Online decision tools for personalized survival prediction and treatment optimization in elderly patients with lung squamous cell carcinoma: a retrospective cohort study.

BACKGROUND: Despite major advances in cancer therapeutics, the therapeutic options of Lung Squamous Cell Carcinoma (LSCC)-specific remain limited. Furthermore, the current staging system is imperfect for defining a prognosis and guiding treatment due to its simplicity and heterogeneity. We sought to develop prognostic decision tools for individualized survival prediction and treatment optimization in elderly patients with LSCC. METHODS: Clinical data of 4564 patients (stageIB-IIIB) diagnosed from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database for prognostic nomograms development. The proposed models were externally validated using a separate group consisting of 1299 patients (stage IB-IIIB) diagnosed from 2012-2015 in China. The prognostic performance was measured using the concordance index (C-index), calibration curves, the average time-dependent area under the receiver operator characteristic curves (AUC), and decision curve analysis. RESULTS: Eleven candidate prognostic variables were identified by the univariable and multivariable Cox regression analysis. The calibration curves showed satisfactory agreement between the actual and nomogram-estimated Lung Cancer-Specific Survival (LCSS) rates. By calculating the c-indices and average AUC, our nomograms presented a higher prognostic accuracy than the current staging system. Clinical usefulness was revealed by the decision curve analysis. User-friendly online decision tools integrating proposed nomograms were created to estimate survival for patients with different treatment regimens. CONCLUSIONS: The decision tools for individualized survival prediction and treatment optimization might facilitate clinicians with decision-making, medical teaching, and experimental design. Online tools are expected to be integrated into clinical practice by using the freely available website ( https://loyal-brand-611803.framer.app/ ).

Appropriateness and Comprehensiveness of Using ChatGPT for Perioperative Patient Education in Thoracic Surgery in Different Language Contexts: Survey Study.

BACKGROUND: ChatGPT, a dialogue-based artificial intelligence language model, has shown promise in assisting clinical workflows and patient-clinician communication. However, there is a lack of feasibility assessments regarding its use for perioperative patient education in thoracic surgery. OBJECTIVE: This study aimed to assess the appropriateness and comprehensiveness of using ChatGPT for perioperative patient education in thoracic surgery in both English and Chinese contexts. METHODS: This pilot study was conducted in February 2023. A total of 37 questions focused on perioperative patient education in thoracic surgery were created based on guidelines and clinical experience. Two sets of inquiries were made to ChatGPT for each question, one in English and the other in Chinese. The responses generated by ChatGPT were evaluated separately by experienced thoracic surgical clinicians for appropriateness and comprehensiveness based on a hypothetical draft response to a patient's question on the electronic information platform. For a response to be qualified, it required at least 80% of reviewers to deem it appropriate and 50% to deem it comprehensive. Statistical analyses were performed using the unpaired chi-square test or Fisher exact test, with a significance level set at P<.05. RESULTS: The set of 37 commonly asked questions covered topics such as disease information, diagnostic procedures, perioperative complications, treatment measures, disease prevention, and perioperative care considerations. In both the English and Chinese contexts, 34 (92%) out of 37 responses were qualified in terms of both appropriateness and comprehensiveness. The remaining 3 (8%) responses were unqualified in these 2 contexts. The unqualified responses primarily involved the diagnosis of disease symptoms and surgical-related complications symptoms. The reasons for determining the responses as unqualified were similar in both contexts. There was no statistically significant difference (34/37, 92% vs 34/37, 92%; P=.99) in the qualification rate between the 2 language sets. CONCLUSIONS: This pilot study demonstrates the potential feasibility of using ChatGPT for perioperative patient education in thoracic surgery in both English and Chinese contexts. ChatGPT is expected to enhance patient satisfaction, reduce anxiety, and improve compliance during the perioperative period. In the future, there will be remarkable potential application for using artificial intelligence, in conjunction with human review, for patient education and health consultation after patients have provided their informed consent.

Design Appropriate Incision Length for Uniportal Video-Assisted Thoracoscopic Lobectomy: Take into Account Safety and Minimal Invasiveness.

BACKGROUND: There is no criterion on the length of the uniportal video-assisted thoracoscopic surgery (UVATS) incision when performing lobectomy. We aimed to develop a nomogram to assist surgeons in designing incision length for different individuals. METHODS: A cohort consisting of 290 patients were enrolled for nomogram development. Univariate and multivariate logistic regression analyses were performed to identify candidate variables among perioperative characteristics. C-index and calibration curves were utilized for evaluating the performance of the nomogram. Short-term outcomes of nomogram-predicted high-risk patients were compared between long incision group and conventional incision group. RESULTS: Of 290 patients, 150 cases (51.7%) were performed incision extension during the surgery. Age, tumor size, and tumor location were identified as candidate variables related with intraoperative incision extension and were incorporated into the nomogram. C-index of the nomogram was 0.75 (95% confidence interval: 0.6961-0.8064), indicating the good predictive performance. Calibration curves presented good consistency between the nomogram prediction and actual observation. Of high-risk patients identified by the nomogram, the long incision group (n = 47) presented shorter duration of operation (p = 0.03), lower incidence of total complications (p = 0.01), and lower incidence of prolonged air leak (p = 0.03) compared with the conventional incision group (n = 55). CONCLUSION: We developed a novel nomogram for predicting the risk of intraoperative incision extension when performing uniportal video-assisted thoracoscopic lobectomy. This model has the potential to assist clinicians in designing the incision length preoperatively to ensure both safety and minimal invasiveness.

Development and Validation of a Clinical Prognostic Nomogram for Esophageal Adenocarcinoma Patients.

BACKGROUND: Clinical staging is essential for clinical decisions but remains imprecise. We purposed to construct a novel survival prediction model for improving clinical staging system (cTNM) for patients with esophageal adenocarcioma (EAC). METHODS: A total of 4180 patients diagnosed with EAC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and included as the training cohort. Significant prognostic variables were identified for nomogram model development using multivariable Cox regression. The model was validated internally by bootstrap resampling, and then subjected to external validation with a separate cohort of 886 patients from 2 institutions in China. The prognostic performance was measured by concordance index (C-index), Akaike information criterion (AIC) and calibration plots. Different risk groups were stratified by the nomogram scores. RESULTS: A total of six variables were determined related with survival and entered into the nomogram construction. The calibration curves showed satisfied agreement between nomogram-predicted survival and actual observed survival for 1-, 3-, and 5-year overall survival. By calculating the AIC and C-index values, our nomogram presented superior discriminative and risk-stratifying ability than current TNM staging system. Significant distinctions in survival curves were observed between different risk subgroups stratified by nomogram scores. CONCLUSION: The established and validated nomogram presented better risk-stratifying ability than current clinical staging system, and could provide a convenient and reliable tool for individual survival prediction and treatment strategy making.

Development and validation of a new clinical staging system to predict survival for esophageal squamous cell carcinoma patients: Application of the nomogram.

INTRODUCTION: Survival of patients with the same clinical stage varies widely and effective tools to evaluate the prognosis utilizing clinical staging information is lacking. This study aimed to develop a clinical nomogram for predicting survival of patients with Esophageal Squamous Cell Carcinoma (ESCC). MATERIALS AND METHODS: On the basis of data extracted from the SEER database (training cohort, n = 3375), we identified and integrated significant prognostic factors for nomogram development and internal validation. The model was then subjected to external validation with a separate dataset obtained from Jinling Hospital of Nanjing Medical University (validation cohort, n = 1187). The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index), Akaike information criterion (AIC) and calibration curves. And risk group stratification was performed basing on the nomogram scores. RESULTS: On multivariable analysis of the training cohort, seven independent prognostic factors were identified and included into the nomogram. Calibration curves presented good consistency between the nomogram prediction and actual observation for 1-, 3-, and 5-year OS. The AIC value of the nomogram was lower than that of the 8th edition American Joint Committee on Cancer TNM (AJCC) staging system, whereas the C-index of the nomogram was significantly higher than that of the AJCC staging system. The risk groups stratified by CART allowed significant distinction between survival curves within respective clinical TNM categories. CONCLUSIONS: The risk stratification system presented better discriminative ability for survival prediction than current clinical staging system and might help clinicians in decision making.

An artificial neural network model predicting pathologic nodal metastases in clinical stage I-II esophageal squamous cell carcinoma patients.

BACKGROUND: Current preoperative staging for lymph nodal status remains inaccurate. The purpose of this study was to build an artificial neural network (ANN) model to predict pathologic nodal involvement in clinical stage I-II esophageal squamous cell carcinoma (ESCC) patients and then validated the performance of the model. METHODS: A total of 523 patients (training set: 350; test set: 173) with clinical staging I-II ESCC who underwent esophagectomy and reconstruction were enrolled in this study. Their post-surgical pathological results were assessed and analysed. An ANN model was established for predicting pathologic nodal positive patients in the training set, which was validated in the test set. A receiver operating characteristic (ROC) curve was also created to illustrate the performance of the predictive model. RESULTS: Of the enrolled 523 patients with ESCC, 41.3% of the patients were confirmed pathologic nodal positive (216/523). The ANN staging system identified the tumour invasion depth, tumour length, dysphagia, tumour differentiation and lymphovascular invasion (LVI) as predictors for pathologic lymph node metastases. The C-index for the ANN model verified in the test set was 0.852, which demonstrated that the ANN model had a good predictive performance. CONCLUSIONS: The ANN model presented good performance for predicting pathologic lymph node metastasis and added indicators not included in current staging criteria and might help improve the staging strategies.

Effect of different therapeutic strategies on the clinical outcome of asymptomatic intralobar pulmonary sequestration.

OBJECTIVES: Pulmonary sequestration is a rare congenital pulmonary malformation. The aim of this study was to explore the effect of different therapeutic strategies on the clinical outcome of asymptomatic intralobar pulmonary sequestration. METHODS: We retrospectively reviewed the clinical data of 37 patients diagnosed with intralobar sequestration. All the patients were asymptomatic. Seventeen patients underwent video-assisted thoracoscopic surgery (VATS) once diagnosed and 20 patients chose to undergo observation. Of these 20 patients, 16 patients developed symptoms during the observation period and also underwent VATS; 4 patients never showed symptoms and did not have surgery. The 33 patients who had VATS were divided into 2 groups: group 1, patients who underwent VATS once diagnosed; group 2, patients who underwent VATS once symptoms appeared. Postoperative data and respiratory function data were compared between the 2 groups. RESULTS: Twenty of the patients were men and 17 were women (mean age 37.05 ± 7.89 years). Results of a comparative analysis of the 2 groups indicated that patients in group 1 had better values for median estimated blood loss, median duration of chest tube insertion, postoperative hospital stay and postoperative hospital stay than those in group 2. Postoperative complications were reported in 1 patient in group 1 and in 3 patients in group 2. Meanwhile, the loss of lung function between group 1 and group 2 was statistically significant, which also suggested that patients benefited from surgery once diagnosed. CONCLUSIONS: For asymptomatic intralobar sequestration, VATS could be effective and safe. The surgical intervention should be performed once the condition is diagnosed to avoid manifestations occurring and to preserve patients' quality of life.

C-reactive protein to albumin ratio is a key indicator in a predictive model for anastomosis leakage after esophagectomy: Application of classification and regression tree analysis.

BACKGROUND: Anastomotic leakage (AL), a serious complication after esophagectomy, might impair patient quality of life, prolong hospital stay, and even lead to surgery-related death. The aim of this study was to show a novel decision model based on classification and regression tree (CART) analysis for the prediction of postoperative AL among patients who have undergone esophagectomy. METHODS: A total of 450 patients (training set: 356; test set: 94) with perioperative information were included. A decision tree model was established to identify the predictors of AL in the training set, which was validated in the test set. A receiver operating characteristic curve was also created to illustrate the diagnostic ability of the decision model. RESULTS: A total of 12.2% (55/450) of the 450 patients suffered AL, which was diagnosed at median postoperative day 7 (range: 6-16). The decision tree model, containing surgical duration, postoperative lymphocyte count, and postoperative C-reactive protein to albumin ratio, was established by CART analysis. Among the three variables, the postoperative C-reactive protein to albumin ratio was identified as the most important indicator in the CART model with normalized importance of 100%. According to the results validated in the test set, the sensitivity, specificity, positive and negative predictive value, and diagnostic accuracy of the prediction model were 80%, 98.8%, 88.9%, 97.6%, and 96.8%, respectively. Moreover, the area under the receiver operating characteristic curve was 0.95. CONCLUSION: The decision model based on CART analysis presented good performance for predicting AL, and might allow the early identification of patients at high risk.