Tumor density is associated with response to endobronchial ultrasound-guided transbronchial needle injection of cisplatin

Correlation of Bronchoscopy and CT in Characterizing Malignant Central Airway Obstruction

BACKGROUND

Malignant Central Airway Obstruction (MCAO) presents a significant challenge in lung cancer management, with notable morbidity and mortality implications. While bronchoscopy is the established diagnostic standard for confirming MCAO and assessing obstruction subtype (intrinsic, extrinsic, mixed) and severity, Computed Tomography (CT) serves as an initial screening tool. However, the extent of agreement between CT and bronchoscopy findings for MCAO remains unclear.

METHODS

To assess the correlation between bronchoscopy and CT, we conducted a retrospective review of 108 patients at Roswell Park Comprehensive Cancer Center, analyzing CT and bronchoscopy results to document MCAO presence, severity, and subtype.

RESULTS

CT correctly identified MCAO in 99% of cases (107/108). Agreement regarding obstruction subtype (80.8%, Cohen's κ = 0.683, p < 0.001), and severity (65%, Quadratic κ = 0.657, p < 0.001) was moderate. CT tended to equally overestimate (7/19) and underestimate (7/19) the degree of obstruction. CT was also poor in identifying mucosal involvement in mixed MCAO.

CONCLUSIONS

CT demonstrates reasonable agreement with bronchoscopy in detecting obstruction. Nevertheless, when CT indicates a positive finding for MCAO, it is advisable to conduct bronchoscopy. This is because CT lacks reliability in determining the severity of obstruction and identifying the mucosal component of mixed disease.

Initial Safety and Feasibility Results From a Phase 1, Diagnose-and-Treat Trial of Neoadjuvant Intratumoral Cisplatin for Stage IV NSCLC

Neoadjuvant intratumoral cisplatin has the potential to generate substantial cytotoxicity and immune priming within the tumor environment, while minimizing systemic, off-target, adverse events. We initiated a phase 1A, 3+3 dose-ranging study of neoadjuvant, intratumoral cisplatin, delivered through endobronchial ultrasound bronchoscopy, in the same procedure as the initial diagnosis. There were no dose-limiting toxicity identified at the 20mg level.

Endobronchial ultrasound-guided transbronchial needle injection of cisplatin results in dynamic changes in the tumor immune microenvironment

RATIONALE

Direct intratumoral delivery of cisplatin via endobronchial ultrasound guided-transbronchial needle injections (EBUS-TBNI) is a novel approach for salvage treatment of advanced stage non-small cell lung cancer (NSCLC). The goal of this study was to evaluate changes in the tumor immune microenvironment during the course of EBUS-TBNI cisplatin therapy.

METHODS

Under an IRB approved protocol, patients with recurrence after radiation therapy who were not receiving other cytotoxic therapy, were prospectively enrolled, and underwent weekly treatments with EBUS-TBNI with additional biopsies obtained for research. Needle aspiration was performed prior to cisplatin delivery at each procedure. Samples were evaluated by flow cytometry for the presence of immune cell types.

RESULTS

Three of the six patients responded to the therapy based on RECIST criteria. Compared to the pre-treatment baseline, intratumoral neutrophils increased in 5 of the 6 patients (p = 0.041), with an average increase of 27.1%, but was not associated with response. A lower pre-treatment CD8+/CD4+ ratio at baseline was associated with response (P = 0.01). Responders demonstrated a lower final proportion of PD-1+ CD8+ T cells compared to non-responders (8.6% vs. 62.3%, respectively, P<0.001. Lower doses of intratumoral cisplatin were associated with subsequent increases in CD8+ T cells within the tumor microenvironment (P = 0.008).

CONCLUSIONS

EBUS-TBNI cisplatin resulted in significant alterations in the tumor immune microenvironment. Further studies are needed to determine if the changes seen here generalize to larger cohorts.

Feature selection methods and predictive models in CT lung cancer radiomics

Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.

A computational modeling approach for dosing endoscopic intratumoral chemotherapy for advanced non-small cell lung cancer

We recently developed a computational model of cisplatin pharmacodynamics in an endobronchial lung tumor following ultrasound-guided transbronchial needle injection (EBUS-TBNI). The model suggests that it is more efficacious to apportion the cisplatin dose between injections at different sites rather than giving it all in a single central injection, but the model was calibrated only on blood cisplatin data from a single patient. Accordingly, we applied a modified version of our original model in a set of 32 patients undergoing EBUS-TBNI for non-small cell lung cancer (NSCLC). We used the model to predict clinical responses and compared them retrospectively to actual patient outcomes. The model correctly predicted the clinical response in 72% of cases, with 80% accuracy for adenocarcinomas and 62.5% accuracy for squamous-cell lung cancer. We also found a power-law relationship between tumor volume and the minimal dose needed to induce a response, with the power-law exponent depending on the number of injections administered. Our results suggest that current injection strategies may be significantly over- or under-dosing the agent depending on tumor size, and that computational modeling can be a useful planning tool for EBUS-TBNI of cisplatin in lung cancer.