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-based image super-resolution of an end-expandable optical fiber probe for application in esophageal cancer diagnostics.

Significance: Endoscopic screening for esophageal cancer (EC) may enable early cancer diagnosis and treatment. While optical microendoscopic technology has shown promise in improving specificity, the limited field of view (<1 mm) significantly reduces the ability to survey large areas efficiently in EC screening. Aim: To improve the efficiency of endoscopic screening, we propose a novel concept of end-expandable endoscopic optical fiber probe for larger field of visualization and for the first time evaluate a deep-learning-based image super-resolution (DL-SR) method to overcome the issue of limited sampling capability. Approach: To demonstrate feasibility of the end-expandable optical fiber probe, DL-SR was applied on simulated low-resolution microendoscopic images to generate super-resolved (SR) ones. Varying the degradation model of image data acquisition, we identified the optimal parameters for optical fiber probe prototyping. The proposed screening method was validated with a human pathology reading study. Results: For various degradation parameters considered, the DL-SR method demonstrated different levels of improvement of traditional measures of image quality. The endoscopists' interpretations of the SR images were comparable to those performed on the high-resolution ones. Conclusions: This work suggests avenues for development of DL-SR-enabled sparse image reconstruction to improve high-yield EC screening and similar clinical applications.

Prediction of malignant esophageal fistula in esophageal cancer using a radiomics-clinical nomogram.

BACKGROUND: Malignant esophageal fistula (MEF), which occurs in 5% to 15% of esophageal cancer (EC) patients, has a poor prognosis. Accurate identification of esophageal cancer patients at high risk of MEF is challenging. The goal of this study was to build and validate a model to predict the occurrence of esophageal fistula in EC patients. METHODS: This study retrospectively enrolled 122 esophageal cancer patients treated by chemotherapy or chemoradiotherapy (53 with fistula, 69 without), and all patients were randomly assigned to a training (n = 86) and a validation (n = 36) cohort. Radiomic features were extracted from pre-treatment CTs, clinically predictors were identified by logistic regression analysis. Lasso regression model was used for feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the clinical nomogram, radiomics-clinical nomogram and radiomics prediction model. The models were validated and compared by discrimination, calibration, reclassification, and clinical benefit. RESULTS: The radiomic signature consisting of ten selected features, was significantly associated with esophageal fistula (P = 0.001). Radiomics-clinical nomogram was created by two predictors including radiomics signature and stenosis, which was identified by logistic regression analysis. The model showed good discrimination with an AUC = 0.782 (95% CI 0.684-0.8796) in the training set and 0.867 (95% CI 0.7461-0.987) in the validation set, with an AIC = 101.1, and good calibration. When compared to the clinical prediction model, the radiomics-clinical nomogram improved NRI by 0.236 (95% CI 0.153, 0.614) and IDI by 0.125 (95% CI 0.040, 0.210), P = 0.004. CONCLUSION: We developed and validated the first radiomics-clinical nomogram for malignant esophageal fistula, which could assist clinicians in identifying patients at high risk of MEF.

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.

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.

Adenoid Cystic Carcinoma of Esophagus with Lung Metastasis: Case Report.

Adenoid cystic carcinoma (ACC) is a malignant tumor originating in the salivary glands. It most commonly affects the salivary and lacrimal glands, with less frequent occurrences in the esophagus. Esophageal ACC (EACC) typically manifests in the middle or lower parts of the esophagus, with exceedingly rare instances in the upper part. Lung metastasis in EACC is uncommon, and understanding its clinical features and treatment strategies remains challenging. In this study, we present a case of ACC originating in the upper esophagus with lung metastasis. The patient, a middle-aged female, was admitted to the Department of Respiratory and Critical Care Medicine due to an esophageal mass discovered during physical examination that had been present for 4.5 years, along with a newly identified pulmonary nodule for 2 weeks. An X-ray barium meal revealed the presence of a benign esophageal cervical mass. Gastroscopy revealed elevated lesions below the esophageal inlet, and a pathological biopsy confirmed the diagnosis of EACC. The aim of this case report is to enhance understanding of this rare condition and improve clinicians' awareness of the disease. By providing details of the patient's diagnosis, clinical presentation, imaging features and pathological features, we aim to improve diagnostic accuracy and clinical management of similar cases in the future.

Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions.

Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our method stands out for its uniqueness, as it relies solely on a single input image from a patient, forming the so-called "You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data. On the other hand, it avoids nearly all generalization-related problems since each trained network is applied only to the same input image itself. In particular, we can push the training to "over-fitting" as much as possible to increase the segmentation accuracy. Our technical details include an interaction with clinical doctors to utilize their expertise, a geometry-based data augmentation over a single lesion image to generate the training dataset (the biggest novelty), and an edge-enhanced UNet. We have evaluated YOHO over an EEC dataset collected by ourselves and achieved a mean Dice score of 0.888, which is much higher as compared to the existing deep-learning methods, thus representing a significant advance toward clinical applications. The code and dataset are available at: https://github.com/lhaippp/YOHO.

Prognostic value of radiomic features in patients with esophageal cancer treated with chemoradiotherapy.

BACKGROUND: The aim of the present study was to evaluate the prognostic value of radiomic features in patients who underwent chemoradiotherapy for esophageal cancer. METHODS: In this retrospective study, two independent cohorts of esophageal cancer patients treated with chemoradiotherapy were included. Radiomics features of each patient were extracted from pre-treatment computed tomography (CT) images. Radiomic features were selected by employing univariate and multivariate analyses in the test cohort. Selected radiomic features were verified in the validation cohort. The endpoint of the present study was overall survival. RESULTS: A total of 101 esophageal cancer patients were included in our study, with 71 patients in the test cohort and 30 patients in the validation cohort. Univariate analysis identified 158 radiomic features as prognostic factors for overall survival in the test cohort. A multivariate analysis revealed that root mean squared and Low-High-High (LHH) median were prognostic factors for overall survival with a hazard ratio of 2.23 (95% confidence interval [CI]: 1.16-4.70, P = 0.017) and 0.26 (95% CI: 0.13-0.54, P < 0.001), respectively. In the validation cohort, root mean squared high/LHH median low group had the most preferable prognosis with a median overall survival of 73.30 months (95% CI: 32.13-NA), whereas root mean squared low/LHH median low group had the poorest prognosis with a median overall survival of 9.72 months (95% CI: 2.50-NA), with a P value of < 0.001. CONCLUSIONS: We identified two radiomic features that might be independent prognostic factors of overall survival of esophageal cancer patients treated with chemoradiotherapy.

A radiomics strategy based on CT intra-tumoral and peritumoral regions for preoperative prediction of neoadjuvant chemoradiotherapy for esophageal cancer.

OBJECTIVE: Develop a method for selecting esophageal cancer patients achieving pathological complete response with pre-neoadjuvant therapy chest-enhanced CT scans. METHODS: Two hundred and one patients from center 1 were enrolled, split into training and testing sets (7:3 ratio), with an external validation set of 30 patients from center 2. Radiomics features from intra-tumoral and peritumoral images were extracted and dimensionally reduced using Student's t-test and least absolute shrinkage and selection operator. Four machine learning classifiers were employed to build models, with the best-performing models selected based on accuracy and stability. ROC curves were utilized to determine the top prediction model, and its generalizability was evaluated on the external validation set. RESULTS: Among 16 models, the integrated-XGBoost and integrated-random forest models performed the best, with average ROC AUCs of 0.906 and 0.918, respectively, and RSDs of 6.26 and 6.89 in the training set. In the testing set, AUCs were 0.845 and 0.871, showing no significant difference in ROC curves. External validation set AUCs for integrated-XGBoost and integrated-random forest models were 0.650 and 0.749. CONCLUSION: Incorporating peritumoral radiomics features into the analysis enhances predictive performance for esophageal cancer patients undergoing neoadjuvant chemoradiotherapy, paving the way for improved treatment outcomes.

2.5D UNet with context-aware feature sequence fusion for accurate esophageal tumor semantic segmentation.

Segmenting esophageal tumor from computed tomography (CT) sequence images can assist doctors in diagnosing and treating patients with this malignancy. However, accurately extracting esophageal tumor features from CT images often present challenges due to their small area, variable position, and shape, as well as the low contrast with surrounding tissues. This results in not achieving the level of accuracy required for practical applications in current methods. To address this problem, we propose a 2.5D context-aware feature sequence fusion UNet (2.5D CFSF-UNet) model for esophageal tumor segmentation in CT sequence images. Specifically, we embed intra-slice multiscale attention feature fusion (Intra-slice MAFF) in each skip connection of UNet to improve feature learning capabilities, better expressing the differences between anatomical structures within CT sequence images. Additionally, the inter-slice context fusion block (Inter-slice CFB) is utilized in the center bridge of UNet to enhance the depiction of context features between CT slices, thereby preventing the loss of structural information between slices. Experiments are conducted on a dataset of 430 esophageal tumor patients. The results show an 87.13% dice similarity coefficient, a 79.71% intersection over union and a 2.4758 mm Hausdorff distance, which demonstrates that our approach can improve contouring consistency and can be applied to clinical applications.

Preoperative ultrasound-guided dual localization with titanium clips and carbon nanoparticles for predicting the surgical approach and guiding the resection of Siewert type II esophagogastric junction adenocarcinoma.

OBJECTIVE: To investigate the superiority of preoperative ultrasound-guided titanium clip and nanocarbon dual localization over traditional methods for determining the surgical approach and guiding resection of Siewert type II adenocarcinoma of the esophagogastric junction (AEG). METHOD: This study included 66 patients with Siewert type II AEG who were treated at the PLA Joint Logistics Support Force 900th Hospital between September 1, 2021, and September 1, 2023. They were randomly divided into an experimental group (n = 33), in which resection was guided by the dual localization technique, and the routine group (n = 33), in which the localization technique was not used. Surgical approach predictions, proximal esophageal resection lengths, pathological features, and the occurrence of complications were compared between the groups. RESULT: The use of the dual localization technique resulted in higher accuracy in predicting the surgical approach (96.8% vs. 75.9%, P = 0.02) and shorter proximal esophageal resection lengths (2.39 ± 0.28 cm vs. 2.86 ± 0.39 cm, P < 0.001) in the experimental group as compared to the routine group, while there was no significant difference in the incidence of postoperative complications (22.59% vs. 24.14%, P = 0.88). CONCLUSION: Preoperative dual localization with titanium clips and carbon nanoparticles is significantly superior to traditional methods and can reliably delineate the actual infiltration boundaries of Siewert type II AEG, guide the surgical approach, and avoid excessive esophageal resection.

[A case of aortic thrombus during chemotherapy for esophageal cancer].

A 59-year-old man presented to our hospital with a chief complaint of epigastric pain. Pertinent history included a distal gastrectomy for gastric cancer and alcohol dependence. He underwent contrast-enhanced computed tomography (CT) and esophagogastroduodenoscopy, which led to a diagnosis of esophageal cancer (cT2N2M1, stage IVb). Subsequently, he underwent chemotherapy using 5-fluorouracil and cis-diamminedichloroplatinum and radiotherapy. A total of 44 days after treatment initiation, the patient experienced nausea and hepatobiliary enzyme elevation. CT and abdominal ultrasonography were performed, and he was diagnosed with an abdominal aortic thrombus. Intravenous heparin was administered as an anticoagulant therapy. Twenty-two days after treatment initiation, the thrombus was no longer visible on abdominal ultrasonography. The patient was then treated with warfarin. It cannot be ruled out that the patient's hepatobiliary enzyme elevation was induced by the anticancer drugs. However, enzyme elevation improved with the disappearance of the abdominal aortic thrombus, suggesting that the aortic thrombus may have contributed to the hepatobiliary enzyme elevation. No thrombus recurrence was observed until the patient's death after an initial treatment with antithrombotic agents. This case indicates that malignant tumors and chemotherapy can cause aortic thrombi, and thus, care should be exercised in monitoring this potential complication.

Acute pericarditis as the presenting symptom of a case of oesophageal carcinoma.

Oesophageal carcinoma is a globally prevalent form of cancer. Patients with advanced disease often experience progressive dysphagia and weight loss as initial symptoms, but pericarditis is an uncommon presentation. This study describes a young man who presented with pericarditis and was diagnosed with oesophageal squamous cell carcinoma. The patient's diagnosis came after presenting with intermittent chest pain. His diagnostic tests included an ECG showing ST elevation, echocardiography showing pericardial effusion and elevated inflammatory markers. His imaging tests revealed a neoplastic lesion in the lower oesophagus with metastases. He was initially treated as a case of pericarditis, followed by palliative chemotherapy for his cancer. Pericarditis, as the initial presentation of oesophageal carcinoma, is rare. There have only been 19 cases reported and published in the literature. Treatment depends on the stage of the disease. This case emphasises the importance of considering malignancy in unusual presentations of pericarditis, especially in young patients.

Radiomics Features Using Dual-energy CT for Lymph Nodes After Preoperative Chemotherapy for Esophageal Cancer.

BACKGROUND/AIM: Progress has been made in a triplet preoperative chemotherapy regimen for advanced esophageal cancer. We performed a preliminary investigation of the radiomics features of pathological lymph node metastasis after neoadjuvant chemotherapy using dual-energy computed tomography (DECT). PATIENTS AND METHODS: From January to December 2022, 36 lymph nodes from 10 patients with advanced esophageal cancer who underwent contrast-enhanced DECT after neoadjuvant chemotherapy and radical surgery in our department were studied. Radiomics features were extracted from iodine-based material decomposition images at the portal venous phase constructed by DECT using MATLAB analysis software. Receiver operating characteristic (ROC) analysis and cut-off values were determined for the presence or absence of pathological metastasis. RESULTS: ROC for the short axis of the pathologically positive lymph nodes showed an AUC of 0.713. Long run emphasis (LRE) within gray-level run-length matrix (GLRLM) was confirmed with a high AUC of 0.812. Sensitivity and specificity for lymph nodes with a short axis >10 mm were 0.222 and 1, respectively. Sensitivity and specificity for LRE within GLRLM were 0.722 and 0.833, respectively. Sensitivity and specificity for small zone emphasis (SZE) within gray-level size zone matrix (GLSZM) were 0.889 and 0.667, and zone percentage (ZP) values within GLSZM were 0.722 and 0.778, respectively. Discrimination of existing metastases using radiomics showed significantly higher sensitivity compared to lymph node short axis >10 mm (odds ratios of LRE, SZE, and ZP: 9.1, 28, and 9.1, respectively). CONCLUSION: Evaluation of radiomics analysis using DECT may enable a more detailed evaluation of lymph node metastasis after neoadjuvant chemotherapy.

Successful Robot-Assisted Surgery for Advanced Metachronous Cancer in a Gastric Conduit after Esophagectomy: A Case Report.

The incidence of gastric tube cancers has increased due to improved survival rates in patients after esophagectomy. However, the optimal surgical approach for gastric tube cancer remains controversial. Here, we report the case of a 70-year-old man with advanced gastric cancer arising from a retrosternally placed gastric conduit, 12 years after thoracic esophagectomy for esophageal cancer. Total resection of the gastric conduit was performed with robotic assistance. Although the working space was limited, secure resection was possible. Continuous en bloc mobilization was achieved with neck dissection, and reconstruction was performed via the same retrosternal route using the ileocolon. The patient was discharged on the 14th postoperative day without any adverse events. Robot-assisted surgery can overcome the technical limitations of laparoscopic mediastinal surgery and has advantages such as improved ergonomics, comfort, and elimination of hand tremors, and therefore may be an option for future minimally invasive surgeries.

A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors.

Background: We aim to evaluate the value of an integrated multimodal radiomics with machine learning model to predict the pathological complete response (pCR) of primary tumor in a prospective cohort of esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (nCRT) and anti-PD-1 inhibitors. Materials and methods: Clinical information of 126 ESCC patients were included for analysis. Radiomics features were extracted from 18F-FDG PET and enhanced plan CT images. Four machine learning algorithms, including SVM (Support Vector Machine), Random Forest (RF), and eXtreme Gradient Boosting (XGB) and logistic regression (LR), were applied using k-fold cross-validation to predict pCR after nCRT. The predictive ability of the models was assessed using receiver operating characteristics (ROC) curve analysis. Results: A total of 842 features were extracted. Among the four machine learning algorithms, SVM achieved the most promising performance on the test set for PET(AUC:0.775), CT (AUC:0.710) and clinical model (AUC:0.722). For all combinations of various modalities-based models, the combination model of 18 F-FDG PET, CT and clinical features with SVM machine learning had the highest AUC of 0.852 in the test set when compared to single-modality models in various algorithms. The other combined models had AUC ranged 0.716 to 0.775. Conclusion: Machine learning models utilizing radiomics features from 18F-FDG PET and enhanced plan CT exhibit promising performance in predicting pCR in ESCC after nCRT and anti-PD-1 inhibitors. The fusion of features from multiple modalities radiomics and clinical features enhances the better predictive performance compared to using a single modality alone.

PARP1-targeted fluorescence molecular endoscopy as novel tool for early detection of esophageal dysplasia and adenocarcinoma.

BACKGROUND: Esophageal cancer is one of the 10 most common cancers worldwide and its incidence is dramatically increasing. Despite some improvements, the current surveillance protocol with white light endoscopy and random untargeted biopsies collection (Seattle protocol) fails to diagnose dysplastic and cancerous lesions in up to 50% of patients. Therefore, new endoscopic imaging technologies in combination with tumor-specific molecular probes are needed to improve early detection. Herein, we investigated the use of the fluorescent Poly (ADP-ribose) Polymerase 1 (PARP1)-inhibitor PARPi-FL for early detection of dysplastic lesions in patient-derived organoids and transgenic mouse models, which closely mimic the transformation from non-malignant Barrett's Esophagus (BE) to invasive esophageal adenocarcinoma (EAC). METHODS: We determined PARP1 expression via immunohistochemistry (IHC) in human biospecimens and mouse tissues. We also assessed PARPi-FL uptake in patient- and mouse-derived organoids. Following intravenous injection of 75 nmol PARPi-FL/mouse in L2-IL1B (n = 4) and L2-IL1B/IL8Tg mice (n = 12), we conducted fluorescence molecular endoscopy (FME) and/or imaged whole excised stomachs to assess PARPi-FL accumulation in dysplastic lesions. L2-IL1B/IL8Tg mice (n = 3) and wild-type (WT) mice (n = 2) without PARPi-FL injection served as controls. The imaging results were validated by confocal microscopy and IHC of excised tissues. RESULTS: IHC on patient and murine tissue revealed similar patterns of increasing PARP1 expression in presence of dysplasia and cancer. In human and murine organoids, PARPi-FL localized to PARP1-expressing epithelial cell nuclei after 10 min of incubation. Injection of PARPi-FL in transgenic mouse models of BE resulted in the successful detection of lesions via FME, with a mean target-to-background ratio > 2 independently from the disease stage. The localization of PARPi-FL in the lesions was confirmed by imaging of the excised stomachs and confocal microscopy. Without PARPi-FL injection, identification of lesions via FME in transgenic mice was not possible. CONCLUSION: PARPi-FL imaging is a promising approach for clinically needed improved detection of dysplastic and malignant EAC lesions in patients with BE. Since PARPi-FL is currently evaluated in a phase 2 clinical trial for oral cancer detection after topical application, clinical translation for early detection of dysplasia and EAC in BE patients via FME screening appears feasible.

Association of preoperative muscle-adipose index measured by computed tomography with survival in patients with esophageal squamous cell carcinoma.

BACKGROUND: Conventional nutritional metrics are closely associated with the prognosis of patients with radically resected esophageal squamous cell carcinoma (ESCC). Nevertheless, the prognostic implications of muscle and adipose tissue composite indexes in ESCC remain unknown. METHODS: We retrospectively analyzed clinicopathological data of 304 patients who underwent resected ESCC. To obtain measurements of the muscle and adipose indexes, preoperative computed tomography (CT) images were used to quantify skeletal-muscle adipose tissue. The diagnostic threshold for muscle-adipose imbalance was determined using X-tile software and used to analyze the association between the muscle-adipose index (MAI) and survival. Instantaneous risk of recurrence was assessed using a hazard function. We constructed a nomogram based on the MAI and other clinical characteristics and established a novel predictive model with independent prognostic factors. The prognostic capabilities of these nomograms were evaluated using calibration curves, receiver operating characteristic (ROC) curves, and decision-curve analysis (DCA). RESULTS: The overall survival (OS) and disease-free survival (DFS) rates in the muscle-adipose-balanced group were significantly better than those in the muscle-adipose-imbalanced group. Multivariate analyses revealed that the MAI, prognostic nutritional index (PNI), tumor stage, and tumor differentiation were independent prognostic factors for OS and DFS in patients with resected ESCC (P < 0.05). The nuclear density curve indicated a lower risk of recurrence for patients in the muscle-adipose-balanced group than that for their imbalanced counterparts. Conversely, the nuclear density curve for PNI was confounded. Postoperative radiotherapy- (RT) benefit analysis demonstrated that patients with ESCC in the muscle-adipose-balanced group could benefit from adjuvant RT. CONCLUSION: This study confirmed that preoperative MAI could serve as a useful independent prognostic factor in patients with resected ESCC. A nomogram based on the MAI and other clinical characteristics could provide individualized survival prediction for patients receiving radical resection. Timely and appropriate nutritional supplements may improve treatment efficacy.

Dual-energy computed tomography in the early evaluation of acute radiation pneumonia.

OBJECTIVE: To investigate the value of dual-energy dual-source computed tomography (DSCT) in evaluating pulmonary perfusion changes before and after radiotherapy for esophageal cancer, and its clinical use in the early diagnosis of acute radiation pneumonia (ARP). METHODS: We selected 45 patients with pathologically confirmed esophageal cancer who received radiotherapy (total irradiation dose of 60 Gy). Dual-energy DSCT scans were performed before and after radiotherapy and the normalized iodine concentrations (NIC) in the lung fields of the areas irradiated with doses of > 20 Gy, 10-20 Gy, 5-10 Gy, and < 5 Gy were measured. We also checked for the occurrence of ARP in the patients, and the differences in NIC values and NIC reduction rates before and after radiotherapy were calculated and statistically analyzed. RESULTS: A total of 16 of the 45 patients developed ARP. The NIC values in the lung fields of all patients decreased at different degrees after radiotherapy, and the NIC values in the area where ARP developed, decreased significantly. The rate of NIC reduction and incidence rate of ARP increased gradually with the increasing irradiation dose, and the inter-group difference in NIC reduction rate was statistically significant (P < 0.05). Based on the receiver operating characteristic (ROC) curve analysis, the areas under the curves of NIC reduction rate versus ARP occurrence in the V5-10 Gy, V10-20 Gy, and V> 20 Gy groups were 0.780, 0.808, and 0.772, respectively. Sensitivity of diagnosis was 81.3%, 75.0%, and 68.8% and the specificity was 65.5%, 82.8%, and 79.3%, when taking 12.50%, 16.50%, and 26.0% as the diagnostic thresholds, respectively. The difference in NIC values in the lung fields of V<5 Gy before and after radiotherapy was not statistically significant (P > 0.05). CONCLUSION: The dual-energy DSCT could effectively evaluate pulmonary perfusion changes after radiotherapy for esophageal cancer, and the NIC reduction rate was useful as a reference index to predict ARP and provide further reference for decisions in clinical practice.