Enhanced CT-based radiomics predicts pathological complete response after neoadjuvant chemotherapy for advanced adenocarcinoma of the esophagogastric junction: a two-center study

Abstract

Purpose This study aimed to develop and validate CT-based models to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for advanced adenocarcinoma of the esophagogastric junction (AEG). Methods Pre-NAC clinical and imaging data of AEG patients who underwent surgical resection after preoperative-NAC at two centers were retrospectively collected from November 2014 to September 2020. The dataset included training (n = 60) and external validation groups (n = 32). Three models, including CT-based radiomics, clinical and radiomics–clinical combined models, were established to differentiate pCR (tumor regression grade (TRG) = grade 0) and nonpCR (TRG = grade 1–3) patients. For the radiomics model, tumor-region-based radiomics features in the arterial and venous phases were extracted and selected. The naïve Bayes classifier was used to establish arterial- and venous-phase radiomics models. The selected candidate clinical factors were used to establish a clinical model, which was further incorporated into the radiomics–clinical combined model. ROC analysis, calibration and decision curves were used to assess the model performance. Results For the radiomics model, the AUC values obtained using the venous data were higher than those obtained using the arterial data (training: 0.751 vs. 0.736; validation: 0.768 vs. 0.750). Borrmann typing, tumor thickness and degree of differentiation were utilized to establish the clinical model (AUC-training: 0.753; AUC-validation: 0.848). The combination of arterial- and venous-phase radiomics and clinical factors further improved the discriminatory performance of the model (AUC-training: 0.838; AUC-validation: 0.902). The decision curve reflects the higher net benefit of the combined model. Conclusion The combination of CT imaging and clinical factors pre-NAC for advanced AEG could help stratify potential responsiveness to NAC.

Deep learning or radiomics based on CT for predicting the response of gastric cancer to neoadjuvant chemotherapy: a meta-analysis and systematic review

“…After screening the titles and abstracts, 177 studies were selected for full-text reading. Finally, 9 studies, comprising 23 cohorts, were included for assessing the diagnostic accuracy of AI algorithms in predicting the response of GC to neoadjuvant chemotherapy (29)(30)(31)(32)(33)(34)(35)(36)(37). Out of these, 7 head-tohead studies were analyzed to compare the diagnostic accuracy of AI, CM, and IM.…”

Intratumoral and peritumoral radiomics predict pathological response after neoadjuvant chemotherapy against advanced gastric cancer

“…Hosmer-Lemeshow test were used to evaluate the correction ability of the model. In the Hosmer-Lemeshow test, p > 0.05 indicated no significant difference between the predicted and real value [22]. Calibration curves and decision curves were used to evaluate the optimal model.…”

“…We speculate that this is related to the decline of physical function in the elderly and the resistance to chemotherapy drugs [42]. Furthermore, the RECIST are limited due to the varying degree of gastric filling and low reproducibility of data measurements, which may bias the results [22,43]. Studies have shown that Borrmann type III and IV gastric cancers have a low survival rate and poor prognosis [44,45], while TRG after NAC was also significantly correlated with prognosis [20].…”

“…Radiomics features included first-order features, shape features, gray-level co-occurrence matrix (GLCM), gray-level size zone matrix (GLSZM), gray-level run length matrix (GLRLM), graylevel dependence matrix (GLDM), and neighborhood grayscale difference matrix (NGTDM). These features are commonly used classic features [22].…”

A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors

“…32 Recent studies have depicted that combining radiomics signatures with clinical factors indicates excellent potential in the diagnosis and prognosis of the disease. 33,34 Routine blood tests have a significant role in diagnosing diseases and observing the curative effect and prognosis and are a commonly used clinical examination method. NEU, LYM, and MONO are common infection indicators and systemic inflammation inside the body.…”