今泉 和良

BACKGROUND: Patients with thoracic malignancy and interstitial pneumonia (IP) are often excluded from clinical trials, consequently lacking quantitative evidence of poorer prognosis and lower programmed death-ligand 1 (PD-L1) testing rates. METHODS: We evaluated the real-world impact of comorbid IP on biomarker adoption and survival in thoracic malignancy patients receiving first-line systemic therapy at a tertiary teaching hospital between 2016 and 2023. RESULTS: Among 1247 patients, 98 (7.5%) had comorbid IP. Multigene testing rates in IP patients were similar to those in non-IP patients. Only three actionable genomic alterations were found in the IP group, highlighting PD-L1 testing as the key element. PD-L1 testing was underutilized in the IP group (63.3%) compared with the non-IP group (75.1%). Immune checkpoint inhibitor (ICI) therapy was utilized in 12.2% of IP versus 29.3% in non-IP, despite comparable clinical situations. Comorbid IP predicted worse survival (hazard ratio: 1.789; 95% confidence interval: 1.373-2.331; p < 0.001). Although survival significantly improved in non-IP after 2020, no benefit was observed in IP. A multivariable model incorporating an IP × Period interaction confirmed comorbid IP remained a negative prognostic factor, highlighting recent advances have not bridged the survival disparity for this high-risk group. CONCLUSIONS: Despite recent progress, patients with comorbid IP experience limited clinical benefit, characterized by lower rates of PD-L1 testing, restricted use of immune checkpoint inhibitors, and absence of post-2020 survival gains. This large-scale and quantitative evidence demonstrates persistent disparities and their prognostic significance, reflecting the limited applicability of current immunotherapy-based strategies in this high-risk population.

BACKGROUND: We compared the capabilities of quantitatively assessed paired inspiratory-expiratory area-detector computed tomography (ADCT) for pulmonary functional loss and disease severity evaluations between upright and supine ADCT in matched progressive pulmonary fibrosis (PPF) patients. MATERIALS AND METHODS: This retrospective cohort consisted of age-, sex-, and underlying disease-matched patients with PPF who underwent paired inspiratory-expiratory CT on upright ADCT (n = 40) and supine ADCT (n = 40), pulmonary function tests, and disease severity assessment. Based on CT data, the absolute values of the logarithm of the Jacobian determinant and warp-field magnitude of the whole lung and all lobes were calculated. Stepwise regression analyses were performed. RESULTS: On supine ADCT, both indices of the left lower lobe (LLL) were the first and only steps for pulmonary function test results and CT-assessed disease severity (absolute value of the logarithm of the Jacobian determinant: 0.139 ≤ r2 ≤ 0.175, 0.007 ≤ p ≤ 0.018; absolute value of the warp-field magnitude: 0.371 ≤ r2 ≤ 0.447, p < 0.001). However, on upright ADCT, both indices indicated that LLL was the first step and the right lower lobe was the second step for pulmonary function test results and CT-assessed disease severity (0.503 ≤ r2 ≤ 0.674, p < 0.001 or 0.000 < p ≤ 0.006 and 0.474 ≤ r2 ≤ 0.652, 0.002 ≤ p ≤ 0.045, respectively). CONCLUSION: Upright ADCT has equal to or better potential than supine ADCT for detecting pulmonary functional loss and evaluating disease severity when paired inspiratory-expiratory ADCT is applied in PPF patients. RELEVANCE STATEMENT: Upright ADCT has superior potential to supine ADCT for pulmonary functional loss and disease severity evaluations when paired inspiratory-expiratory ADCT is performed in patients with progressive pulmonary fibrosis (PPF). KEY POINTS: Matched progressive pulmonary fibrosis patients compared functional loss and disease severity evaluations between inspiratory-expiratory upright and supine area-detector CT. Clinical parameters demonstrated better correlations with upright than with supine inspiratory-expiratory area-detector CT. Warp-field magnitude showed better correlations with disease severities than the logarithm of the Jacobian determinant on each area-detector CT.

Introduction: Accurate diagnosis of peripheral pulmonary lesions is crucial in respiratory medicine. Radial endobronchial ultrasound (R-EBUS), navigation technologies, and ultrathin bronchoscopes have progressively enhanced distal airway access. Mixed reality (MR) offers a hands-free method for visualizing and manipulating CT-derived three-dimensional (3D) anatomy within the operator’s field of view. This retrospective study aimed to describe the technical feasibility and safety of intraprocedural MR-based holographic virtual bronchoscopy (VB) use. Methods: This study included patients who underwent bronchoscopy for peripheral pulmonary lesions using an MR-based 3D holographic VB system. CT datasets were converted into 3D polygon models and displayed on a HoloLens 2 headset. Operators/assistants intraprocedurally referenced and manipulated the hologram while advancing the bronchoscope. Procedural variables, R-EBUS findings, biopsy techniques, diagnostic yield, and complications were evaluated. Results: Eighteen patients were included. A direct bronchus sign was present in 12 lesions. The median bronchial generation that could be visualized on CT and 3D-VB was six, whereas bronchoscopy enabled advancement to a median of five generations. Radial EBUS demonstrated a within-lesion position in 13 cases. Biopsy techniques included forceps biopsy, cryobiopsy, and TBNA. The overall diagnostic yield was 72.2% (13/18), with malignant disease accounting for the majority of diagnoses. One patient developed mild pneumothorax, which resolved without drainage. Conclusion: MR-based holographic VB enabled real-time, hands-free 3D anatomical referencing without interrupting the procedure. Further prospective studies are warranted to assess procedural benefits and potential integration with other bronchoscopic modalities and devices.

BACKGROUND: Cisplatin-induced nephrotoxicity (CIN) is a major dose-limiting adverse event that can lead to both acute and chronic kidney injury. The formation of thiol-cisplatin conjugates within renal tubular cells has been implicated as a key mechanism underlying CIN. Flopropione is an inhibitor of cysteine conjugate β-lyase 1, an enzyme that catalyzes the formation of the thiol-cisplatin conjugate, which might prevent CIN. OBJECTIVE: We designed a clinical trial to evaluate the safety of flopropione in patients receiving cisplatin-based chemotherapy and explore its efficacy in preventing CIN. METHODS: This is a phase 1 and 2a, single-center, randomized, open-label trial conducted in patients undergoing cisplatin therapy. Participants are randomized in a 5:2 ratio per cohort to receive either flopropione or no treatment. On the day of cisplatin administration, the flopropione group receives oral flopropione twice daily (80 mg in cohort 1, 160 mg in cohort 2, and 240 mg in cohort 3). On the following day, all cohorts receive 3 doses of 80 mg of oral flopropione. A step-up dose escalation design is adopted, progressing from cohort 1 to 3 after confirming safety at each level. The primary end point is the safety of flopropione use in combination with cisplatin; the secondary end points include changes in the levels of urinary biomarkers of nephrotoxicity such as neutrophil gelatinase-associated lipocalin, liver-type fatty acid-binding protein, and kidney injury molecule-1. Blood and urine samples are collected within 48 hours before cisplatin administration and at 24 hours, 48 hours, and 1 week after its initiation for safety and efficacy assessments. RESULTS: The first participant was registered in July 2024. As of January 2026, participant registration is ongoing. The final participant will complete the study by March 2026. Publication of results is expected by March 2027. CONCLUSIONS: This study is expected to contribute to advances in preventive strategies for CIN by providing evidence that inhibition of cysteine conjugate β-lyase 1 by flopropione may attenuate CIN. TRIAL REGISTRATION: Japan Registry of Clinical Trials jRCTs041220021; https://jrct.mhlw.go.jp/en-latest-detail/jRCTs041220021. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/87907.

In lung cytology, screeners and pathologists examine many cells in cytological specimens and describe their corresponding imaging findings. To support this process, our previous study proposed an image-finding generation model based on convolutional neural networks and a transformer architecture. However, further improvements are required to enhance the accuracy of these findings. In this study, we developed a cytology-specific image-finding generation model using a vision transformer and open-source large language models. In the proposed method, a vision transformer pretrained on large-scale image datasets and multiple open-source large language models was introduced and connected through an original projection layer. Experimental validation using 1059 cytological images demonstrated that the proposed model achieved favorable scores on language-based evaluation metrics and good classification performance when cells were classified based on the generated findings. These results indicate that a task-specific model is an effective approach for generating imaging findings in lung cytology.

There is limited data on BRAF V600E-mutant squamous cell carcinoma (SCC). We report three cases of SCC of the lung with a history of resected papillary thyroid carcinoma (PTC), showing p40 positivity, TTF-1 negativity, and PAX8 expression. While treated as lung SCC, metastatic thyroid carcinoma was unconfirmed due to absence of PTC recurrence, clinicopathologic features consistent with primary lung origin, and unavailable archival PTC pathology. BRAF inhibitors yielded only transient responses, and outcomes were poor. These cases underscore the diagnostic and therapeutic value of multigene testing in SCC, highlighting the need to integrate detailed clinical history into precision oncology strategies.

In the diagnosis of lung cancer, imaging findings of lung nodules are essential for benign and malignant classifications. Although numerous studies have investigated the classification of lung nodules, no method has been proposed for obtaining detailed imaging findings. This study aimed to develop a novel method for generating image findings and classifying benign and malignant nodules in chest computed tomography (CT) images using vision–language models. In this study, we collected chest CT images of 77 patients diagnosed with either benign or malignant tumors at Fujita Health University Hospital. For these images, we cropped the regions of interest around the nodules, and a pulmonologist provided the corresponding image findings. We used vision–language models for image captioning to generate image findings. The findings generated by these two models were grammatically correct, with no deviations in notation, as expected from the image findings. Moreover, the descriptions of benign and malignant characteristics were accurately obtained. The bootstrapping language–image pretraining (BLIP) base model achieved an accuracy of 79.2% in classifying nodules, and the bilingual evaluation understudy-4 score for agreement with physician findings was 0.561. These results suggest that the proposed method may be effective for classifying and generating lung nodule findings.

OBJECTIVES: To develop a comprehensive machine learning model incorporating various clinical factors, including frailty and comorbidities, to predict 30-day readmission and mortality risk in patients with chronic obstructive pulmonary disease (COPD). METHODS: This retrospective cohort study used electronic health records (EHR) from Fujita Health University Hospital (2004-2019) for 1294 patients with COPD and 3499 hospitalization or death events. The EHR contained longitudinal patient data (demographics, diagnoses, test results, clinical records). We developed two eXtreme Gradient Boosting models, the comprehensive Top64 and practical 11-feature models. We compared these with the Comorbidity, Obstruction, Dyspnea, and Previous Exacerbations index (CODEX) model, a widely used tool for predicting hospital readmission or death in patients with COPD. The area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI), sensitivity, and specificity were used to evaluate the model performance. RESULTS: The Top64 (AUC: 0.769, 95% CI: 0.747-0.791) and practical 11-feature (AUC: 0.746, 95% CI: 0.730-0.762) models performed better than the CODEX model (AUC: 0.587, 95% CI: 0.563-0.611). The Top64 model showed 0.978 sensitivity and 0.341 specificity, and the practical 11-feature model achieved 0.955 sensitivity and 0.361 specificity. The calibration curves showed good agreement between the observed and predicted results for both models. CONCLUSIONS: A machine learning approach based on clinical data readily available from the EHR performed better than existing models in predicting 30-day readmission and mortality risks in patients with COPD. A comprehensive risk prediction tool may enhance individualized care strategies and improve patient outcomes in COPD management.

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.