Machine learning-powered prediction of recurrence in patients with non-small cell lung cancer using quantitative clinical and radiomic biomarkers

Moon, Sehwa; Choi, Dahim; Lee, Ji Yeon; Kim, Myoung Hee; Hong, Helen; Kim, Bong Seog; Choi, Jang Hwan

doi:10.1117/12.2549962

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The available studies have shown that several factors are linked to postoperative lung cancer recurrence, including patient-related factors such as smoking, age, or gender [ 11 , 12 , 13 , 14 ]; perioperative factors such as surgical trauma, transfusion, hypothermia, and anesthesia [ 15 , 16 , 17 ]; systemic inflammation [ 18 ]; molecular biomarkers [ 19 , 20 , 21 ]; and image features like tumor and body composition features [ 22 , 23 , 24 ]. While most of these studies only focused on a limited number of variables, the complex pathophysiology of NSCLC means that many variables may interact with each other with regard to contributing to recurrence.…”

Section: Introductionmentioning

confidence: 99%

A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer

Iyer

Ren

et al. 2023

Cancers

View full text Add to dashboard Cite

The accurate identification of the preoperative factors impacting postoperative cancer recurrence is crucial for optimizing neoadjuvant and adjuvant therapies and guiding follow-up treatment plans. We modeled the causal relationship between radiographical features derived from CT scans and the clinicopathologic factors associated with postoperative lung cancer recurrence and recurrence-free survival. A retrospective cohort of 363 non-small-cell lung cancer (NSCLC) patients who underwent lung resections with a minimum 5-year follow-up was analyzed. Body composition tissues and tumor features were quantified based on preoperative whole-body CT scans (acquired as a component of PET-CT scans) and chest CT scans, respectively. A novel causal graphical model was used to visualize the causal relationship between these factors. Variables were assessed using the intervention do-calculus adjustment (IDA) score. Direct predictors for recurrence-free survival included smoking history, T-stage, height, and intramuscular fat mass. Subcutaneous fat mass, visceral fat volume, and bone mass exerted the greatest influence on the model. For recurrence, the most significant variables were visceral fat volume, subcutaneous fat volume, and bone mass. Pathologic variables contributed to the recurrence model, with bone mass, TNM stage, and weight being the most important. Body composition, particularly adipose tissue distribution, significantly and causally impacted both recurrence and recurrence-free survival through interconnected relationships with other variables.

show abstract