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

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.

Informations de copyright

2020 Journal of Thoracic Disease. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/jtd-20-674). CMK reports grants from NIH, during the conduct of the study; personal fees from Olympus America, grants and personal fees from Johnson and Johnson, personal fees from Boston Scientific, personal fees from Gala Therapeutics, outside the submitted work. In addition, CMK has a patent Methods for Computational Modeling to Guide Intratumoral Therapy pending and Equity holder of company, Quantitative Imaging Solutions, the performs image analysis work in lung cancer. RSJE reports grants from NHLBI, personal fees from Chiesi, personal fees from LeukoLabs, grants and personal fees from Boehringer Ingelheim, personal fees from Eolo Medical, outside the submitted work; and he is also a founder and co-owner of Quantitative Imaging Solutions which is a company that provides image based consulting and develops software to enable data sharing. Dr. JHTB reports personal fees from Johnson and Johnson, outside the submitted work. In addition, Dr. JHTB has a patent Methods for computational modeling to guide intratumoral therapy (U.S. Patent Application No. 62/542,623. Filed: August 8, 2017 pending). GW reports grants from NIH, grants and other from Boehringer Ingelheim, other from Quantitative Imaging Solutions, other from PulmonX, grants from BTG Interventional Medicine, grants and other from Janssen Pharmaceuticals, other from GlaxoSmithKline, other from Novartis, other from Vertex, outside the submitted work; and Dr. GW’s spouse works for Biogen. The other authors have no conflicts of interest to declare.