A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma.

Background: Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display variable treatment responses. The purpose of this study is to establish and validate a radiomics based on enhanced computed tomography (CT) and combined with clinical data to predict the major pathological response to NIT in ESCC patients. Methods: This retrospective study included 82 ESCC patients who were randomly divided into the training group (n = 57) and the validation group (n = 25). Radiomic features were derived from the tumor region in enhanced CT images obtained before treatment. After feature reduction and screening, radiomics was established. Logistic regression analysis was conducted to select clinical variables. The predictive model integrating radiomics and clinical data was constructed and presented as a nomogram. Area under curve (AUC) was applied to evaluate the predictive ability of the models, and decision curve analysis (DCA) and calibration curves were performed to test the application of the models. Results: One clinical data (radiotherapy) and 10 radiomic features were identified and applied for the predictive model. The radiomics integrated with clinical data could achieve excellent predictive performance, with AUC values of 0.93 (95% CI 0.87-0.99) and 0.85 (95% CI 0.69-1.00) in the training group and the validation group, respectively. DCA and calibration curves demonstrated a good clinical feasibility and utility of this model. Conclusion: Enhanced CT image-based radiomics could predict the response of ESCC patients to NIT with high accuracy and robustness. The developed predictive model offers a valuable tool for assessing treatment efficacy prior to initiating therapy, thus providing individualized treatment regimens for patients.

CT-based delta-radiomics nomogram to predict pathological complete response after neoadjuvant chemoradiotherapy in esophageal squamous cell carcinoma patients.

BACKGROUND: This study developed a nomogram model using CT-based delta-radiomics features and clinical factors to predict pathological complete response (pCR) in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiotherapy (nCRT). METHODS: The study retrospectively analyzed 232 ESCC patients who underwent pretreatment and post-treatment CT scans. Patients were divided into training (n = 186) and validation (n = 46) sets through fivefold cross-validation. 837 radiomics features were extracted from regions of interest (ROIs) delineations on CT images before and after nCRT to calculate delta values. The LASSO algorithm selected delta-radiomics features (DRF) based on classification performance. Logistic regression constructed a nomogram incorporating DRFs and clinical factors. Receiver operating characteristic (ROC) and area under the curve (AUC) analyses evaluated nomogram performance for predicting pCR. RESULTS: No significant differences existed between the training and validation datasets. The 4-feature delta-radiomics signature (DRS) demonstrated good predictive accuracy for pCR, with α-binormal-based and empirical AUCs of 0.871 and 0.869. T-stage (p = 0.001) and differentiation degree (p = 0.018) were independent predictors of pCR. The nomogram combined the DRS and clinical factors improved the classification performance in the training dataset (AUCαbin = 0.933 and AUCemp = 0.941). The validation set showed similar performance with AUCs of 0.958 and 0.962. CONCLUSIONS: The CT-based delta-radiomics nomogram model with clinical factors provided high predictive accuracy for pCR in ESCC patients after nCRT.

A nomogram based on pretreatment radiomics and dosiomics features for predicting overall survival associated with esophageal squamous cell cancer.

OBJECTIVES: To propose a nomogram-based survival prediction model for esophageal squamous cell carcinoma (ESCC) treated with definitive chemoradiotherapy using pretreatment computed tomography (CT), positron emission tomography (PET) radiomics and dosiomics features, and common clinical factors. METHODS: Radiomics and dosiomics features were extracted from CT and PET images and dose distribution from 2 institutions. The least absolute shrinkage and selection operator (LASSO) with logistic regression was used to select radiomics and dosiomics features by calculating the radiomics and dosiomics scores (Rad-score and Dos-score), respectively, in the training model. The model was trained in 81 patients and validated in 35 patients at Center 1 using 10-fold cross validation. The model was externally tested in 26 patients at Center 2. The predictive clinical factors, Rad-score, and Dos-score were identified to develop a nomogram model. RESULTS: Using LASSO Cox regression, 13, 11, and 19 CT, PET-based radiomics, and dosiomics features, respectively, were selected. The clinical factors T-stage, N-stage, and clinical stage were selected as significant prognostic factors by univariate Cox regression. In the external validation cohort, the C-index of the combined model of CT-based radiomics, PET-based radiomics, and dosiomics features with clinical factors were 0.74, 0.82, and 0.92, respectively. Significant differences in overall survival (OS) in the combined model of CT-based radiomics, PET-based radiomics, and dosiomics features with clinical factors were observed between the high- and low-risk groups (P = 0.019, 0.038, and 0.014, respectively). CONCLUSION: The dosiomics features have a better predicter for OS than CT- and PET-based radiomics features in ESCC treated with radiotherapy. CLINICAL RELEVANCE STATEMENT: The current study predicted the overall survival for esophageal squamous cell carcinoma patients treated with definitive chemoradiotherapy. The dosiomics features have a better predicter for overall survival than CT- and PET-based radiomics features.

Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma.

BACKGROUND: Neoadjuvant immunochemotherapy (NICT) plus esophagectomy has emerged as a promising treatment option for locally advanced esophageal squamous cell carcinoma (LA-ESCC). Pathologic complete response (pCR) is a key indicator associated with great efficacy and overall survival (OS). However, there are insufficient indicators for the reliable assessment of pCR. METHODS: 192 patients with LA-ESCC treated with NICT from December 2019 to October 2023 were recruited. According to pCR status, patients were categorized into pCR group (22.92%) and non-pCR group (77.08%). Radiological features of pretreatment and preoperative CT images were extracted. Logistic and COX regressions were trained to predict pathological response and prognosis, respectively. RESULTS: Four of the selected radiological features were combined to construct an ESCC preoperative imaging score (ECPI-Score). Logistic models revealed independent associations of ECPI-Score and vascular sign with pCR, with AUC of 0.918 in the training set and 0.862 in the validation set, respectively. After grouping by ECPI-Score, a higher proportion of pCR was observed among the high-ECPI group and negative vascular sign. Kaplan Meier analysis demonstrated that recurrence-free survival (RFS) with negative vascular sign was significantly better than those with positive (P = 0.038), but not for OS (P = 0.310). CONCLUSIONS: This study demonstrates dynamic radiological features are independent predictors of pCR for LA-ESCC treated with NICT. It will guide clinicians to make accurate treatment plans.

Association between prognosis and lymph node status using 18F-fluorodeoxyglucose-positron emission tomography in esophageal squamous cell carcinoma treated with esophagectomy post-neoadjuvant chemotherapy.

BACKGROUND: There are few reports on the associations between lymph node (LN) status, determined by preoperative 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET), and prognosis in patients with locally advanced esophageal squamous cell carcinoma (ESCC) who underwent esophagectomy post-neoadjuvant chemotherapy (NCT). Additionally, details on the diagnostic performance of LN metastasis determination based on pathological examination versus FDG-PET have not been reported. In this study, we aimed to evaluate the associations among LN status using FDG-PET, LN status based on pathological examination, and prognosis in patients with locally advanced ESCC who underwent esophagectomy post-NCT. METHODS: We reviewed the data of 124 consecutive patients with ESCC who underwent esophagectomy with R0 resection post-NCT between December 2008 and August 2022 and were evaluated pre- and post-NCT using FDG-PET. The associations among LN status using FDG-PET, LN status based on pathological examination, and prognosis were assessed. RESULTS: Station-by-station analysis of PET-positive LNs pre- and post-NCT correlated significantly with pathological LN metastases (sensitivity, specificity, and accuracy pre- and post-NCT: 51.6%, 96.0%, and 92.1%; and 28.2%, 99.5%, and 93.1%, respectively; both p < 0.0001). Using univariate and multivariate analyses, LN status determined using PET post-NCT was a significant independent predictor of both recurrence-free survival and overall survival. CONCLUSION: The LN status assessed using FDG-PET post-NCT was significantly associated with the pathological LN status and prognosis in patients with ESCC who underwent esophagectomy post-NCT. Therefore, FDG-PET is a useful diagnostic tool for preoperatively predicting pathological LN metastasis and survival in these patients and could provide valuable information for selecting individualized treatment strategies.

Potential value of CT-based comprehensive nomogram in predicting occult lymph node metastasis of esophageal squamous cell paralaryngeal nerves: a two-center study.

PURPOSE: The aim of this study is to construct a combined model that integrates radiomics, clinical risk factors and machine learning algorithms to predict para-laryngeal lymph node metastasis in esophageal squamous cell carcinoma. METHODS: A retrospective study included 361 patients with esophageal squamous cell carcinoma from 2 centers. Radiomics features were extracted from the computed tomography scans. Logistic regression, k nearest neighbor, multilayer perceptron, light Gradient Boosting Machine, support vector machine, random forest algorithms were used to construct radiomics models. The receiver operating characteristic curve and The Hosmer-Lemeshow test were employed to select the better-performing model. Clinical risk factors were identified through univariate logistic regression analysis and multivariate logistic regression analysis and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis. RESULTS: A total of 1024 radiomics features were extracted. Among the radiomics models, the KNN model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.84 in the training cohort and 0.62 in the internal test cohort. Furthermore, the combined model exhibited an AUC of 0.97 in the training cohort and 0.86 in the internal test cohort. CONCLUSION: A clinical-radiomics integrated nomogram can predict occult para-laryngeal lymph node metastasis in esophageal squamous cell carcinoma and provide guidance for personalized treatment.

HRU-Net: A high-resolution convolutional neural network for esophageal cancer radiotherapy target segmentation.

BACKGROUND AND OBJECTIVE: The effective segmentation of esophageal squamous carcinoma lesions in CT scans is significant for auxiliary diagnosis and treatment. However, accurate lesion segmentation is still a challenging task due to the irregular form of the esophagus and small size, the inconsistency of spatio-temporal structure, and low contrast of esophagus and its peripheral tissues in medical images. The objective of this study is to improve the segmentation effect of esophageal squamous cell carcinoma lesions. METHODS: It is critical for a segmentation network to effectively extract 3D discriminative features to distinguish esophageal cancers from some visually closed adjacent esophageal tissues and organs. In this work, an efficient HRU-Net architecture (High-Resolution U-Net) was exploited for esophageal cancer and esophageal carcinoma segmentation in CT slices. Based on the idea of localization first and segmentation later, the HRU-Net locates the esophageal region before segmentation. In addition, an Resolution Fusion Module (RFM) was designed to integrate the information of adjacent resolution feature maps to obtain strong semantic information, as well as preserve the high-resolution features. RESULTS: Compared with the other five typical methods, the devised HRU-Net is capable of generating superior segmentation results. CONCLUSIONS: Our proposed HRU-NET improves the accuracy of segmentation for squamous esophageal cancer. Compared to other models, our model performs the best. The designed method may improve the efficiency of clinical diagnosis of esophageal squamous cell carcinoma lesions.

A combined nomogram based on radiomics and hematology to predict the pathological complete response of neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma.

BACKGROUND: To predict pathological complete response (pCR) in patients receiving neoadjuvant immunochemotherapy (nICT) for esophageal squamous cell carcinoma (ESCC), we explored the factors that influence pCR after nICT and established a combined nomogram model. METHODS: We retrospectively included 164 ESCC patients treated with nICT. The radiomics signature and hematology model were constructed utilizing least absolute shrinkage and selection operator (LASSO) regression, and the radiomics score (radScore) and hematology score (hemScore) were determined for each patient. Using the radScore, hemScore, and independent influencing factors obtained through univariate and multivariate analyses, a combined nomogram was established. The consistency and prediction ability of the nomogram were assessed utilizing calibration curve and the area under the receiver operating factor curve (AUC), and the clinical benefits were assessed utilizing decision curve analysis (DCA). RESULTS: We constructed three predictive models.The AUC values of the radiomics signature and hematology model reached 0.874 (95% CI: 0.819-0.928) and 0.772 (95% CI: 0.699-0.845), respectively. Tumor length, cN stage, the radScore, and the hemScore were found to be independent factors influencing pCR according to univariate and multivariate analyses (P < 0.05). A combined nomogram was constructed from these factors, and AUC reached 0.934 (95% CI: 0.896-0.972). DCA demonstrated that the clinical benefits brought by the nomogram for patients across an extensive range were greater than those of other individual models. CONCLUSIONS: By combining CT radiomics, hematological factors, and clinicopathological characteristics before treatment, we developed a nomogram model that effectively predicted whether ESCC patients would achieve pCR after nICT, thus identifying patients who are sensitive to nICT and assisting in clinical treatment decision-making.

Transparency-enhancing technology allows the three-dimensional assessment of esophageal carcinoma obtained by endoscopic submucosal dissection.

BACKGROUND: Although much progress has been made in diagnosis of carcinomas, no established methods have been confirmed to elucidate their morphological features. METHODS: Three-dimensional structure of esophageal carcinomas was assessed using transparency-enhancing technology. Endoscopically resected esophageal squamous cell carcinoma was fluorescently stained, optically cleared using a transparency-enhancing reagent called LUCID, and visualized using laser scanning microscopy. The resulting microscope images were converted to virtual HE images for observation using ImageJ software. RESULTS: Microscopic observation and image editing enabled three-dimensional image reconstruction and conversion to virtual HE images. The structure of abnormal blood vessels in esophageal carcinoma recognized by endoscopy could be observed in the 3 dimensions. Squamous cell carcinoma and normal squamous epithelium could be distinguished in the virtual HE images. CONCLUSIONS: The results suggested that transparency-enhancing technology and virtual HE images may be feasible for clinical application and represent a novel histopathological method for evaluating endoscopically resected specimens.

The role of spleen radiomics model for predicting prognosis in esophageal squamous cell carcinoma patients receiving definitive radiotherapy.

BACKGROUND: The spleen plays an important role in systemic antitumor immune response, but whether spleen imaging features have predictive effect for prognosis and immune status was unknown. The aim of this study was to investigate computed tomography (CT)-based spleen radiomics to predict the prognosis of patients with esophageal squamous cell carcinoma (ESCC) underwent definitive radiotherapy (dRT) and to try to find its association with systemic immunity. METHODS: This retrospective study included 201 ESCC patients who received dRT. Patients were randomly divided into training (n = 142) and validation (n = 59) groups. The pre- and delta-radiomic features were extracted from enhanced CT images. LASSO-Cox regression was used to select the radiomics signatures most associated with progression-free survival (PFS) and overall survival (OS). Independent prognostic factors were identified by univariate and multivariate Cox analyses. The ROC curve and C-index were used to evaluate the predictive performance. Finally, the correlation between spleen radiomics and immune-related hematological parameters was analyzed by spearman correlation analysis. RESULTS: Independent prognostic factors involved TNM stage, treatment regimen, tumor location, pre- or delta-Rad-score. The AUC of the delta-radiomics combined model was better than other models in the training and validation groups in predicting PFS (0.829 and 0.875, respectively) and OS (0.857 and 0.835, respectively). Furthermore, some spleen delta-radiomic features are significantly correlated with delta-ALC (absolute lymphocyte count) and delta-NLR (neutrophil-to-lymphocyte ratio). CONCLUSIONS: Spleen radiomics is expected to be a useful noninvasive tool for predicting the prognosis and evaluating systemic immune status for ESCC patients underwent dRT.

Treatment Response to Neoadjuvant Therapy in Squamous Esophageal Cancer-Correlation Between Metabolic Response and Histopathology.

PURPOSE: Esophageal cancer is among the leading causes of cancer-related mortality worldwide. Patients presenting with localized and loco-regionally advanced cancer without distant metastases have reasonable survival with multimodality management. Adequate and comprehensive staging is the backbone for proper selection of patients fit for curative treatment. Positron emission tomography (PET) in combination with contrast-enhanced computed tomography (CECT) is utilized as the standard staging modality. Multimodality treatment has been able to achieve evaluable tumor responses including pathological complete response (pCR). It is, therefore, necessary to understand whether the impact of neoadjuvant therapy can be evaluated on imaging, i.e., standardized uptake value (SUV) on PET scan done for response assessment and if this can be correlated with histopathological response and later, with survival. Squamous cell carcinoma (SCC) is more common globally and in the Indian subcontinent; hence, we chose this subgroup to evaluate our hypothesis. METHODS: This is a single institution, retrospective study. Out of the 1967 patients who were treated between 2009 and 2019, 1369 (78.54%) patients had SCC. Out of these, 44 received NACTRT, whereas 1325 received NACT followed by curative surgery. The standardized uptake value (SUV) of 18-fluorodeoxyglucose was recorded during pre- and post-neoadjuvant treatment (NAT) using positron emission tomography (PET). The histopathology of the final resection specimen was evaluated using the Mandard tumor regression grade (TRG) criteria with response being graded from 0 to 5 as no residual tumor (NRT), scanty residual tumor (SRT), and residual tumor We attempted to find a cut-off value of the post neoadjuvant SUV of the primary tumor site which correlated with achievement of better histopathological response. RESULTS: Out of 1325 patients of SCC esophagus who underwent surgery, 943 patients had available data of TRG, and it was categorized into the 0-2 category which had 325 patients (34.5%) and 3-5 category, 618 patients (65.5%). The SUV was taken only from the PET scans done at our institution, so as to achieve a more homogenous cohort, and this was available for 186 patients, 151 from the NACT group and 35 from the NACTRT group. The ROC method was used to find the cut-off for SUV (5.05) in the NACT cohort, which depicted significant difference in the outcome. Out of these, 93 patients who underwent NACT had SUV > 5.05 and 58 had SUV < 5.05. It was found that the subjective and objective histopathological scores correlated at a p value of < 0.0001. Specifically, the majority of cases with SRT tended to be in the 3-5 category of TRG, whereas cases with NRT are predominantly in the 0-2 category. In the ≥ 5.05 category of SUV, there were 76 cases with SRT. In the NACT cohort, the < 5.05 category of SUV, there are 26 cases with SRT and 32 cases with NRT. Among cases with SRT, 74.5% had SUV ≥ 5.05, while 25.5% had SUV < 5.05. Among cases with NRT, 34.7% had SUV ≥ 5.05, while 65.3% had SUV < 5.05 (p value 0.007). No significant association was found in the radio-pathological correlation in the NACTRT group. CONCLUSION: Our study confirms the correlation of post neoadjuvant chemotherapy PET SUV with histopathological response, the cut-off of SUV being 5.05 in our cohort. This confirms the predictive value of FDG PET as demonstrated in other studies. Furthermore, its prognostic value with respect to survival has been verified in multiple other studies. With larger scale randomized studies, we may be able to identify the group of patients who have borderline operability anatomically as well as physiologically, where alternative treatment regimens may be indicated to improve outcomes.

Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach.

BACKGROUND: Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. Despite this, current techniques, which can be invasive and costly, struggle with achieving the necessary precision, highlighting a pressing need for more effective, non-invasive alternatives. METHOD: We developed ResoLSTM-Depth, a deep learning model to distinguish ESCC stages T1-T2 from T3-T4. It integrates ResNet-18 and Long Short-Term Memory (LSTM) networks, leveraging their strengths in spatial and sequential data processing. This method uses arterial phase CT scans from ESCC patients. The dataset was meticulously segmented by an experienced radiologist for effective training and validation. RESULTS: Upon performing five-fold cross-validation, the ResoLSTM-Depth model exhibited commendable performance with an accuracy of 0.857, an AUC of 0.901, a sensitivity of 0.884, and a specificity of 0.828. These results were superior to the ResNet-18 model alone, where the average accuracy is 0.824 and the AUC is 0.879. Attention maps further highlighted influential features for depth prediction, enhancing model interpretability. CONCLUSION: ResoLSTM-Depth is a promising tool for ESCC invasion depth prediction. It offers potential for improvement in the staging and therapeutic planning of ESCC.

Contrast-enhanced CT radiomics features to preoperatively identify differences between tumor and proximal tumor-adjacent and tumor-distant tissues of resectable esophageal squamous cell carcinoma.

BACKGROUND: Esophagectomy is the main treatment for esophageal squamous cell carcinoma (ESCC), and patients with histopathologically negative margins still have a relatively higher recurrence rate. Contrast-enhanced CT (CECT) radiomics might noninvasively obtain potential information about the internal heterogeneity of ESCC and its adjacent tissues. This study aimed to develop CECT radiomics models to preoperatively identify the differences between tumor and proximal tumor-adjacent and tumor-distant tissues in ESCC to potentially reduce tumor recurrence. METHODS: A total of 529 consecutive patients with ESCC from Centers A (n = 447) and B (n = 82) undergoing preoperative CECT were retrospectively enrolled in this study. Radiomics features of the tumor, proximal tumor-adjacent (PTA) and proximal tumor-distant (PTD) tissues were individually extracted by delineating the corresponding region of interest (ROI) on CECT and applying the 3D-Slicer radiomics module. Patients with pairwise tissues (ESCC vs. PTA, ESCC vs. PTD, and PTA vs. PTD) from Center A were randomly assigned to the training cohort (TC, n = 313) and internal validation cohort (IVC, n = 134). Univariate analysis and the least absolute shrinkage and selection operator were used to select the core radiomics features, and logistic regression was performed to develop radiomics models to differentiate individual pairwise tissues in TC, validated in IVC and the external validation cohort (EVC) from Center B. Diagnostic performance was assessed using area under the receiver operating characteristics curve (AUC) and accuracy. RESULTS: With the chosen 20, 19 and 5 core radiomics features in TC, 3 individual radiomics models were developed, which exhibited excellent ability to differentiate the tumor from PTA tissue (AUC: 0.965; accuracy: 0.965), the tumor from PTD tissue (AUC: 0.991; accuracy: 0.958), and PTA from PTD tissue (AUC: 0.870; accuracy: 0.848), respectively. In IVC and EVC, the models also showed good performance in differentiating the tumor from PTA tissue (AUCs: 0.956 and 0.962; accuracy: 0.956 and 0.937), the tumor from PTD tissue (AUCs: 0.990 and 0.974; accuracy: 0.952 and 0.970), and PTA from PTD tissue (AUCs: 0.806 and 0.786; accuracy: 0.760 and 0.786), respectively. CONCLUSION: CECT radiomics models could differentiate the tumor from PTA tissue, the tumor from PTD tissue, and PTA from PTD tissue in ESCC.

Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma.

OBJECTIVES: This research aimed to develop a radiomics-clinical nomogram based on enhanced thin-section CT radiomics and clinical features for the purpose of predicting the presence or absence of metastasis in lymph nodes among patients with resectable esophageal squamous cell carcinoma (ESCC). METHODS: This study examined the data of 256 patients with ESCC, including 140 cases with lymph node metastasis. Clinical information was gathered for each case, and radiomics features were derived from thin-section contrast-enhanced CT with the help of a 3D slicer. To validate risk factors that are independent of the clinical and radiomics models, least absolute shrinkage and selection operator logistic regression analysis was used. A nomogram pattern was constructed based on the radiomics features and clinical characteristics. The receiver operating characteristic curve and Brier Score were used to evaluate the model's discriminatory ability, the calibration plot to evaluate the model's calibration, and the decision curve analysis to evaluate the model's clinical utility. The confusion matrix was used to evaluate the applicability of the model. To evaluate the efficacy of the model, 1000 rounds of 5-fold cross-validation were conducted. RESULTS: The clinical model identified esophageal wall thickness and clinical T (cT) stage as independent risk factors, whereas the radiomics pattern was built based on 4 radiomics features chosen at random. Area under the curve (AUC) values of 0.684 and 0.701 are observed for the radiomics approach and clinical model, respectively. The AUC of nomogram combining radiomics and clinical features was 0.711. The calibration plot showed good agreement between the incidence of lymph node metastasis predicted by the nomogram and the actual probability of occurrence. The nomogram model displayed acceptable levels of performance. After 1000 rounds of 5-fold cross-validation, the AUC and Brier score had median values of 0.702 (IQR: 0.65, 7.49) and 0.21 (IQR: 0.20, 0.23), respectively. High-risk patients (risk point >110) were found to have an increased risk of lymph node metastasis [odds ratio (OR) = 5.15, 95% CI, 2.95-8.99] based on the risk categorization. CONCLUSION: A successful preoperative prediction performance for metastasis to the lymph nodes among patients with ESCC was demonstrated by the nomogram that incorporated CT radiomics, wall thickness, and cT stage. ADVANCES IN KNOWLEDGE: This study demonstrates a novel radiomics-clinical nomogram for lymph node metastasis prediction in ESCC, which helps physicians determine lymph node status preoperatively.

Radiomics and dosiomics for predicting complete response to definitive chemoradiotherapy patients with oesophageal squamous cell cancer using the hybrid institution model.

OBJECTIVES: To develop a multi-institutional prediction model to estimate the local response to oesophageal squamous cell carcinoma (ESCC) treated with definitive radiotherapy based on radiomics and dosiomics features. METHODS: The local responses were categorised into two groups (incomplete and complete). An external validation model and a hybrid model that the patients from two institutions were mixed randomly were proposed. The ESCC patients at stages I-IV who underwent chemoradiotherapy from 2012 to 2017 and had follow-up duration of more than 5 years were included. The patients who received palliative or pre-operable radiotherapy and had no FDG PET images were excluded. The segmentations included the GTV, CTV, and PTV which are used in treatment planning. In addition, shrinkage, expansion, and shell regions were created. Radiomic and dosiomic features were extracted from CT, FDG PET images, and dose distribution. Machine learning-based prediction models were developed using decision tree, support vector machine, k-nearest neighbour (kNN) algorithm, and neural network (NN) classifiers. RESULTS: A total of 116 and 26 patients enrolled at Centre 1 and Centre 2, respectively. The external validation model exhibited the highest accuracy with 65.4% for CT-based radiomics, 77.9% for PET-based radiomics, and 72.1% for dosiomics based on the NN classifiers. The hybrid model exhibited the highest accuracy of 84.4% for CT-based radiomics based on the kNN classifier, 86.0% for PET-based radiomics, and 79.0% for dosiomics based on the NN classifiers. CONCLUSION: The proposed hybrid model exhibited promising predictive performance for the local response to definitive radiotherapy in ESCC patients. CLINICAL RELEVANCE STATEMENT: The prediction of the complete response for oesophageal cancer patients may contribute to improving overall survival. The hybrid model has the potential to improve prediction performance than the external validation model that was conventionally proposed. KEY POINTS: • Radiomics and dosiomics used to predict response in patients with oesophageal cancer receiving definitive radiotherapy. • Hybrid model with neural network classifier of PET-based radiomics improved prediction accuracy by 8.1%. • The hybrid model has the potential to improve prediction performance.

The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma.

OBJECTIVES: To investigate the MRI radiomics signatures in predicting pathologic response among patients with locally advanced esophageal squamous cell carcinoma (ESCC), who received neoadjuvant chemotherapy (NACT). METHODS: Patients who underwent NACT from March 2015 to October 2019 were prospectively included. Each patient underwent esophageal MR scanning within one week before NACT and within 2-3 weeks after completion of NACT, prior to surgery. Radiomics features extracted from T2-TSE-BLADE were randomly split into the training and validation sets at a ratio of 7:3. According to the progressive tumor regression grade (TRG), patients were stratified into two groups: good responders (GR, TRG 0 + 1) and poor responders (non-GR, TRG 2 + 3). We constructed the Pre/Post-NACT model (Pre/Post-model) and the Delta-NACT model (Delta-model). Kruskal-Wallis was used to select features, logistic regression was used to develop the final model. RESULTS: A total of 108 ESCC patients were included, and 3/2/4 out of 107 radiomics features were selected for constructing the Pre/Post/Delta-model, respectively. The selected radiomics features were statistically different between GR and non-GR groups. The highest area under the curve (AUC) was for the Delta-model, which reached 0.851 in the training set and 0.831 in the validation set. Among the three models, Pre-model showed the poorest performance in the training and validation sets (AUC, 0.466 and 0.596), and the Post-model showed better performance than the Pre-model in the training and validation sets (AUC, 0.753 and 0.781). CONCLUSIONS: MRI-based radiomics models can predict the pathological response after NACT in ESCC patients, with the Delta-model exhibiting optimal predictive efficacy. CLINICAL RELEVANCE STATEMENT: MRI radiomics features could be used as a useful tool for predicting the efficacy of neoadjuvant chemotherapy in esophageal carcinoma patients, especially in selecting responders among those patients who may be candidates to benefit from neoadjuvant chemotherapy. KEY POINTS: • The MRI radiomics features based on T2WI-TSE-BLADE could potentially predict the pathologic response to NACT among ESCC patients. • The Delta-model exhibited the best predictive ability for pathologic response, followed by the Post-model, which similarly had better predictive ability, while the Pre-model performed less well in predicting TRG.

Preoperative Prediction of Perineural Invasion in Oesophageal Squamous Cell Carcinoma Based on CT Radiomics Nomogram: A Multicenter Study.

RATIONALE AND OBJECTIVES: To investigate the value of computed tomography (CT) radiomics nomogram in the preoperative prediction of perineural invasion (PNI) in oesophageal squamous cell carcinoma (ESCC) through a multicenter study. MATERIALS AND METHODS: We retrospectively collected postoperative pathological data of 360 ESCC patients with definite PNI status (131 PNI-positive and 229 PNI-negative) from two centres. Radiomic features were extracted from the arterial-phase CT images, and the least absolute shrinkage and selection operator and logistic regression algorithm were used to screen valuable features for identifying the PNI status and calculating the radiomics score (Rad-score). A radiomics nomogram was established by integrating the Rad-score and clinical risk factors. A receiver operating characteristic curve was used to evaluate model performance, and decision curve analysis was used to evaluate the predictive performance of the radiomics nomogram in the training, internal validation, and external validation sets. RESULTS: Twenty radiomics features were extracted from a full-volume tumour region of interest to construct the model, and the radiomics nomogram combined with radiomics features and clinical risk factors was superior to the clinical and radiomics models in predicting the PNI status of ESCC patients. The area under the curve values of the radiomics nomogram in the training, internal validation, and external validation sets were 0.856 (0.794-0.918), 0.832 (0.742-0.922), and 0.803 (0.709-0.898), respectively. CONCLUSION: The radiomics nomogram based on CT has excellent predictive ability; it can non-invasively predict the preoperative PNI status of ESCC patients and provide a basis for preoperative decision-making.