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.

18F-fluorodeoxyglucose positron emission tomography/computed tomography parameters are associated with histological outcomes in superficial esophageal squamous cell carcinoma.

Endoscopic submucosal dissection is a standard treatment for early esophageal squamous cell carcinoma. However, submucosal or lymphovascular invasion increases the risk of lymph node metastasis. Although 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) parameters are associated with prognosis in patients with advanced esophageal squamous cell carcinoma, the utility of FDG PET/CT in diagnosing superficial esophageal carcinoma remains unclear. This study aimed to investigate the association between FDG PET/CT parameters and histopathological findings in superficial esophageal carcinoma. Fifty-three patients with superficial esophageal cancer who underwent FDG PET/CT scans before undergoing interventions were retrospectively analyzed. The maximal standardized uptake value (SUVmax), metabolic tumor volume, and total lesion glycolysis were significantly higher in the cases with submucosal invasion (T1b) compared with those confined to the muscularis mucosa (T1a). In contrast, classification of intrapapillary capillary loops patterns with magnifying endoscopy did not yield statistical differences between T1a and T1b. Multivariable analysis revealed that SUVmax was the only independent predictor of submucosal and lymphovascular invasion. This study demonstrated that SUVmax may be useful in predicting submucosal and lymphovascular invasion. Thus, the value of SUVmax may guide clinical decision-making in superficial esophageal squamous cell carcinoma.

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.

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.

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.

Involved-field radiotherapy in older patients with superficial thoracic esophageal squamous cell carcinoma: long-term outcomes and recurrence patterns.

PURPOSE: An optimal radiotherapy field for superficial esophageal carcinoma is yet to be established. We evaluated the long-term outcomes and recurrence patterns of involved-field radiotherapy (IFRT) in older patients with superficial thoracic esophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS: Fifty-four patients (49 men and 5 women; mean age, 77 [range: 66-90] years) who underwent IFRT for superficial thoracic ESCC between January 2003 and January 2019 were retrospectively reviewed. Concurrent chemotherapy was administered at the discretion of the attending physician. The primary endpoint was overall survival. The secondary endpoints were progression-free survival and complete response rate. RESULTS: The tumors were localized in the upper, middle, and lower thoracic esophagus in 2, 40, and 12 patients, respectively. All patients underwent IFRT using anteroposterior and anterior-posterior oblique opposed beams (off-cord). The prescribed total doses were 50.4, 59.4-61.2, and 66-70 Gy for 6, 40, and 8 patients, respectively. Concurrent chemotherapy was administered to 33 patients. The median follow-up duration was 57 months. The median overall survival was 115 months. The 5-year overall and progression-free survival rates were 71.7% and 60.1%, respectively. Forty-nine patients had a complete response at one month after IFRT (complete response rate: 90.7%). Twenty patients had recurrence; there were 13 in-field and 7 out-of-field recurrence cases. The radiation-related adverse events were generally mild. Grade 3 late toxicity was observed in one patient. CONCLUSIONS: The efficacy of IFRT was suggested to be comparable to that of standard treatments. Therefore, IFRT can be a promising approach for treating superficial ESCC in older adults, especially those with severe comorbidities.

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.

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.

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.

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.

Deep Learning for Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study.

PURPOSE: To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation therapy treatment planning. METHODS AND MATERIALS: In this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by 2 experts via consensus were used as ground truth. A 3-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in 3 validation cohorts. The AI tool was compared against 12 board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance. Additionally, our previously established radiomics model for predicting pathologic complete response was used to compare AI-generated and ground truth contours, to assess the potential of the AI contouring tool in radiomics analysis. RESULTS: The AI tool demonstrated good GTV contouring performance in multicenter validation cohorts, with median DSC values of 0.865, 0.876, and 0.866 and median average surface distance values of 0.939, 0.789, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of 12 board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = .003-0.048), reduced the intra- and interobserver variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pathologic complete response prediction performance for these contours (P = .430) was observed. CONCLUSIONS: Our AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicates its potential for GTV contouring during radiation therapy treatment planning and radiomics studies.

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.

Consensus Clustering Analysis Based on Enhanced-CT Radiomic Features: Esophageal Squamous Cell Carcinoma patients' 3-Year Progression-Free Survival.

RATIONALE AND OBJECTIVES: To assess the efficacy of consensus cluster analysis based on CT radiomics in stratifying risk and predicting postoperative progression-free survival (PFS) in patients diagnosed with esophageal squamous cell carcinoma (ESC). MATERIALS AND METHODS: We conducted a retrospective study involving 546 patients diagnosed with ESC between January 2016 and March 2021. All patients underwent preoperative enhanced CT examinations. From the enhanced CT images, radiomics features were extracted, and a consensus clustering algorithm was applied to group the patients based on these features. Statistical analysis was performed to examine the relationship between the clustering results and gene protein expression, histopathological features, and patients' 3-year PFS. We applied the Kruskal-Wallis test for continuous data, chi-square or Fisher's exact tests for categorical data, and the log-rank test for PFS. RESULTS: This study identified four groups: Cluster 1 (n = 100, 18.3%), Cluster 2 (n = 197, 36.1%), Cluster 3 (n = 205, 37.5%), and Cluster 4 (n = 44, 8.1%). The cancer gene Breast Cancer Susceptibility Gene 1 (BRCA1) was most highly expressed in Cluster 4 (75%), showing significant differences between the four subtypes with a P-value of 0.035. The expression of programmed death-1 (PD-1) was highest in Cluster 1 (51%), with a P-value of 0.022. Vascular invasion occurred most frequently in Cluster 2 (28.9%), with a P-value of 0.022. The majority of patients with stage T3-4 were in Cluster 2 (67%), with a P-value of 0.003. Kaplan-Meier survival analysis revealed significant differences in PFS between the four groups (P = 0.013). Among them, patients in Cluster 1 had the best prognosis, while those in Cluster 2 had the worst. CONCLUSION: This study highlights the effectiveness of consensus clustering analysis based on enhanced CT radiomics features in identifying associations between radiomics features, histopathological characteristics, and prognosis in different clusters. These findings provide valuable insights for clinicians in accurately and effectively evaluating the prognosis of esophageal cancer.

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.

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.