Qualia Radiomics

This paper has been published in the Computational and Structural Biotechnology Journal.

Preoperative ¹⁸F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma

Wookjin Choi ^{a d1}, Chia-Ju Liu ^{b 1}, Sadegh Riyahi Alam ^a, Jung Hun Oh ^a, Raj Vaghjiani ^c, John Humm ^a, Wolfgang Weber ^b, Prasad S. Adusumilli ^c, Joseph O. Deasy ^a, Wei Lu ^a

^a Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
^b Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
^c Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
^d Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA 19107, USA

Abstract

Lung adenocarcinoma (ADC) is the most common non-small cell lung cancer. Surgical resection is the primary treatment for early-stage lung ADC while lung-sparing surgery is an alternative for non-aggressive cases. Identifying histopathologic subtypes before surgery helps determine the optimal surgical approach. Predominantly solid or micropapillary (MIP) subtypes are aggressive and associated with a higher likelihood of recurrence and metastasis and lower survival rates. This study aims to non-invasively identify these aggressive subtypes using preoperative ¹⁸F-FDG PET/CT and diagnostic CT radiomics analysis. We retrospectively studied 119 patients with stage I lung ADC and tumors ≤ 2 cm, where 23 had aggressive subtypes (18 solid and 5 MIPs). Out of 214 radiomic features from the PET/CT and CT scans and 14 clinical parameters, 78 significant features (3 CT and 75 PET features) were identified through univariate analysis and hierarchical clustering with minimized feature collinearity. A combination of Support Vector Machine classifier and Least Absolute Shrinkage and Selection Operator built predictive models. Ten iterations of 10-fold cross-validation (10 ×10-fold CV) evaluated the model. A pair of texture feature (PET GLCM Correlation) and shape feature (CT Sphericity) emerged as the best predictor. The radiomics model significantly outperformed the conventional predictor SUV_max (accuracy: 83.5% vs. 74.7%, p = 9e-9) and identified aggressive subtypes by evaluating FDG uptake in the tumor and tumor shape. It also demonstrated a high negative predictive value of 95.6% compared to SUV_max (88.2%, p = 2e-10). The proposed radiomics approach could reduce unnecessary extensive surgeries for non-aggressive subtype patients, improving surgical decision-making for early-stage lung ADC patients.

https://www.sciencedirect.com/science/article/pii/S2001037023004233

2017 AAPM annual meeting

http://www.aapm.org/meetings/2017AM/PRAbs.asp?mid=127&aid=37917