Development and validation of a radiomics-based nomogram for the prediction of postoperative malnutrition in stage IB1-IIA2 cervical carcinoma

Abstract

ObjectiveIn individuals with stage IB1-IIA2 cervical cancer (CC) who received postoperative radiotherapy ± chemotherapy (PORT/CRT), the interaction between sarcopenia and malnutrition remains elusive, let alone employing a nomogram model based on radiomic features of psoas extracted at the level of the third lumbar vertebra (L3). This study was set to develop a radiomics-based nomogram model to predict malnutrition as per the Patient-Generated Subjective Global Assessment (PG-SGA) for individuals with CC.MethodsIn total, 120 individuals with CC underwent computed tomography (CT) scans before PORT/CRT. The radiomic features of psoas at L3 were obtained from non-enhanced CT images. Identification of the optimal features and construction of the rad-score formula were conducted utilizing the least absolute shrinkage and selection operator (LASSO) logistic regression to predict malnutrition in the training dataset (radiomic model). Identification of the major clinical factors in the clinical model was performed by means of binary logistic regression analysis. The radiomics-based nomogram was further developed by integrating radiomic signatures and clinical risk factors (combined model). The receiver operating characteristic (ROC) curves and decision curves analysis (DCA) were employed for the evaluation and comparison of the three models in terms of their predictive performance.ResultsTwelve radiomic features in total were chosen, and the rad-score was determined with the help of the non-zero coefficient from LASSO regression. Multivariate analysis revealed that besides rad-score, age and Eastern Cooperative Oncology Group performance status could independently predict malnutrition. As per the data of this analysis, a nomogram prediction model was constructed. The area under the ROC curves (AUC) values of the radiomic and clinical models were 0.778 and 0.847 for the training and 0.776 and 0.776 for the validation sets, respectively. An increase in the AUC was observed up to 0.972 and 0.805 in the training and validation sets, respectively, in the combined model. DCA also confirmed the clinical benefit of the combined model.ConclusionThis radiomics-based nomogram model depicted potential for use as a marker for predicting malnutrition in stage IB1-IIA2 CC patients who underwent PORT/CRT and required further investigation with a large sample size.

Development of a nomogram for predicting malnutrition in elderly hospitalized cancer patients: a cross-sectional study in China

“…This is crucial for clinicians to rapidly and accurately assess the risk of malnutrition in elderly cancer patients in a complex medical environment. Yu et al ( 9 ) collected the X-ray computed tomography (CT) scanning data of 120 patients with cervical cancer before receiving radiotherapy and chemotherapy. By analyzing the non-enhanced CT images, they obtained the radiological characteristics of the L3 psoas major muscle.…”

“…However, this model focused solely on a single type of cancer and lacked a specific nutritional risk screening tool for elderly cancer patients. Yu et al ( 9 ) collected X-ray computed tomography (CT) scans from 120 cervical cancer patients prior to radiotherapy and chemotherapy. They analyzed non-enhanced CT images to determine the radiological characteristics of the L3 psoas major muscle, using LASSO regression for predicting malnutrition in the dataset, though the data were challenging to collect and had limited clinical utility.…”

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

“…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].…”