Prognostic implications of body composition change during primary treatment in patients with ovarian cancer: A retrospective study using an artificial intelligence-based volumetric technique

Yoon, Soonho; Kim, Taek Min; Cho, Jeong Yeon; Chung, Hyun Hoon; Song, Yong Sang

doi:10.1016/j.ygyno.2021.05.004

Cited by 11 publications

(7 citation statements)

References 28 publications

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“…Additionally, sarcopenic patients may tolerate surgery/chemoradiotherapy poorly, leading to toxicity or early discontinuation of therapy, and thus accelerated progression and death. Our results are in line with recently published studies this year, which show that muscle CSA at the L3 vertebral level on CT imaging, as assessed by a CNN, was significantly associated with survival in advanced cancer, and that greater muscle loss, as assessed by an AI-based volumetric technique, was a poor prognostic factor for OS [46,47].…”

Section: Discussionsupporting

confidence: 92%

Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma

Mauricaite

Pakzad-Shahabi

et al. 2021

Br J Cancer

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Background Glioblastoma is the commonest malignant brain tumour. Sarcopenia is associated with worse cancer survival, but manually quantifying muscle on imaging is time-consuming. We present a deep learning-based system for quantification of temporalis muscle, a surrogate for skeletal muscle mass, and assess its prognostic value in glioblastoma. Methods A neural network for temporalis segmentation was trained with 366 MRI head images from 132 patients from 4 different glioblastoma data sets and used to quantify muscle cross-sectional area (CSA). Association between temporalis CSA and survival was determined in 96 glioblastoma patients from internal and external data sets. Results The model achieved high segmentation accuracy (Dice coefficient 0.893). Median age was 55 and 58 years and 75.6 and 64.7% were males in the in-house and TCGA-GBM data sets, respectively. CSA was an independently significant predictor for survival in both the in-house and TCGA-GBM data sets (HR 0.464, 95% CI 0.218–0.988, p = 0.046; HR 0.466, 95% CI 0.235–0.925, p = 0.029, respectively). Conclusions Temporalis CSA is a prognostic marker in patients with glioblastoma, rapidly and accurately assessable with deep learning. We are the first to show that a head/neck muscle-derived sarcopenia metric generated using deep learning is associated with oncological outcomes and one of the first to show deep learning-based muscle quantification has prognostic value in cancer.

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Section: Discussionsupporting

confidence: 92%

Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma

Mauricaite

Pakzad-Shahabi

et al. 2021

Br J Cancer

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“…Most studies (82.5%) reported body mass index (BMI) using categories recommended by the World Health Organization [ 47 ], with a BMI < 18.5 kg/m 2 classified as underweight; 18.5–24.9 kg/m 2 as normal weight; 25.0–29.9 kg/m 2 as overweight; and ≥30.0 kg/m 2 as obese. The remaining studies [ 10 , 24 , 44 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ] used various BMI categories recommended for Asian or Western Pacific populations. A total of 25 studies investigated measures of muscle mass, muscle density, and/or fat mass using computed tomography (CT) scans routinely conducted for diagnostic or surveillance purposes.…”

Section: Resultsmentioning

confidence: 99%

Association between Energy Balance-Related Factors and Clinical Outcomes in Patients with Ovarian Cancer: A Systematic Review and Meta-Analysis

Stelten

Schofield

Hartman

et al. 2022

Cancers

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Background: This systematic review and meta-analysis synthesized evidence in patients with ovarian cancer at diagnosis and/or during first-line treatment on; (i) the association of body weight, body composition, diet, exercise, sedentary behavior, or physical fitness with clinical outcomes; and (ii) the effect of exercise and/or dietary interventions. Methods: Risk of bias assessments and best-evidence syntheses were completed. Meta-analyses were performed when ≥3 papers presented point estimates and variability measures of associations or effects. Results: Body mass index (BMI) at diagnosis was not significantly associated with survival. Although the following trends were not supported by the best-evidence syntheses, the meta-analyses revealed that a higher BMI was associated with a higher risk of post-surgical complications (n = 5, HR: 1.63, 95% CI: 1.06–2.51, p = 0.030), a higher muscle mass was associated with a better progression-free survival (n = 3, HR: 1.41, 95% CI: 1.04–1.91, p = 0.030) and a higher muscle density was associated with a better overall survival (n = 3, HR: 2.12, 95% CI: 1.62–2.79, p < 0.001). Muscle measures were not significantly associated with surgical or chemotherapy-related outcomes. Conclusions: The prognostic value of baseline BMI for clinical outcomes is limited, but muscle mass and density may have more prognostic potential. High-quality studies with comprehensive reporting of results are required to improve our understanding of the prognostic value of body composition measures for clinical outcomes. Systematic review registration number: PROSPERO identifier CRD42020163058.

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“…Imaging analysis methods for this study were the same as our previous study on patients with epithelial ovarian cancer (23), including the use of the same commercially available, artificial intelligence-based software (DEEPCATCH v1.0.0.0; MEDICALIP Co. Ltd., Seoul, Korea). In brief, we used this deep neural network-based software for automatic volumetric segmentation of body composition (skeletal muscle, abdominal visceral fat, and subcutaneous fat) from anonymized, precontrast CT images in DICOM format.…”

Section: Imaging Analysismentioning

confidence: 99%

“…In addition, the latest high-throughput technology allows automated and fast volumetric measurements of each component from CT scans (21,22). With the use of such an advanced tool, tracking the volumetric change of specific body composition components is feasible (23), which has not yet been investigated in early cervical cancer.…”

Section: Introductionmentioning

confidence: 99%

Impact of Computed Tomography-Based, Artificial Intelligence-Driven Volumetric Sarcopenia on Survival Outcomes in Early Cervical Cancer

et al. 2021

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The purpose of this study was to investigate the impact of sarcopenia and body composition change during primary treatment on survival outcomes in patients with early cervical cancer. We retrospectively identified patients diagnosed with 2009 International Federation of Gynecology and Obstetrics stage IB1-IIA2 cervical cancer who underwent primary radical hysterectomy between 2007 and 2019. From pre-treatment CT scans (n = 306), the skeletal muscle area at the third lumbar vertebra (L3) and the waist skeletal muscle volume were measured using an artificial intelligence-based tool. These values were converted to the L3 and volumetric skeletal muscle indices by normalization. We defined L3 and volumetric sarcopenia using 39.0 cm2/m2 and the first quartile (Q1) value, respectively. From pre- and post-treatment CT scan images (n = 192), changes (%) in waist skeletal muscle and fat volumes were assessed. With the use of Cox regression models, factors associated with progression-free survival (PFS) and overall survival (OS) were analyzed. Between the L3 sarcopenia and non-sarcopenia groups, no differences in PFS and OS were observed. In contrast, volumetric sarcopenia was identified as a poor prognostic factor for PFS (adjusted hazard ratio [aHR], 1.874; 95% confidence interval [CI], 1.028–3.416; p = 0.040) and OS (aHR, 3.001; 95% CI, 1.016–8.869; p = 0.047). During primary treatment, significant decreases in waist skeletal muscle (median, −3.9%; p < 0.001) and total fat (median, −5.3%; p < 0.001) were observed. Of the two components, multivariate analysis revealed that the waist fat gain was associated with worse PFS (aHR, 2.007; 95% CI, 1.009–3.993; p = 0.047). The coexistence of baseline volumetric sarcopenia and waist fat gain further deteriorated PFS (aHR, 2.853; 95% CI, 1.257–6.474; p = 0.012). In conclusion, baseline volumetric sarcopenia might be associated with poor survival outcomes in patients with early cervical cancer undergoing primary RH. Furthermore, sarcopenia patients who gained waist fat during primary treatment were at a high risk of disease recurrence.

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Prognostic implications of body composition change during primary treatment in patients with ovarian cancer: A retrospective study using an artificial intelligence-based volumetric technique

Cited by 11 publications

References 28 publications

Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma

Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma

Association between Energy Balance-Related Factors and Clinical Outcomes in Patients with Ovarian Cancer: A Systematic Review and Meta-Analysis

Impact of Computed Tomography-Based, Artificial Intelligence-Driven Volumetric Sarcopenia on Survival Outcomes in Early Cervical Cancer

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