An MRI-based radiomics nomogram for detecting cervical esophagus invasion in hypopharyngeal squamous cell carcinoma

Abstract

Background: Accurate detection of cervical esophagus invasion (CEI) in HPSCC is challenging but crucial. We aimed to investigate the value of magnetic resonance imaging (MRI)-based radiomics for detecting CEI in patients with HPSCC.

Methods: This retrospective study included 151 HPSCC patients with or without CEI, which were randomly assigned into a training (n = 101) or validation (n = 50) cohort. A total of 750 radiomics features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (ceT1WI), respectively. A radiomics signature was constructed using the least absolute shrinkage and selection operator method. Multivariable logistic regression analyses were adopted to establish a clinical model and a radiomics nomogram. Two experienced radiologists evaluated the CEI status based on morphological findings. Areas under the curve (AUCs) of the models and readers were compared using the DeLong method. The performance of the nomogram was also assessed by its calibration and clinical usefulness.

Results: The radiomics signature, consisting of five T2WI and six ceT1WI radiomics features, was significantly associated with CEI in both cohorts (all p < 0.001). The radiomics nomogram combining the radiomics signature and clinical T stage achieved significantly higher predictive value than the clinical model and pooled readers in the training (AUC 0.923 vs. 0.723 and 0.621, all p < 0.001) and validation (AUC 0.888 vs. 0.754 and 0.647, all p < 0.05) cohorts. The radiomics nomogram showed favorable calibration in both cohorts and provided better net benefit than the clinical model.

Conclusions: The MRI-based radiomics nomogram is a promising method for detecting CEI in HPSCC.

Keywords: Cervical esophagus invasion; Hypopharyngeal squamous cell carcinoma; Magnetic resonance imaging; Radiomics.

Fig. 1

Flowchart of the patient selection process. HPSCC, hypopharyngeal squamous cell carcinoma; PACS, picture archiving and communication system; CEI, cervical esophagus invasion

Fig. 2

Tumor segmentation of hypopharyngeal squamous cell carcinoma. The segmentation was performed on T2-weighted imaging (a) and contrast-enhanced T1-weighted imaging (b). By drawing regions of interest slice by slice, a region of interest covering the entire tumor (c) was acquired

Fig. 3

Radiomic feature selection by least absolute shrinkage and selection operator (LASSO) logistic regression. (a) Selection of tuning parameter (λ) in the LASSO model using 10-fold cross-validation. Dotted vertical lines were drawn at the optimal values by using the minimum criteria and 1 standard error of the minimum criteria (the 1-standard error criteria). The optimal log (λ) of -2.53 was chosen. (b) LASSO coefficient profiles of the 31 features. A vertical line was plotted at the optimal log (λ), which resulted in 11 features with non-zero coefficients

Fig. 4

Radiomics nomogram developed with calibration curves. (a) A radiomics nomogram was developed in the training cohort, with radiomics signature and clinical T stage incorporated. Calibration curves of the radiomics nomogram in the training (b) and validation (c) cohorts

Fig. 5

Receiver operating characteristic curves of the radiomics nomogram, clinical model, and pooled readers in the training (a) and validation (b) cohorts

Fig. 6

Decision curve analysis for the clinical model and radiomics nomogram. The y-axis indicates the net benefit and the x-axis indicates threshold probability. The radiomics nomogram had a higher overall net benefit in detecting cervical esophagus invasion (CEI) compared with the clinical model and simple diagnoses such as all patients with or without CEI across the majority of the range of reasonable threshold probabilities in the validation cohort