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.

Background: In developing countries where both high rates of smoking and endemic tuberculosis (TB) are often present, identification of early lung cancer can be significantly confounded by the presence of nodules such as those due to latent TB (LTB). It is very challenging to distinguish lung cancer and LTB without invasive procedures, which have their own risks of morbidity and even mortality. Methods: Our method uses a customized VGG16-based 15-layer 2-dimensional deep convolutional neural network (DNN) architecture with transfer learning. The DNN was trained and tested on sets of CT images set extracted from the National Lung Screening Trial and the National Institute of Allergy and Infectious Disease TB Portals. Performance of the DNN was evaluated under locked and step-wise unlocked pretrained weight conditions. Results: The DNN with unlocked pretrained weights achieved an accuracy of 90.4% with an F score of 90.1%. Conclusions: Our findings support the potential for a DNN to serve as a noninvasive screening tool capable of reliably detecting and distinguishing between lung cancer and LTB.

Background: Endobronchial ultrasound-guided transbronchial needle injection of cisplatin (EBUS-TBNI cisplatin) is a therapeutic option for patients with recurrent lung cancer. However, the tumor characteristics that influence the distribution of the agent following intratumoral delivery remain largely unknown. Methods: We performed a retrospective evaluation of EBUS-TBNI cisplatin cases performed at two centers. Semi-automated tumor segmentation from CT scans was performed while blinded to the outcome of response. Twenty-four algorithmic radiomics features from two categories, Morphology (i.e., shape, volume) and Intensity (i.e., density), were extracted, and feature selection performed via least absolute shrinkage and selection operator (LASSO) regression. Models were constructed from clinicoepidemiologic variables and selected radiomics features and evaluated using the likelihood ratio chi-square assessment and Akaike’s information criterion (AIC). Results: Thirty-eight patients with available imaging data were analyzed. Based on RECIST criteria, 27 of 38 treated sites demonstrated complete or partial remission (71%). The top three features identified by LASSO regression were variance, energy, and kurtosis. All three are measures of intensity, a surrogate for tumor density. Two logistic regression models with the outcome of response were created, each with the top 3 categorical features: (I) an Intensity model including variance, energy, and kurtosis, and (II) a Morphology model including surface-to-volume ratio, spherical disproportion, and maximum 3-dimensional (3D) diameter. Only the Intensity model met criteria for significance (P=0.024), and it resulted in a lower AIC and higher pseudo R square value vs. the Morphology model. Conclusions: Measures of tumor density are more highly associated with response to EBUS-TBNI cisplatin than measures of morphology.

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