Since the outbreak of COVID-19 pandemic, a large number of elective or limited operations, including tumor treatment, have been postponed. With the deepening of the understanding of the virus and the change of the prevention policy, the impact of the pandemic is gradually shrinking, and a large number of operations delayed by the pandemic will be rescheduled. However, there is no consensus on the best time to perform surgery for patients infected with SARS-CoV-2, and the consensus on thoracic surgery is more limited. This article reviews the research progress in the timing of surgical operations, especially thoracic surgery, after SARS-CoV-2 infection.
This study reports a case of a 56-year-old female patient with BRAF-mutated non-small cell lung cancer (NSCLC) who successfully underwent curative surgery after neoadjuvant targeted therapy with the BRAF inhibitor dabrafenib combined with the MEK inhibitor trametinib. The chest drainage tube was removed 2 days postoperatively, and the patient was discharged smoothly. Postoperative pathology indicated invasive adenocarcinoma, moderately to highly differentiated, with 80% being lepidic type, and the maximum tumor diameter was 4 cm. No vascular invasion, nerve invasion, air cavity dissemination, pleural invasion, or lymph node metastasis were observed. The postoperative staging was ypT2aN0M0. The patient continued with adjuvant treatment with dabrafenib combined with trametinib postoperatively, and no signs of recurrence were found in the follow-up examination six months after surgery.
ObjectiveTo explore the accuracy of machine learning algorithms based on SHOX2 and RASSF1A methylation levels in predicting early-stage lung adenocarcinoma pathological types. MethodsA retrospective analysis was conducted on formalin-fixed paraffin-embedded (FFPE) specimens from patients who underwent lung tumor resection surgery at Nantong University Affiliated Hospital from January 2021 to January 2023. The methylation levels of SHOX2 and RASSF1A in FFPE specimens were measured using the LungMe kit through methylation-specific PCR (MS-PCR). Using the methylation levels of SHOX2 and RASSF1A as predictive variables, various machine learning algorithms (including logistic regression, XGBoost, random forest, and naive Bayes) were employed to predict different lung adenocarcinoma pathological types, and a web server was constructed for clinical use. ResultsA total of 272 patients were included. Based on the pathological classification of the tumors, patients were divided into three groups: benign tumor/adenocarcinoma in situ (BT/AIS), micro-invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA). The average ages of patients in the BT/AIS, MIA, and IA groups were 57.97, 61.31, and 63.84 years, respectively; the proportions of female patients were 55.38%, 61.11%, and 61.36%, respectively. In the early-stage lung adenocarcinoma prediction model established based on SHOX2 and RASSF1A methylation levels, the random forest and XGBoost models performed well in predicting each pathological type. The C-statistics of the random forest model for the BT/AIS, MIA, and IA groups were 0.70, 0.71, and 0.78, respectively. The C-statistics of the XGBoost model for the BT/AIS, MIA, and IA groups were 0.70, 0.75, and 0.77, respectively. The naive Bayes model only showed robust performance in the IA group, with a C-statistic of 0.73, indicating some predictive ability. The logistic regression model performed the worst among all groups, showing no predictive ability for any group. Through decision curve analysis, the random forest model demonstrated higher net benefit in predicting BT/AIS and MIA pathological types, indicating its potential value in clinical application. Finally, a website for predicting early-stage lung adenocarcinoma pathological types based on the random forest model was developed. ConclusionMachine learning algorithms based on SHOX2 and RASSF1A methylation levels have high accuracy in predicting early-stage lung adenocarcinoma pathological types. The establishment of the pathological type prediction website makes the clinical application of the model more convenient, enhancing the ability of clinicians in making decisions about lung tumor pathological typing.