Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients

Abstract

BackgroundRadiation proctitis is a common complication after radiotherapy for cervical cancer. Unlike simple radiation damage to other organs, radiation proctitis is a complex disease closely related to the microbiota. However, analysis of the gut microbiota is time-consuming and expensive. This study aims to mine rectal information using radiomics and incorporate it into a nomogram model for cheap and fast prediction of severe radiation proctitis prediction in postoperative cervical cancer patients.MethodsThe severity of the patient’s radiation proctitis was graded according to the RTOG/EORTC criteria. The toxicity grade of radiation proctitis over or equal to grade 2 was set as the model’s target. A total of 178 patients with cervical cancer were divided into a training set (n = 124) and a validation set (n = 54). Multivariate logistic regression was used to build the radiomic and non-raidomic models.ResultsThe radiomics model [AUC=0.6855(0.5174-0.8535)] showed better performance and more net benefit in the validation set than the non-radiomic model [AUC=0.6641(0.4904-0.8378)]. In particular, we applied SHapley Additive exPlanation (SHAP) method for the first time to a radiomics-based logistic regression model to further interpret the radiomic features from case-based and feature-based perspectives. The integrated radiomic model enables the first accurate quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients, addressing the limitations of the current qualitative assessment of the plan through dose-volume parameters only.ConclusionWe successfully developed and validated an integrated radiomic model containing rectal information. SHAP analysis of the model suggests that radiomic features have a supporting role in the quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients.

Unravelling Quality of Life for Head and Neck Cancer Patients after VMAT Radiation Therapy: Insights from Toxicity, Dosimetry and Symptoms Correlation

“…The detailed grading system facilitates precise evaluation and management of patient outcomes, allowing for tailored interventions and optimized treatment protocols. Furthermore, the EORTC-RTOG criteria have been validated in numerous studies, demonstrating their reliability and robustness in assessing radiation-induced toxicities [ 31 ]. The statistically significant correlation that was observed reinforces the validity of utilizing the EORTC_RTOG criteria in clinical practice for evaluating treatment-related toxicity, facilitating accurate monitoring and management of adverse effects in head and neck cancer patients undergoing radiation therapy.…”

Hepatic encephalopathy post-TIPS: Current status and prospects in predictive assessment

“…These scoring frameworks incorporate the analysis of biochemical parameters, imaging studies, and the integration of advanced computational strategies. Currently, AI algorithms play a pivotal role in various fields, including biomics and healthcare data analysis [140] , [141] , [142] , [143] , [144] , [145] , [146] , [147] , [148] . The fusion of AI techniques with biochemical and radiological data is increasingly recognized for its potential to improve the accuracy of diagnostic and risk stratification processes in the context of HE.…”

Radiomics in Gynaecological Imaging: A State-of-the-Art Review

“…Another interesting and widely studied field of radiomics research in patients with CC is the assessment of response to treatments including conization [98], radiotherapy, chemotherapy, and chemo-radiation therapy [99][100][101][102][103][104][105][106][107][108][109], the evaluation of recurrence [110][111][112][113], and toxicity related to radiotherapy [114][115][116]. Among all studies on the assessment of response to treatments, there are four multicentric studies and one prospective study that deserve attention (Table 2).…”

“…Finally, the multicentre retrospective study by Gui et al [104] analysed 183 patients from two institutions and created a radiomics model to predict pathological complete response after neoadjuvant chemoradiotherapy, yielding promising results (the RF_DEF model showed a mean AUC of 0.80). It is also interesting to mention that some studies have investigated the role of radiomics in predicting malnutrition after intervention [117], as well as haematological toxicities, changes in the pelvic bone marrow, and proctitis related to radiotherapy [114][115][116].…”