MRI radiomics help predict cervical cancer treatment outcomes

"Handcrafted" radiomics and deep-learning radiomics based on pretreatment MRI data help predict locally advanced cervical cancer (LACC) concurrent chemoradiotherapy treatment outcomes, according to research published January 12 in Scientific Reports. The study results show promise for tailoring care for patients with this particular cancer, wrote a team led by Sungmoon Jeong, PhD, of Kyungpook National University, Daegu, South Korea. "Our findings may contribute to the development of personalized treatment strategies for locally advanced cervical cancer patients," the group noted. Cervical cancer is the fourth most common cancer and the fourth leading cause of cancer-related deaths in women around the world, the investigators wrote. Concurrent chemoradiotherapy (CRT) is the standard treatment for locally advanced cervical cancer, and consists of external beam radiotherapy and intracavitary brachytherapy with concurrent chemotherapy. But a "reliable tool for predicting CRT responses" is needed, they explained. Jeong and colleagues conducted a study that included 252 locally advanced cervical cancer patients who underwent chemoradiotherapy between 2006 and 2019, dividing the group into training (167 patients) and test (85 patients) sets for a radiomics algorithm based on data from T1- and T2-weighted MR scans. The "handcrafted" radiomics model included 1,890 imaging features, while the deep-learning radiomics model consisted of a 3D convolutional neural network. Jeong and ...
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