Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

Abstract

To develop and validate a radiomics model for evaluating pathologic complete response (pCR) to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer (LARC). We enrolled 222 patients (152 in the primary cohort and 70 in the validation cohort) with clinicopathologically confirmed LARC who received chemoradiotherapy before surgery. All patients underwent T2-weighted and diffusion-weighted imaging before and after chemoradiotherapy; 2,252 radiomic features were extracted from each patient before and after treatment imaging. The two-sample test and the least absolute shrinkage and selection operator regression were used for feature selection, whereupon a radiomics signature was built with support vector machines. Multivariable logistic regression analysis was then used to develop a radiomics model incorporating the radiomics signature and independent clinicopathologic risk factors. The performance of the radiomics model was assessed by its calibration, discrimination, and clinical usefulness with independent validation. The radiomics signature comprised 30 selected features and showed good discrimination performance in both the primary and validation cohorts. The individualized radiomics model, which incorporated the radiomics signature and tumor length, also showed good discrimination, with an area under the receiver operating characteristic curve of 0.9756 (95% confidence interval, 0.9185-0.9711) in the validation cohort, and good calibration. Decision curve analysis confirmed the clinical utility of the radiomics model. Using pre- and posttreatment MRI data, we developed a radiomics model with excellent performance for individualized, noninvasive prediction of pCR. This model may be used to identify LARC patients who can omit surgery after chemoradiotherapy. .

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach

“…Radiomics is a procedure of extracting high‐throughput quantitative image features from standard‐of‐care medical images and applying the useful features within clinical decision‐support systems . Currently, radiomics is mainly used in oncology to facilitate improved clinical decision making . Indeed, radiomics can also be applied in patients with BCa, such as predicting lymph node metastasis and treatment response .…”

“…17 Currently, radiomics is mainly used in oncology to facilitate improved clinical decision making. [18][19][20] Indeed, radiomics can also be applied in patients with BCa, such as predicting lymph node metastasis and treatment response. [21][22][23][24] In addition, 2 MRI-based radiomics signatures to differentiate MIBC from NMIBC have been developed.…”

MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy

“…Radiomics analysis involves computer-based extraction of a large number of quantitative features and has potential for aiding clinical decision making (22). Prior studies have evaluated the use of radiomics analysis in MR imaging for distinguishing cancer from benign tissue or adding information about cancer aggressiveness (21,(23)(24)(25)(26)(27)(28)(29)(30), as well as for predicting response after CRT (31)(32)(33). We hypothesize that MR imaging-based radiomics may add value to the current MR imaging assessment for evaluating patients with locally advanced rectal cancer after CRT, improving on qualitative assessment to differentiate patients with clinical partial response from those with cCR after CRT.…”

Radiomics Analysis of DTI Data to Assess Vision Outcome After Intravenous Methylprednisolone Therapy in Neuromyelitis Optic Neuritis

“…Previous studies have demonstrated that radiomics analysis achieved outstanding performance in evaluating treatment response, estimating prognosis, and aiding clinical diagnosis . Liu et al employed radiomics analysis of MRI data to evaluate the pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer and obtained satisfying performance …”

“…24,25 Liu et al employed radiomics analysis of MRI data to evaluate the pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer and obtained satisfying performance. 26 In the current study, we aimed to assess the visual recovery of NMO-ON after IVMP treatment by radiomics analysis of DTI data. Furthermore, it was expected that the results might provide insight into the white matter impairments related to visual recovery in NMO-ON.…”