Application of contrast-enhanced CT radiomics in prediction of early recurrence of locally advanced oesophageal squamous cell carcinoma after trimodal therapy

Abstract

Background Early recurrence of oesophageal squamous cell carcinoma (SCC) is defined as recurrence after surgery within 1 year, and appears as local recurrence, distant recurrence, and lymph node positive and disseminated recurrence. Contrast-enhanced computed tomography (CECT) is recommended for diagnosis of primary tumor and initial staging of oesophageal SCC, but it cannot be used to predict early recurrence. It is reported that radiomics can help predict preoperative stages of oesophageal SCC, lymph node metastasis before operation, and 3-year overall survival of oesophageal SCC patients following chemoradiotherapy by extracting high-throughput quantitative features from CT images. This study aimed to develop models based on CT radiomics and clinical features of oesophageal SCC to predict early recurrence of locally advanced cancer. Methods We collected electronic medical records and image data of 197 patients with confirmed locally advanced oesophageal SCC. These patients were randomly allocated to 137 patients in the training cohort and 60 in the test cohort. 352 radiomics features were extracted by delineating region-of-interest (ROI) around the lesion on CECT images and clinical signature was generated by medical records. The radiomics model, clinical model, the combined model of radiomics and clinical features were developed by radiomics features and/or clinical characteristics. Predicting performance of the three models was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1 score. Results Eleven radiomics features and/or six clinical signatures were selected to build prediction models related to recurrence of locally advanced oesophageal SCC after trimodal therapy. The AUC of integration of radiomics and clinical models was better than that of radiomics or clinical model for the training cohort (0.821 versus 0.754 or 0.679, respectively) and for the validation cohort (0.809 versus 0.646 or 0.658, respectively). Integrated model of radiomics and clinical features showed good performance in predicting early recurrence of locally advanced oesophageal SCC for both the training and validation cohorts (accuracy = 0.730 and 0.733, and F-1score = 0.730 and 0.778, respectively). Conclusions The integrated model of CECT radiomics and clinical features may be a potential imaging biomarker to predict early recurrence of locally advanced oesophageal SCC after trimodal therapy.

A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography Images

“…Previous studies have shown that radiomics in computed tomography (CT) imaging can improve the prediction of prognosis of HNSCC . Radiomics on CT HNSCC imaging has been used to predict HPV status [8,9], overall survival [10][11][12][13][14], progression-free survival [10,[12][13][14], local tumour control [8,12,[15][16][17][18][19][20][21], tumour grade [9,22], lymph node response [23,24], tumour invasiveness [9,25], xerostomia [26][27][28], tumour resectability [29], and classifying molecular subtypes [30,31]. While the survival studies show that radiomics on CT data can significantly stratify patients in multiple survival groups, performance expressed through Harrell's C index ranged widely, from 0.58 to 0.9.…”

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

“…Radiomics has also made progress in predicting the recurrence and prognosis of EC patients[ 44 - 47 ]. Tang et al [ 48 ] predicted the early recurrence of locally advanced ESCC after trimodal therapy based on enhanced CT radiomics. The results showed that in the training cohort, the AUCs of the radiomics model, the clinical model, and the combined model were 0.754, 0.679, and 0.821, respectively, and they were 0.646, 0.658, and 0.809 in the validation cohort; the combined model was the best.…”

A practical nomogram included hyperlipidemia for predicting lymph node metastasis in patients with superficial esophageal squamous cell carcinoma

“…Based on existing preoperative examination methods, such as computed tomography, magnetic resonance imaging, and endoscopic ultrasonography, it is still difficult to accurately detect the presence of LNM. [12][13][14] In addition, the clinicopathological risk factors associated with LNM in SESC remain unclear. Therefore, this study aimed to determine the risk factors for LNM in patients with SESCC and establish a practical nomogram based on these factors to guide the treatment of patients with SESCC.…”