Extending the value of routine lung screening CT with quantitative body composition assessment

Xu, Kaiwen; Gao, Riqiang; Tang, Yucheng; Deppen, Stephen A.; Sandler, Kim L.; Kammer, Michael N.; Antic, Sanja; Maldonado, Fabien; Huo, Yuankai; Khan, Mirza S.; Landman, Bennett A.

doi:10.1117/12.2611784

Cited by 4 publications

(6 citation statements)

References 37 publications

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“…• Body composition. We applied the multi-level body composition assessment tool developed in [13]. The crosssectional areas of subcutaneous adipose tissue (SAT) and skeletal muscle tissue (SMT) were measured on the axial slices at the fifth, eighth, and 10th vertebral locations.…”

Section: Results Characterizationmentioning

confidence: 99%

“…Current methods for body habitus evaluation using CT images are mainly based on quantitative measurement of certain organs. For example, the body composition profiles were depicted by the measured cross-sectional areas of muscle and adipose tissue on axial CT slices selected at certain landmarks [13]. The thoracic cavity morphology was described by the dimensions of lung regions on given anatomical directions [14].…”

Section: Introductionmentioning

confidence: 99%

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Stratification of lung cancer risk with thoracic imaging phenotypes

Khan

et al. 2023

Medical Imaging 2023: Image Processing

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In lung cancer screening, estimation of future lung cancer risk is usually guided by demographics and smoking status. The role of constitutional profiles of human body, a.k.a. body habitus, is increasingly understood to be important, but has not been integrated into risk models. Chest low dose computed tomography (LDCT) is the standard imaging study in lung cancer screening, with the capability to discriminate differences in body composition and organ arrangement in the thorax. We hypothesize that the primary phenotypes identified using lung screening chest LDCT can form a representation of body habitus and add predictive power for lung cancer risk stratification. In this pilot study, we evaluated the feasibility of body habitus image-based phenotyping on a large lung screening LDCT dataset. A thoracic imaging manifold was estimated based on an intensity-based pairwise (dis)similarity metric for pairs of spatial normalized chest LDCT images. We applied the hierarchical clustering method on this manifold to identify the primary phenotypes. Body habitus features of each identified phenotype were evaluated and associated with future lung cancer risk using time-to-event analysis. We evaluated the method on the baseline LDCT scans of 1,200 male subjects sampled from National Lung Screening Trial. Five primary phenotypes were identified, which were associated with highly distinguishable clinical and body habitus features. Time-to-event analysis against future lung cancer incidences showed two of the five identified phenotypes were associated with elevated future lung cancer risks (HR=1.61, 95% CI = [1.08, 2.38], p=0.019; HR=1.67, 95% CI = [0.98, 2.86], p=0.057). These results indicated that it is feasible to capture the body habitus by image-base phenotyping using lung screening LDCT and the learned body habitus representation can potentially add value for future lung cancer risk stratification.

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Section: Results Characterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stratification of lung cancer risk with thoracic imaging phenotypes

Khan

et al. 2023

Medical Imaging 2023: Image Processing

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“…The examination of skeletal muscles through the analysis of multiple axial images as undertaken in the present study covered diverse muscle groups across the fifth, eight, and 10th vertebral levels, enabling a comprehensive encoding of anatomic information linked to skeletal muscle function (47–49). The utilization of fully automatic AI-based solutions enhances the feasibility of deploying such assessments at population scale (27, 30, 33, 34). Previous investigations based on the NLST data have affirmed the added prognostic value of AI measurements of the muscle attenuation, particularly in mortality risk prediction (27, 30).…”

Section: Discussionmentioning

confidence: 99%

“…The user instructions are documented at https://github.com/MASILab/S-EFOV. The development and evaluation of the algorithm have been described in previous publications (27, 33, 34). An example measurement is presented in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

“…Our primary focus was to evaluate the potential benefits of smoking cessation in decelerating fatty infiltration among individuals with a heavy smoking history. By utilizing the lung screening non-contrast low-dose CT (LDCT) and a previously validated artificial intelligence algorithm (27, 33, 34), we obtained the longitudinal muscle attenuation data for a population-scale lung cancer screening cohort. We hypothesized that, in addition to lowered muscle attenuation, active smoking is associated with a more rapid muscle attenuation decline rate, signifying an accelerated progression of fatty infiltration associated with ongoing smoking.…”

Section: Introductionmentioning

confidence: 99%

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Age-related Muscle Fat Infiltration in Lung Screening Participants: Impact of Smoking Cessation

Xu,

Li,

Terry

et al. 2023

Preprint

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RationaleSkeletal muscle fat infiltration progresses with aging and is worsened among individuals with a history of cigarette smoking. Many negative impacts of smoking on muscles are likely reversible with smoking cessation.ObjectivesTo determine if the progression of skeletal muscle fat infiltration with aging is altered by smoking cessation among lung cancer screening participants.MethodsThis was a secondary analysis based on the National Lung Screening Trial. Skeletal muscle attenuation in Hounsfield unit (HU) was derived from the baseline and follow-up low-dose CT scans using a previously validated artificial intelligence algorithm. Lower attenuation indicates greater fatty infiltration. Linear mixed-effects models were constructed to evaluate the associations between smoking status and the muscle attenuation trajectory.Measurements and Main ResultsOf 19,019 included participants (age: 61 years, 5 [SD]; 11,290 males), 8,971 (47.2%) were actively smoking cigarettes. Accounting for body mass index, pack-years, percent emphysema, and other confounding factors, actively smoking predicted a lower attenuation in both males (β0=-0.88 HU,P<.001) and females (β0=−0.69 HU,P<.001), and an accelerated muscle attenuation decline-rate in males (βl=−0.08 HU/y,P<.05). Age-stratified analyses indicated that the accelerated muscle attenuation decline associated with smoking likely occurred at younger age, especially in females.ConclusionsAmong lung cancer screening participants, active cigarette smoking was associated with greater skeletal muscle fat infiltration in both males and females, and accelerated muscle adipose accumulation rate in males. These findings support the important role of smoking cessation in preserving muscle health.

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Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks

Krishnan,

Xu,

et al. 2024

Medical Physics

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BackgroundThe kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction kernel choice is important for quantitative CT‐based assessment as kernel differences can lead to substantial shifts in measurements unrelated to underlying anatomical structures.PurposeIn this study, we investigate kernel harmonization in a multi‐vendor low‐dose CT lung cancer screening cohort and evaluate our approach's validity in quantitative CT‐based assessments.MethodsUsing the National Lung Screening Trial, we identified CT scan pairs of the same sessions with one reconstructed from a soft tissue kernel and one from a hard kernel. In total, 1000 pairs of five different paired kernel types (200 each) were identified. We adopt the pix2pix architecture to train models for kernel conversion. Each model was trained on 100 pairs and evaluated on 100 withheld pairs. A total of 10 models were implemented. We evaluated the efficacy of kernel conversion based on image similarity metrics including root mean squared error (RMSE), peak signal‐to‐noise ratio (PSNR), and structural similarity index measure (SSIM) as well as the capability of the models to reduce measurement shifts in quantitative emphysema and body composition measurements. Additionally, we study the reproducibility of standard radiomic features for all kernel pairs before and after harmonization.ResultsOur approach effectively converts CT images from one kernel to another in all paired kernel types, as indicated by the reduction in RMSE (p < 0.05) and an increase in the PSNR (p < 0.05) and SSIM (p < 0.05) for both directions of conversion for all pair types. In addition, there is an increase in the agreement for percent emphysema, skeletal muscle area, and subcutaneous adipose tissue (SAT) area for both directions of conversion. Furthermore, radiomic features were reproducible when compared with the ground truth features.ConclusionsKernel conversion using deep learning reduces measurement variation in percent emphysema, muscle area, and SAT area.

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Extending the value of routine lung screening CT with quantitative body composition assessment

Cited by 4 publications

References 37 publications

Stratification of lung cancer risk with thoracic imaging phenotypes

Stratification of lung cancer risk with thoracic imaging phenotypes

Age-related Muscle Fat Infiltration in Lung Screening Participants: Impact of Smoking Cessation

Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks

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