Browsing by Author "Ritika Ranjan"

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Prediction of response to radiotherapy in locally advanced carcinoma cervix using multiparametric MRI: A prospective, single-center, longitudinal study
(John Wiley and Sons Inc, 2023) Ashish Verma; Ishan Kumar; Deepak Singh Patel; Ritika Ranjan; Munendra Singh; Satyajeet Pradhan; Ram Chandra Shukla
Purpose: To evaluate the possible role of a multiparametric magnetic resonance imaging (MRI) and semiquantitative fusion map for the prediction of response to radiotherapy in carcinoma cervix. Methods: This was a prospective, single-center, longitudinal observational study performed on patients with locally advanced carcinoma cervix. Relative blood flow (rBF), relative blood volume (rBV), and apparent diffusion coefficient (ADC) values were obtained before and after the onset of radiotherapy. rBV, rBF, and ADC values were used to generate a semiquantitative pharmacokinetic model map to identify any hypoxic component of the tumor. The subjects were retrospectively classified as responders and nonresponders based on response to treatment. Prospective prediction of response status was done using pretreatment multiparametric MRI parameters (rBF, rBV, and ADC) and semiquantitative parametric map. Results: In 32 patients (29 with parametrial involvement and 15 with lymphadenopathy), pretreatment ADC of the primary tumor was the most accurate measure for predicting response to treatment as well as for treatment-induced fractional volume reduction. Although rBV and rBF were insignificant metrics in isolation for predicting response status, a combination with ADC in the form of parametric map had a sensitivity of 86.4% and 77.2%, specificity of 70% and 70%, positive predictive value of 86.4% and 85%, and negative predictive value 70% and 59% respectively by two independent observers. Conclusion: ADC is the most accurate measure for predicting the response to treatment. A manual parametric map generated by an off-line fusion of the above map with those generated by pharmacokinetic modeling of perfusion-weighted MRI may be a useful tool for the prediction of response to radiotherapy. © 2022 John Wiley & Sons Australia, Ltd.