CT-derived body composition associated with lung cancer recurrence after surgery

Gezer, Naciye Sinem; Bandos, Andriy I.; Beeche, Cameron A.; Leader, Joseph K.; Dhupar, Rajeev; Pu, Jiantao

doi:10.1016/j.lungcan.2023.107189

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…Based on the L3 vertebral body level, there were at least three phenotypes of physical compositions, containing visceral fat area (VFA), subcutaneous fat area (SFA), together with skeletal muscle index (SMI) are associated with the prognosis of NSCLC patients. [164][165][166] Based on the above research, some scholars 167…”

Section: Survival and Prognosismentioning

confidence: 99%

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Yuan,

An,

Zhu

et al. 2024

CMAR

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As a disease with high morbidity and high mortality, lung cancer has seriously harmed people’s health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

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Section: Survival and Prognosismentioning

confidence: 99%

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Yuan,

An,

Zhu

et al. 2024

CMAR

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“…Research has indicated that visceral and subcutaneous fat have distinct functions and play diverse roles in cancer occurrence and progression, sometimes even exerting opposing effects (Lee et al 2018 ; Buckley et al 2022 ). Similarly, the prognostic value of visceral and subcutaneous fat for postoperative survival in lung cancer patients remains a subject of debate (Lee et al 2022a ; Gezer et al 2023 ), and the interplay among obesity markers in the prognosis of NSCLC remains unclear.…”

Section: Introductionmentioning

confidence: 99%

Influence of abdominal fat distribution and inflammatory status on post-operative prognosis in non-small cell lung cancer patients: a retrospective cohort study

Ma,

Luo,

Liu

et al. 2024

J Cancer Res Clin Oncol

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Purpose To evaluate the influence of visceral fat area (VFA), subcutaneous fat area (SFA), the systemic immune-inflammation index (SII) and total inflammation-based systemic index (AISI) on the postoperative prognosis of non-small cell lung cancers (NSCLC) patients. Methods 266 NSCLC patients received surgery from two academic medical centers were included. To assess the effect of abdominal fat measured by computed tomography (CT) imaging and inflammatory indicators on patients’ overall survival (OS) and progression-free survival (PFS), Kaplan–Meier survival analysis and Cox proportional hazards models were used. Results Kaplan–Meier analysis showed the OS and PFS of patients in high-VFA group was better than low-VFA group (p < 0.05). AISI and SII were shown to be risk factors for OS and PFS (p < 0.05) after additional adjustment for BMI (Cox regression model II). After further adjustment for VFA (Cox regression model III), low-SFA group had longer OS (p < 0.05). Among the four subgroups based on VFA (high/low) and SFA (high/low) (p < 0.05), the high-VFA & low-SFA group had the longest median OS (108 months; 95% CI 74–117 months) and PFS (85 months; 95% CI 65–117 months), as well as the lowest SII and AISI (p < 0.05). Low-SFA was a protective factor for OS with different VFA stratification (p < 0.05). Conclusion VFA, SFA, SII and AISI may be employed as significant prognostic markers of postoperative survival in NSCLC patients. Moreover, excessive SFA levels may encourage systemic inflammation decreasing the protective impact of VFA, which may help to provide targeted nutritional support and interventions for postoperative NSCLC patients with poor prognosis.

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