Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer.

OBJECTIVES: To investigate if delta-radiomics features have the potential to predict the major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) patients. METHODS: Two hundred six stage IIA-IIIB NSCLC patients from three institutions (Database1 = 164; Database2 = 21; Database3 = 21) who received neoadjuvant chemoimmunotherapy and surgery were included. Patients in Database1 were randomly assigned to the training dataset and test dataset, with a ratio of 0.7:0.3. Patients in Database2 and Database3 were used as two independent external validation datasets. Contrast-enhanced CT scans were obtained at baseline and before surgery. The delta-radiomics features were defined as the relative net change of radiomics features between baseline and preoperative. The delta-radiomics model and pre-treatment radiomics model were established. The performance of Immune-Related Response Evaluation Criteria in Solid Tumors (iRECIST) for predicting MPR was also evaluated. RESULTS: Half of the patients (106/206, 51.5%) showed MPR after neoadjuvant chemoimmunotherapy. For predicting MPR, the delta-radiomics model achieved a satisfying area under the curves (AUCs) values of 0.768, 0.732, 0.833, and 0.716 in the training, test, and two external validation databases, respectively, which showed a superior predictive performance than the pre-treatment radiomics model (0.644, 0.616, 0.475, and 0.608). Compared with iRECIST criteria (0.624, 0.572, 0.650, and 0.466), a mixed model that combines delta-radiomics features and iRECIST had higher AUC values for MPR prediction of 0.777, 0.761, 0.850, and 0.670 in four sets. CONCLUSION: The delta-radiomics model demonstrated superior diagnostic performance compared to pre-treatment radiomics model and iRECIST criteria in predicting MPR preoperatively in neoadjuvant chemoimmunotherapy for stage II-III NSCLC. CLINICAL RELEVANCE STATEMENT: Delta-radiomics features based on the relative net change of radiomics features between baseline and preoperative CT scans serve a vital support tool in accurately identifying responses to neoadjuvant chemoimmunotherapy, which can help physicians make more appropriate treatment decisions. KEY POINTS: • The performances of pre-treatment radiomics model and iRECIST model in predicting major pathological response of neoadjuvant chemoimmunotherapy were unsatisfactory. • The delta-radiomics features based on relative net change of radiomics features between baseline and preoperative CT scans may be used as a noninvasive biomarker for predicting major pathological response of neoadjuvant chemoimmunotherapy. • Combining delta-radiomics features and iRECIST can further improve the predictive performance of responses to neoadjuvant chemoimmunotherapy.

Diagnosis of Esophageal Squamous Cell Carcinoma by High-Performance Serum Metabolic Fingerprints: A Retrospective Study.

Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.

New improved incision-tubing approach for bronchoesophageal Fistula with mediastinal abscess after esophagectomy: A case report.

Bronchoesophageal fistula is a serious threat to the survival after esophagectomy for esophageal cancer. The erosion of mediastinal abscess post anastomotic leakage is the most likely directly cause. However, the bronchoesophageal fistula with gastric conduit necrosis and mediastinal abscess is refractory to either surgical or conservative treatment. In the article, a unique case of Bronchoesophageal fistula with mediastinal abscess after gastric conduit necrosis is presented. A 74-year-old female was detected the right inferior bronchus-esophageal fistula with mediastinal abscess on 15 postoperative day after esophagectomy for esophageal cancer. A successful new improved minimally invasive management was performed.

Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer.

Background: Computed tomography (CT) is important in the diagnosing of lung cancer. The combination of CT features and artificial intelligence algorithm have been used in the diagnosis of various lung diseases. However, limited studies focused on the relationship between the combination of CT features and artificial intelligence algorithm and lymph node metastasis in non-small cell lung cancer (NSCLC). This study developed an algorithm for lung cancer CT image segmentation based on an artificial neural network model and investigated the role of a nomogram model based on CT images for predicting lymph node metastasis in lung cancer. Methods: Wiener filtering and fuzzy enhancement were first used to suppress image noise and improve image contrast. Next, texture features and fractal features were extracted. In the third step, the artificial neural network model was trained and tested according to the best parameters of the network. Results: The area under the curve (AUC) of the constructed nomogram model on the training set and the test set were 0.859 (sensitivity, 0.810; specificity, 0.773) and 0.864 (sensitivity, 0.820; specificity, 0.753), respectively. The decision curve indicated that the model had good clinical application value. The lung cancer CT images contained 13 significant regional features of cancer. The best classification function obtained from training and testing data was Levenberg-Marquardt backpropagation. The sensitivity, specificity, and accuracy in the training stage could reach 98.4%, 100%, and 98.6%, respectively, and the corresponding indexes in the test stage reached 90.9%, 100%, and 95.1%, respectively. Conclusions: The image segmentation algorithm based on the artificial neural network model could extract CT lung cancer lesions efficiently and quasi-determinately, which could be used as an effective tool for radiologists to diagnose lung cancer. The nomogram model based on CT image features and related clinical indicators was an effective method for noninvasive prediction of lymph node metastasis in lung cancer.

Wide Gastric Conduit Increases the Risk of Benign Anastomotic Stricture After Esophagectomy.

BACKGROUND: To identify the association between the width of the gastric conduit and the benign anastomotic stricture (BAS) after esophagectomy with end-to-side cervical anastomosis for esophageal cancer. METHODS: Patients with esophageal cancer who underwent esophagectomy between July 2013 and July 2014 were included in this study. The gastric conduit was used for reconstruction in all patients and end-to-side cervical anastomosis were performed using a circular stapler. The patients were divided into a narrow group (3-5 cm) and a wide group (>5 cm) based on the gastric conduit width. Univariate and multivariate logistic regressions were used to analyze the possible factors (patients' age, gender, preoperative comorbidities, neoadjuvant chemotherapy, gastric conduit width, anastomotic leakage) that could affect the incidence of BAS. RESULTS: Two-hundred and one patients were included in this study. The median follow-up period was 29 months (17-58 months). Seven cases (3.5%) showed anastomotic leakage in the postoperative period and 38 patients (18.9%) developed BAS; all within the first year of follow-up. In univariate analysis, the width of the gastric conduit was the only risk factor for the development of BAS (odds ratio [OR] = 3.36, P = .005). In multivariate logistic regression analysis, the wide group was an independent significant risk factor for the development of BAS developing compared with the narrow group (OR = 2.84, P = .02). CONCLUSIONS: A wide gastric conduit width (>5 cm) is an independent risk factor for the development of BAS after esophagectomy and stapled cervical end-to-side anastomosis for esophageal cancer.

[Clinical features and management of pyothorax due to postoperative cervical anastomotic leakage in esophageal cancer surgery].

OBJECTIVE: To investigate the clinical characteristics and managements of pyothorax due to postoperative cervical anastomotic leakage after esophageal cancer surgery. METHODS: From January 2006 to January 2013, 3342 patients with esophageal carcinoma underwent esophagectomy and cervical esophagogastric anastomosis. Of them, 19 patients developed pyothorax following cervical anastomotic leakage and their clinicopathological data were analyzed retrospectively. RESULTS: All the patients underwent a cervical anastomosis via a three-incisional approach (right cervicothoracic mid-abdominal incision, RT group, n=1094) or a two-incisional approach (left cervicothoracic incision, LT group, n=2248). The total number of cervical anastomotic leakage cases was 237, of which 152 cases were in LT group (6.8%), and 85 cases in RT group (7.8%), respectively (P=0.287). The incidence of pyothorax was 2.0% (n=3) in LT group, and 18.8% (n=16) in RT group, respectively (P<0.01). Fourteen cases develop pyothorax within 3 days after operation. The main symptoms were high fever, dyspnea and chest pain. All the pyothorax patients received conservative treatments, including thoracic closed drainage, nasogastric tube placement, jejunal stoma, nutritional support, antibiotics and symptomatic treatment. Sixteen cases were cured, while 3 cases were dead. CONCLUSIONS: The right thoracotomy approach predisposes the cervical anastomotic leakage-associated pyothorax. Sufficient drainage and sufficient nutritional support are critical to the treatment.