Right upper lobe segmentectomy and subsegmentectomy guided by classification pattern of peripheral segmental veins.

Background: Studies have analyzed the simplified branching pattern of peripheral segmental veins and developed a standardized approach for intersegmental vein identification in the right upper lobe (RUL). However, the identification approach of intersubsegmental veins has not been reported. This study aimed to supplement the identification approach of intersubsegmental veins and the classification pattern of peripheral segmental veins by using three-dimensional computed tomography bronchography and angiography (3D-CTBA). Materials and methods: A total of 600 patients with ground glass opacity (GGO) who had undergone 3D-CTBA preoperatively at Hebei General Hospital between September 2020 and September 2022 were used for the retrospective study. We reviewed the anatomical variations of RUL veins in these patients using 3D-CTBA images. Results: According to the anatomical position, the peripheral segmental veins structures of RUL were classified into five categories: "Iab type of anterior with central vein" (256/600, 42.7%), "Ib type of anterior with central vein" (166/600, 27.7%), "Central vein type" (38/600, 6.3%), "Anterior vein type" (81/600, 13.5%), "Right top pulmonary vein type" (57/600, 9.5%). The approach for intersegmental vein and intersubsegmental veins identification was divided into five types: anterior approach, posterobronchial approach, central vein approach, V2t approach, and intermediate bronchus posterior surface approach. Conclusions: The classification pattern of peripheral segmental veins should find wide application. Further, approaches identifying intersegmental veins and intersubsegmental veins may help thoracic surgeons perform safe and accurate RUL segmentectomy.

Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology.

The aim of the present study was to develop a non-invasive method based on histological imaging and clinical features for predicting the preoperative status of visceral pleural invasion (VPI) in patients with lung adenocarcinoma (LUAD) located near the pleura. VPI is associated with a worse prognosis of LUAD; therefore, early and accurate detection is critical for effective treatment planning. A total of 112 patients with preoperative computed tomography presentation of adjacent pleura and postoperative pathological findings confirmed as invasive LUAD were retrospectively enrolled. Clinical and histological imaging features were combined to develop a preoperative VPI prediction model and validate the model's efficacy. Finally, a nomogram for predicting LUAD was established and validated using a logistic regression algorithm. Both the clinical signature and radiomics signature (Rad signature) exhibited a perfect fit in the training cohort. The clinical signature was overfitted in the testing cohort, whereas the Rad signature showed a good fit. To combine clinical and radiomics signatures for optimal performance, a nomogram was created using the logistic regression algorithm. The results indicated that this approach had the highest predictive performance, with an area under the curve of 0.957 for the clinical signature and 0.900 for the Rad signature. In conclusion, histological imaging and clinical features can be combined in columnar maps to predict the preoperative VPI status of patients with adjacent pleural infiltrative lung carcinoma.

Prognostic significance of Memorial Sloan-Kettering prognostic score in small-cell lung cancer patients.

Aim: To assess the potential factors with predictive value for survival in small-cell lung cancer (SCLC) patients and to develop a nomogram prediction model. Patients & methods: We retrospectively screened and analyzed patients with pathologically confirmed SCLC from April 2015 to December 2021. Results: A total of 167 patients with SCLC were included. According to the Memorial Sloan-Kettering prognostic score (MPS), patients were divided into three groups: group 0 (n = 65), group 1 (n = 69) and group 2 (n = 33). The multivariate analysis showed that MPS was an independent prognostic factor for progression-free and overall survival in SCLC patients (p < 0.05). The nomogram showed that MPS was the most influential factor for overall survival. Conclusion: MPS is an independent prognostic factor for overall and progression-free survival in SCLC patients and performed better than other indicators used in this study.

Clinical Significance of Preoperative Naples Prognostic Score in Patients With Non-Small Cell Lung Cancer.

Background: Naples Prognostic Score (NPS) is a novel score based on inflammatory-nutritional indicators. We aimed to analyze the prognostic value of the Naples Prognostic Score in non-small cell lung cancer (NSCLC) patients following surgery. Methods: A total of 319 NSCLCpatients following surgery were analyzed in the retrospective cohort study. We analyzed the predictive value of Naples Prognostic Score for overall survival and recurrence-free survival in postoperative non-small cell lung cancer patients by using Kaplan-Meier survival curves and multivariate Cox regression analysis. At the same time, the time-dependent ROC and the area under curves were also created to compare the accuracy of different scoring systems. Results: According to NPS, we divided all patients into 3 groups，120 patients were divided into group 0, 161 patients were divided into group 1, and 38 patients were divided into group 2. The median survival time for all patients is 32 months, and the median survival times for different groups were 35 months, 31 months, and 28 months, respectively. The overall survival and recurrence-free survival survival curves of different groups were significantly different (both P < .05), and patients in the higher NPS groups had a disappointing prognosis. NPS may be an independent prognostic factor for overall survival and recurrence-free survival, according to the results of multivariate analysis (both P < .05). The area under curve showed that the accuracy of the NPS was significantly better than other score systems. Conclusions: The NPS is closely related to the long-term survival prognosis of patients with NSCLC, especially in stage III patients.

Naples Prognostic Score is an Independent Prognostic Factor in Patients with Small Cell Lung Cancer and Nomogram Predictive Model Established.

Background: The routine clinical nutritional and inflammatory indicators such as serum albumin, total cholesterol and lymphocytes have been widely investigated in the prognosis of small cell lung cancer (SCLC). The Naples prognostic score (NPS), based on nutritional and inflammatory status, has been identified as a prognostic impactor in several malignancies. However, the prognostic role of NPS in SCLC has not been elucidated. This study aims to evaluate the prognostic effect of NPS in SCLC patients. Patients and Methods: Patients with SCLC were recruited at Hebei General Hospital between April 2015 and August 2021. Pretreatment clinical and laboratory data were obtained. Participants were assigned into three groups according to NPS (group 0: NPS=0, group 1: NPS=1 or 2, group 2: NPS=3 or 4). Kaplan-Meier and Cox regression analysis were performed to assess the prognostic significance of NPS. The RMS package in R software was used to draw the nomogram predictive model. Results: A total of 128 patients were enrolled. The median progression-free survival (PFS) and overall survival (OS) was 7.2 and 12.3 months, respectively. The median PFS and OS was 12.3 vs 19.8 months, 7.6 vs 14.1 months and 6.0 vs 8.45 months for the three groups respectively. There were significant differences in both OS and FPS among the three groups. Survival analysis showed that NPS was significantly correlated with both OS and PFS (P<0.05). Lower NPS is associated with longer OS and PFS. Multivariate analysis showed that NPS has an independent prognostic impact on OS (P<0.05). The nomogram predictive model showed that NPS has good predictive power for survival rates. Conclusion: NPS is an independent prognostic factor for OS in SCLC patients. Low NPS may predict longer OS. Therefore NPS plays a vital role in the nomogram predictive model of survival rates in SCLC patients.