A Real-World Assessment of Stage I Lung Cancer Through Electronic Nose Technology.

INTRODUCTION: Electronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer. METHODS: This phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2-0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models. RESULTS: Based on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001). CONCLUSIONS: In the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a "multiomics" platform.

Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study.

Objective: The timing and nature of surgical intervention for semisolid abnormalities are dependent upon distinguishing between adenocarcinoma-in-situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (INV). We sought to develop and evaluate a quantitative imaging method to determine invasiveness of small, ground-glass lesions on computed tomography (CT) chest scans. Methods: The study comprised 268 patients from 4 institutions with resected (<=3 cm) semisolid lesions with confirmed histopathological diagnosis of MIA/AIS or INV. A total of 248 radiomic texture features from within the tumor nodule (intratumoral) and adjacent to the nodule (peritumoral) were extracted from manually annotated lung nodules of chest CT scans. The datasets were randomly divided, with 40% of patients used for training and 60% used for testing the machine classifier (Training DTrain, N=106; Testing, DTest, N=162). Results: The top five radiomic stable features included four intratumoral (Laws and Haralick feature families) and one peritumoral feature within 3 to 6 mm of the nodule (CoLlAGe feature family), which successfully differentiated INV from MIA/AIS nodules with an AUC of 0.917 [0.867-0.967] on DTrain and 0.863 [0.79-0.931] on DTest. The radiomics model successfully differentiated INV from MIA cases (<1 cm AUC: 0.76 [0.53-0.98], 1-2 cm AUC: 0.92 [0.85-0.98], 2-3 cm AUC: 0.95 [0.88-1]). The final integrated model combining the classifier with the radiologists' score gave the best AUC on DTest (AUC=0.909, p<0.001). Conclusions: Addition of advanced image analysis via radiomics to the routine visual assessment of CT scans help better differentiate adenocarcinoma subtypes and can aid in clinical decision making. Further prospective validation in this direction is warranted.

Long-term assessment of efficacy with a novel Thoracic Survivorship Program for patients with lung cancer.

OBJECTIVE: We developed a novel, nurse practitioner-run Thoracic Survivorship Program to aid in long-term follow-up. Patients with non-small cell lung cancer who were disease-free at least 1 year after resection could be referred to the Thoracic Survivorship Program by their surgeon. Our objectives were to summarize follow-up compliance and assess long-term outcomes between Thoracic Survivorship Program enrollment and non-Thoracic Survivorship Program. METHODS: Patients who underwent R0 resection for stages I to IIIA between 2006 and 2016 were stratified by enrollment in Thoracic Survivorship Program versus surgeon only follow-up (non-Thoracic Survivorship Program). Follow-up included 6-month chest computed tomography scans for 2 years and then annually. Lack of follow-up compliance was defined by 2 or more consecutive delayed annual computed tomography scans/visits ± 90 days. Relationships between Thoracic Survivorship Program and second primary non--small cell lung cancers, extrathoracic cancers, and survival were quantified using multivariable Cox proportional hazards regression with time-varying covariate reflecting timing of enrollment. RESULTS: A total of 1162 of 3940 patients (29.5%) were enrolled in the Thoracic Survivorship Program. The median time to enrollment was 2.3 years; 3279 of 3940 (83%) had complete computed tomography scan data, and 60 of 3279 (1.8%) had 2 or more delayed scans; 323 of 9082 (3.6%) non-Thoracic Survivorship Program visits were noncompliant versus 132 of 4823 (2.7%) of Thoracic Survivorship Program visits (P = .009); 136 of 1146 Thoracic Survivorship Program patients developed second primary non-small cell lung cancer, and 69 of 1123 developed extrathoracic cancer, whereas 322 of 2794 of non-Thoracic Survivorship Program patients developed second primary non-small cell lung cancer and 225 of 2817 patients developed extrathoracic cancer. In multivariable analyses, Thoracic Survivorship Program enrollment was associated with improved disease-free survival (hazard ratio, 0.57; 95% confidence interval, 0.48-0.67; P < .001). CONCLUSIONS: Our novel nurse practitioner-run Thoracic Survivorship Program is associated with high patient compliance and outcomes not different from those seen with physician-based follow-up. These results have important implications for health care resource allocation and costs.

External Validation of Surgical Risk Preoperative Assessment System in Pulmonary Resection.

BACKGROUND: Accurate preoperative risk assessment is necessary for informed decision making for patients and surgeons. Several preoperative risk calculators are available but few have been examined in the general thoracic surgical patient population. The Surgical Risk Preoperative Assessment System (SURPAS), a risk-assessment tool applicable to a wide spectrum of surgical procedures, was developed to predict the risks of common adverse postoperative outcomes using a parsimonious set of preoperative input variables. We sought to externally validate the performance of SURPAS for postoperative complications in patients undergoing pulmonary resection. METHODS: Between January 2016 and December 2018, 2514 patients underwent pulmonary resection at our center. Using data from our institution's prospectively maintained database, we calculated the predicted risks of 12 categories of postoperative outcomes using the latest version of SURPAS. Performance of SURPAS against observed patient outcomes was assessed by discrimination (concordance index) and calibration (calibration curves). RESULTS: The discrimination ability of SURPAS was moderate across all outcomes (concordance indices, 0.640 to 0.788). Calibration curves indicated good calibration for all outcomes except infectious and cardiac complications, discharge to a location other than home, and mortality (all overestimated by SURPAS). CONCLUSIONS: SURPAS demonstrates outcomes for pulmonary resections with reasonable predictive ability. Discretion should be applied when assessing risk for postoperative infectious and cardiac complications, discharge to a location other than home, and mortality. Although the parsimonious nature of SURPAS is one of its strengths, its performance might be improved by including additional factors known to influence outcomes after pulmonary resection, such as sex and pulmonary function.

New strategies in pleural mesothelioma: BAP1 and NF2 as novel targets for therapeutic development and risk assessment.

Malignant pleural mesothelioma (MPM) is a highly lethal cancer with limited therapeutic options. Recent work has focused on the frequent somatic inactivation of two tumor suppressor genes in MPM-NF2 (Neurofibromatosis type 2) and the recently identified BAP1 (BRCA associated protein 1). In addition, germline mutations in BAP1 have been identified that define a new familial cancer syndrome, which includes MPM, ocular melanoma, and other cancers. These recent advances may allow screening of high-risk individuals and the development of new therapies that target key pathways in MPM.