Naozumi Hashimoto

Bronchoscopic lung volume reduction (BLVR) with endobronchial valves is an established treatment for selected patients with advanced emphysema. A 74-year-old male patient with chronic obstructive pulmonary disease and severe dyspnea was scheduled to undergo BLVR targeting the right middle lobe bronchus based on high-resolution CT findings, which showed severe emphysematous changes with hyperinflation and fissure completeness of 98% in the right middle lobe. The physician conducted preoperative virtual reality (VR)-assisted planning using the patient's imaging data, enabling comprehensive visualisation of the bronchial tree, airway measurements, and procedural simulation. The Chartis system confirmed a 'no flow' pattern, supporting the absence of collateral ventilation. During the procedure, a size 5.5 valve was placed in the right B4/5 bronchus following VR and intraoperative assessments. The patient remained stable postoperatively without complications. VR enhanced procedural planning by improving airway assessment, optimising valve sizing, and reducing cognitive load, leading to increased efficiency and operator confidence. Further research is warranted to validate the utility of VR in bronchoscopic interventions.

The benefit of programmed cell death protein-1 (PD-1)/programmed cell death protein ligand-1 (PD-L1) inhibitors remains unclear in non-small cell lung cancer (NSCLC) patients with poor performance status (PS). In the current multi-centre retrospective cohort study, advanced or recurrent NSCLC patients treated with PD-1/PD-L1 inhibitors were enrolled. Of the 219 patients enrolled, 44 had PS 2-4. The objective response rate (ORR) of patients with PS 2-4 in 1st line was 33%. Among 1st line group, median progression-free survival (PFS) in patients with PS 2 was significantly longer compared to that in patients with PS 3-4 (15.3 months vs. 0.9 months, P = 0.039, Log-rank test). Among previously treated patients, the ORR of patients with PS 2-4 was only 4%, and PFS and overall survival was poor even in patients with PS 2. PD-1/PD-L1 inhibitors can be an option for PS 2 NSCLC patients in 1st line setting.

BACKGROUND: The increasing prevalence of lung cancer in the elderly population necessitates a closer evaluation of diagnostic and therapeutic approaches. This study aimed to compare the safety and diagnostic efficacy of transbronchial lung cryobiopsy (TBLC) between patients ≥ 80 years and younger patients. METHODS: A retrospective review was conducted of 96 patients diagnosed with peripheral lung cancer who underwent TBLC between April 2021 and October 2023. The patients were categorized into two groups: the elderly group (age ≥ 80 years, n = 20) and younger group (age < 80 years; n = 76). Data regarding the biopsy yield, complications, and feasibility of molecular analyses were collected and analyzed. RESULTS: The diagnostic yield of TBLC was comparable between the elderly and younger groups (95% vs. 89.5%, p = 0.679). Biomarker testing, including programmed death-ligand 1 expression and genetic mutations, were feasible in all cases diagnosed with cancer using TBLC samples. No significant differences were observed in major complications such as pneumothorax or bleeding. CONCLUSIONS: TBLC was found to be a safe and effective diagnostic tool for peripheral lung cancer in elderly patients and provided adequate samples for molecular testing. Since the complication rates did not significantly differ between the two age groups, age alone should not be considered a contraindication for the procedure.

BACKGROUND: Multiple first-line treatment options have been developed for advanced non-small cell lung cancer (NSCLC) in each subgroup determined by predictive biomarkers, specifically driver oncogene and programmed cell death ligand-1 (PD-L1) status. However, the methodology for optimal treatment selection in individual patients is not established. This study aimed to develop artificial intelligence (AI)-based personalized survival prediction model according to treatment selection. METHODS: The prediction model was built based on random survival forest (RSF) algorithm using patient characteristics, anticancer treatment histories, and radiomics features of the primary tumor. The predictive accuracy was validated with external test data and compared with that of cox proportional hazard (CPH) model. RESULTS: A total of 459 patients (training, n = 299; test, n = 160) with advanced NSCLC were enrolled. The algorithm identified following features as significant factors associated with survival: age, sex, performance status, Brinkman index, comorbidity of chronic obstructive pulmonary disease, histology, stage, driver oncogene status, tumor PD-L1 expression, administered anticancer agent, six markers of blood test (sodium, lactate dehydrogenase, etc.), and three radiomics features associated with tumor texture, volume, and shape. The C-index of RSF model for test data was 0.841, which was higher than that of CPH model (0.775, P < 0.001). Furthermore, the RSF model enabled to identify poor survivor treated with pembrolizumab because of tumor PD-L1 high expression and those treated with driver oncogene targeted therapy according to driver oncogene status. CONCLUSIONS: The proposed AI-based algorithm accurately predicted the survival of each patient with advanced NSCLC. The AI-based methodology will contribute to personalized medicine. TRIAL REGISTRATION: The trial design was retrospectively registered study performed in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Nagoya University Graduate School of Medicine (approval: 2020 - 0287).