; cervical cancer radiomics; diffusion-weighted; MRI; cervical cancer
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MDPI and ACS Style

Jajodia, A.; Gupta, A.; Prosch, H.; Mayerhoefer, M.; Mitra, S.; Pasricha, S.; Mehta, A.; Puri, S.; Chaturvedi, A. Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer. Tomography 2021, 7, 344-357. https://doi.org/10.3390/tomography7030031

AMA Style

Jajodia A, Gupta A, Prosch H, Mayerhoefer M, Mitra S, Pasricha S, Mehta A, Puri S, Chaturvedi A. Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer. Tomography. 2021; 7(3):344-357. https://doi.org/10.3390/tomography7030031

Chicago/Turabian Style

Jajodia, Ankush, Ayushi Gupta, Helmut Prosch, Marius Mayerhoefer, Swarupa Mitra, Sunil Pasricha, Anurag Mehta, Sunil Puri, and Arvind Chaturvedi. 2021. "Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer" Tomography 7, no. 3: 344-357. https://doi.org/10.3390/tomography7030031

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