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.

Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis.

BACKGROUND: Esophageal cancer, a global health concern, impacts predominantly men, particularly in Eastern Asia. Lymph node metastasis (LNM) significantly influences prognosis, and current imaging methods exhibit limitations in accurate detection. The integration of radiomics, an artificial intelligence (AI) driven approach in medical imaging, offers a transformative potential. This meta-analysis evaluates existing evidence on the accuracy of radiomics models for predicting LNM in esophageal cancer. METHODS: We conducted a systematic review following PRISMA 2020 guidelines, searching Embase, PubMed, and Web of Science for English-language studies up to November 16, 2023. Inclusion criteria focused on preoperatively diagnosed esophageal cancer patients with radiomics predicting LNM before treatment. Exclusion criteria were applied, including non-English studies and those lacking sufficient data or separate validation cohorts. Data extraction encompassed study characteristics and radiomics technical details. Quality assessment employed modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) tools. Statistical analysis involved random-effects models for pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Heterogeneity and publication bias were assessed using Deek's test and funnel plots. Analysis was performed using Stata version 17.0 and meta-DiSc. RESULTS: Out of 426 initially identified citations, nine studies met inclusion criteria, encompassing 719 patients. These retrospective studies utilized CT, PET, and MRI imaging modalities, predominantly conducted in China. Two studies employed deep learning-based radiomics. Quality assessment revealed acceptable QUADAS-2 scores. RQS scores ranged from 9 to 14, averaging 12.78. The diagnostic meta-analysis yielded a pooled sensitivity, specificity, and AUC of 0.72, 0.76, and 0.74, respectively, representing fair diagnostic performance. Meta-regression identified the use of combined models as a significant contributor to heterogeneity (p-value = 0.05). Other factors, such as sample size (> 75) and least absolute shrinkage and selection operator (LASSO) usage for feature extraction, showed potential influence but lacked statistical significance (0.05 < p-value < 0.10). Publication bias was not statistically significant. CONCLUSION: Radiomics shows potential for predicting LNM in esophageal cancer, with a moderate diagnostic performance. Standardized approaches, ongoing research, and prospective validation studies are crucial for realizing its clinical applicability.

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.

Esophageal schwannoma mimicking non-functional parathyroid adenoma on 99mTc-sestamibi imaging: a case report.

Technetium-99m sestamibi single-photon emission computed tomography/computed tomography (99mTc-sestamibi SPECT/CT) is a mainstay of the pre-operative localization of parathyroid lesions. We report here the case of a 30 year-old woman with a fortuitously discovered 2 cm cervical mass for which a parathyroid origin was originally suspected due to its retro-thyroidal localization and a personal history of nephrolithiasis. Normal serum calcium and parathyroid hormone (PTH) levels excluded primary hyperparathyroidism, raising suspicion of a non-functional parathyroid adenoma, and SPECT/CT imaging showed that the mass was 99mTc-sestamibi-avid. Fine-needle aspiration (FNA) was performed; cytology was non-diagnostic but the needle washout was negative for thyroglobulin, calcitonin and PTH, arguing against a thyroidal or parathyroidal origin of the mass. Core needle biopsy revealed a schwannoma, ostensibly originating from the recurrent laryngeal nerve; upon surgical resection, it was finally found to arise from the esophageal submucosa. This case illustrates the fact that endocrinologists, radiologists, nuclear medicine, head and neck, and other specialists investigating patients with cervical masses should be aware that schwannomas need to be considered in the differential diagnosis of focal 99mTc-sestamibi uptake in the neck region.

Radiomics in esophagogastric junction cancer: A scoping review of current status and advances.

PURPOSE: This scoping review aimed to understand the advances in radiomics in esophagogastric junction (EGJ) cancer and assess the current status of radiomics in EGJ cancer. METHODS: We conducted systematic searches of PubMed, Embase, and Web of Science databases from January 18, 2012, to January 15, 2023, to identify radiomics articles related to EGJ cancer. Two researchers independently screened the literature, extracted data, and assessed the quality of the studies using the Radiomics Quality Score (RQS) and the METhodological RadiomICs Score (METRICS) tool, respectively. RESULTS: A total of 120 articles were retrieved from the three databases, and after screening, only six papers met the inclusion criteria. These studies investigated the role of radiomics in differentiating adenocarcinoma from squamous carcinoma, diagnosing T-stage, evaluating HER2 overexpression, predicting response to neoadjuvant therapy, and prognosis in EGJ cancer. The median score percentage of RQS was 34.7% (range from 22.2% to 38.9%). The median score percentage of METRICS was 71.2% (range from 58.2% to 84.9%). CONCLUSION: Although there is a considerable difference between the RQS and METRICS scores of the included literature, we believe that the research value of radiomics in EGJ cancer has been revealed. In the future, while actively exploring more diagnostic, prognostic, and biological correlation studies in EGJ cancer, greater emphasis should be placed on the standardization and clinical application of radiomics.

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 transformer-guided cross-modality adaptive feature fusion framework for esophageal gross tumor volume segmentation.

BACKGROUND AND OBJECTIVE: Accurate segmentation of esophageal gross tumor volume (GTV) indirectly enhances the efficacy of radiotherapy for patients with esophagus cancer. In this domain, learning-based methods have been employed to fuse cross-modality positron emission tomography (PET) and computed tomography (CT) images, aiming to improve segmentation accuracy. This fusion is essential as it combines functional metabolic information from PET with anatomical information from CT, providing complementary information. While the existing three-dimensional (3D) segmentation method has achieved state-of-the-art (SOTA) performance, it typically relies on pure-convolution architectures, limiting its ability to capture long-range spatial dependencies due to convolution's confinement to a local receptive field. To address this limitation and further enhance esophageal GTV segmentation performance, this work proposes a transformer-guided cross-modality adaptive feature fusion network, referred to as TransAttPSNN, which is based on cross-modality PET/CT scans. METHODS: Specifically, we establish an attention progressive semantically-nested network (AttPSNN) by incorporating the convolutional attention mechanism into the progressive semantically-nested network (PSNN). Subsequently, we devise a plug-and-play transformer-guided cross-modality adaptive feature fusion model, which is inserted between the multi-scale feature counterparts of a two-stream AttPSNN backbone (one for the PET modality flow and another for the CT modality flow), resulting in the proposed TransAttPSNN architecture. RESULTS: Through extensive four-fold cross-validation experiments on the clinical PET/CT cohort. The proposed approach acquires a Dice similarity coefficient (DSC) of 0.76 ± 0.13, a Hausdorff distance (HD) of 9.38 ± 8.76 mm, and a Mean surface distance (MSD) of 1.13 ± 0.94 mm, outperforming the SOTA competing methods. The qualitative results show a satisfying consistency with the lesion areas. CONCLUSIONS: The devised transformer-guided cross-modality adaptive feature fusion module integrates the strengths of PET and CT, effectively enhancing the segmentation performance of esophageal GTV. The proposed TransAttPSNN has further advanced the research of esophageal GTV segmentation.

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.

Ultrasound-Guided Quantitative Fluorescence Molecular Endoscopy for Monitoring Response in Patients with Esophageal Cancer Following Neoadjuvant Chemoradiotherapy.

PURPOSE: The ability to identify residual tumor tissues in patients with locally advanced esophageal cancer following neoadjuvant chemoradiotherapy (nCRT) is essential for monitoring the treatment response. Using the fluorescent tracer bevacizumab-800CW, we evaluated whether ultrasound-guided quantitative fluorescent molecular endoscopy (US-qFME), which combines quantitative fluorescence molecular endoscopy (qFME) with ultrasound-guided needle biopsy/single-fiber fluorescence (USNB/SFF), can be used to identify residual tumor tissues in patients following nCRT. EXPERIMENTAL DESIGN: Twenty patients received an additional endoscopy procedure the day before surgery. qFME was performed at the primary tumor site (PTS) and in healthy tissue to first establish the optimal tracer dose. USNB/SFF was then used to measure intrinsic fluorescence in the deeper PTS layers and lymph nodes (LN) suspected for metastasis. Finally, the intrinsic fluorescence and the tissue optical properties-specifically, the absorption and reduced scattering coefficients-were combined into a new parameter called omega. RESULTS: First, a 25-mg bevacizumab-800CW dose allowed for clear differentiation between the PTS and healthy tissue, with a target-to-background ratio (TBR) of 2.98 (IQR, 1.86-3.03). Moreover, we found a clear difference between the deeper esophageal PTS layers and suspected LN compared to healthy tissues, with TBR values of 2.18 and 2.17, respectively. Finally, our new parameter, omega, further improved the ability to differentiate between the PTS and healthy tissue. CONCLUSIONS: Combining bevacizumab-800CW with US-qFME may serve as a viable strategy for monitoring the response to nCRT in esophageal cancer and may help stratify patients regarding active surveillance versus surgery.

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.

Impact of FAPI-46/dual-tracer PET/CT imaging on radiotherapeutic management in esophageal cancer.

BACKGROUND: Fibroblast activation protein (FAP) is expressed in the tumor microenvironment (TME) of various cancers. In our analysis, we describe the impact of dual-tracer imaging with Gallium-68-radiolabeled inhibitors of FAP (FAPI-46-PET/CT) and fluorodeoxy-D-glucose (FDG-PET/CT) on the radiotherapeutic management of primary esophageal cancer (EC). METHODS: 32 patients with EC, who are scheduled for chemoradiation, received FDG and FAPI-46 PET/CT on the same day (dual-tracer protocol, 71%) or on two separate days (29%) We compared functional tumor volumes (FTVs), gross tumor volumes (GTVs) and tumor stages before and after PET-imaging. Changes in treatment were categorized as "minor" (adaption of radiation field) or "major" (change of treatment regimen). Immunohistochemistry (IHC) staining for FAP was performed in all patients with available tissue. RESULTS: Primary tumor was detected in all FAPI-46/dual-tracer scans and in 30/32 (93%) of FDG scans. Compared to the initial staging CT scan, 12/32 patients (38%) were upstaged in nodal status after the combination of FDG and FAPI-46 PET scans. Two lymph node metastases were only visible in FAPI-46/dual-tracer. New distant metastasis was observed in 2/32 (6%) patients following FAPI-4 -PET/CT. Our findings led to larger RT fields ("minor change") in 5/32 patients (16%) and changed treatment regimen ("major change") in 3/32 patients after FAPI-46/dual-tracer PET/CT. GTVs were larger in FAPI-46/dual-tracer scans compared to FDG-PET/CT (mean 99.0 vs. 80.3 ml, respectively (p < 0.001)) with similar results for nuclear medical FTVs. IHC revealed heterogenous FAP-expression in all specimens (mean H-score: 36.3 (SD 24.6)) without correlation between FAP expression in IHC and FAPI tracer uptake in PET/CT. CONCLUSION: We report first data on the use of PET with FAPI-46 for patients with EC, who are scheduled to receive RT. Tumor uptake was high and not depending on FAP expression in TME. Further, FAPI-46/dual-tracer PET had relevant impact on management in this setting. Our data calls for prospective evaluation of FAPI-46/dual-tracer PET to improve clinical outcomes of EC.

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.

Role of indocyanine green fluorescence imaging for evaluating blood supply in the gastric conduit via the substernal route after McKeown minimally invasive esophagectomy.

BACKGROUND: Anastomotic leakage (AL) is a determining factor of morbidity and mortality after esophagectomy. Adequate perfusion of the gastric conduit is crucial for AL prevention. This study aimed to determine whether intraoperative angiography using indocyanine green (ICG) fluorescence improves the incidence of AL after McKeown minimally invasive esophagectomy (MIE) with gastric conduit via the substernal route (SR). METHODS: This retrospective cohort study included 120 patients who underwent MIE with gastric conduit via SR for esophageal cancer between February 2019 and April 2023. Of 120 patients, 88 experienced intraoperative angiography using ICG (ICG group), and 32 patients experienced intraoperative angiography without ICG (no-ICG group). Baseline characteristics and operative outcomes, including AL as the main concern, were compared between the 2 groups. In addition, the outcomes among patients in the ICG group with different levels of fluorescence intensity were compared. RESULTS: The ICG and no-ICG groups were comparable in baseline characteristics and operative outcomes. There was no significant difference between the 2 groups regarding the rate of AL (31.0% vs 37.5%; P = .505), median dates of AL (9 vs 9 days; P = .810), and severity of AL (88.9%, 11.11%, and 0.0% vs 66.7%, 16.7%, and 16.7% for grades I, II, and III, respectively; P = .074). Patients in the ICG group with lower intensity of ICG had higher rates of leakage (24.6%, 39.3%, and 100% in levels I, II, and III of ICG intensity, respectively; P = .04). CONCLUSION: The use of ICG did not seem to reduce the rate of AL. However, abnormal intensity of ICG fluorescence was associated with a higher rate of AL, which implies a predictive potential.

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.

Association between interstitial lung abnormality and mortality in patients with esophageal cancer.

PURPOSE: To investigate the relationship between interstitial lung abnormalities (ILAs) and mortality in patients with esophageal cancer and the cause of mortality. MATERIALS AND METHODS: This retrospective study investigated patients with esophageal cancer from January 2011 to December 2015. ILAs were visually scored on baseline CT using a 3-point scale (0 = non-ILA, 1 = indeterminate for ILA, and 2 = ILA). ILAs were classified into subcategories of non-subpleural, subpleural non-fibrotic, and subpleural fibrotic. Five-year overall survival (OS) was compared between patients with and without ILAs using the multivariable Cox proportional hazards model. Subgroup analyses were performed based on cancer stage and ILA subcategories. The prevalences of treatment complications and death due to esophageal cancer and pneumonia/respiratory failure were analyzed using Fisher's exact test. RESULTS: A total of 478 patients with esophageal cancer (age, 66.8 years ± 8.6 [standard deviation]; 64 women) were evaluated in this study. Among them, 267 patients showed no ILAs, 125 patients were indeterminate for ILAs, and 86 patients showed ILAs. ILAs were a significant factor for shorter OS (hazard ratio [HR] = 1.68, 95% confidence interval [CI] 1.10-2.55, P = 0.016) in the multivariable Cox proportional hazards model adjusting for age, sex, smoking history, clinical stage, and histology. On subgroup analysis using patients with clinical stage IVB, the presence of ILAs was a significant factor (HR = 3.78, 95% CI 1.67-8.54, P = 0.001). Subpleural fibrotic ILAs were significantly associated with shorter OS (HR = 2.22, 95% CI 1.25-3.93, P = 0.006). There was no significant difference in treatment complications. Patients with ILAs showed a higher prevalence of death due to pneumonia/respiratory failure than those without ILAs (non-ILA, 2/95 [2%]; ILA, 5/39 [13%]; P = 0.022). The prevalence of death due to esophageal cancer was similar in patients with and without ILA (non-ILA, 82/95 [86%]; ILA 32/39 [82%]; P = 0.596). CONCLUSION: ILAs were significantly associated with shorter survival in patients with esophageal cancer.