Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non-Small-Cell Lung Cancer: A Retrospective Study.

BACKGROUND: Neoadjuvant chemotherapy has variable efficacy in patients with non-small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete response and postneoadjuvant chemotherapy survival in NSCLC. MATERIALS AND METHODS: Retrospective data collection involved 130 patients with NSCLC who underwent neoadjuvant chemotherapy and surgery. Patients were randomly divided into training and independent testing sets. Nine radiomics features from prechemotherapy computed tomography (CT) images were extracted from intratumoral and peritumoral regions. An auto-encoder model was constructed, and its performance was evaluated. X-tile software classified patients into high and low-risk groups based on their predicted probabilities. survival of patients in different risk groups and the role of postoperative adjuvant chemotherapy were examined. RESULTS: The model demonstrated area under the receiver operating characteristic (ROC) curve of 0.874 (training set) and 0.876 (testing set). The larger the area under curve (AUC), the better the model performance. Calibration curve and decision curve analysis indicated excellent model calibration (Hosmer-Lemeshow test, P = .763, the higher the P-value, the better the model fit) and potential clinical applicability. Survival analysis revealed significant differences in overall survival (P = .011) and disease-free survival (P = .017) between different risk groups. Adjuvant chemotherapy significantly improved survival in the low-risk group (P = .041) but not high-risk group (P = 0.56). CONCLUSION: This study represents the first successful prediction of pathological complete response achievement after neoadjuvant chemotherapy for NSCLC, as well as the patients' survival, utilizing intratumoral and peritumoral radiomics features.

A nomogram for predicting liver metastasis in patients with gastric gastrointestinal stromal tumor.

BACKGROUND: Liver metastasis (LIM) is an important factor in the diagnosis, treatment, follow-up, and prognosis of patients with gastric gastrointestinal stromal tumor (GIST). There is no simple tool to assess the risk of LIM in patients with gastric GIST. Our aim was to develop and validate a nomogram to identify patients with gastric GIST at high risk of LIM. METHODS: Patient data diagnosed as having gastric GIST between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training cohort and internal validation cohort in a 7:3 ratio. For external validation, retrospective data collection was performed on patients diagnosed as having gastric GIST at Yunnan Cancer Center (YNCC) between January 2015 and May 2023. Univariate and multivariate logistic regression analyses were used to identify independent risk factors associated with LIM in patients with gastric GIST. An individualized LIM nomogram specific for gastric GIST was formulated based on the multivariate logistic model; its discriminative performance, calibration, and clinical utility were evaluated. RESULTS: In the SEER database, a cohort of 2341 patients with gastric GIST was analyzed, of which 173 cases (7.39%) were found to have LIM; 239 patients with gastric GIST from the YNCC database were included, of which 25 (10.46%) had LIM. Multivariate analysis showed tumor size, tumor site, and sex were independent risk factors for LIM (P < .05). The nomogram based on the basic clinical characteristics of tumor size, tumor site, sex, and age demonstrated significant discrimination, with an area under the curve of 0.753 (95% CI, 0.692-0.814) and 0.836 (95% CI, 0.743-0.930) in the internal and external validation cohort, respectively. The Hosmer-Lemeshow test showed that the nomogram was well calibrated, whereas the decision curve analysis and the clinical impact plot demonstrated its clinical utility. CONCLUSION: Tumor size, tumor subsite, and sex were significantly correlated with the risk of LIM in gastric GIST. The nomogram for patients with GIST can effectively predict the individualized risk of LIM and contribute to the planning and decision making related to metastasis management in clinical practice.

Effect of initial recurrence site on the prognosis of different tissue types of non-small cell lung cancer: a retrospective cohort study.

PURPOSE: To explore the correlation between the initial recurrence site and survival after recurrence (PRS) in non-small cell lung cancer (NSCLC). METHODS: We collected 588 stages I-III NSCLC patients with recurrence after radical resection in Yunnan Cancer Hospital from January 2013 to December 2018. We used Kaplan-Meier survival curves to compare PRS in patients with different site recurrences. The univariate and multivariate Cox proportional hazard models were used to analyze the impact of the initial recurrence site on PRS. RESULTS: The recurrence site included the lung (n = 109), brain (n = 113), bone (n = 79), abdomen (n = 28), pleura (n = 24), lymph node (n = 81), and multisite (n = 154). In the total population, patients with multisite recurrence had substantially worse PRS (24.8 months, 95% confidence interval [CI]: 17.46-32.20) than that of patients without multiple sites recurrence (42.2 months, 95% CI 32.24-52.10) (P = 0.026). However, patients with lung recurrence had better RFS (63.1 months, 95% CI 51.13-74.00) than those who did not (31.0 months, 95% CI 25.10-36.96) (P < 0.001). In adenocarcinoma, patients with pleural recurrence had substantially worse PRS (21.3 months, 95% CI 15.07-27.46) than that of patients without pleural recurrence (46.9 months, 95% CI 35.07-58.80) (P = 0.031). Multivariate Cox proportional hazards regression analysis revealed that lung recurrence (HR 0.58, 95% CI 0.40-0.82; P = 0.003) was independent protective prognostic factor for PRS in the total population, while pleural recurrence (HR 2.18, 95% CI 1.14-4.17; P = 0.018) was independent adverse prognostic factors for PRS in adenocarcinoma patients. CONCLUSION: The initial recurrence site was associated with PRS in NSCLC patients. Identification of recurrence sites could guide the subsequent treatment.