Nomogram of Combining CT-Based Body Composition Analyses and Prognostic Inflammation Score: Prediction of Survival in Advanced Epithelial Ovarian Cancer Patients

Wang, Xin; Zhang, Chao; Cao, Feng; Wang, Chuan-bin; Dong, Jianying; Wang, Zhen-huan

doi:10.1016/j.acra.2021.11.011

Cited by 9 publications

(4 citation statements)

References 35 publications

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“…BMD, assessed before treatment, is independent prognostic factors for OS in patients with advanced cholangiocellular adenocarcinoma ( 46 ). The loss of BMD has been linked to shorter overall survival in AEOC patients undergoing primary debulking surgery and adjuvant chemotherapy, corroborating our study findings ( 20 ).…”

Section: Discussionsupporting

confidence: 90%

“…Emerging evidence also suggests that sarcopenic obesity, characterized by severe obesity and low skeletal muscle area (SMA), might be a predictor of cancer ( 18 ). Many observational studies have shown that sarcopenic obesity as the biomarker predicts a poor OS in cancer patients ( 19 ), as well as the loss of body mineral density (BMD) ( 20 ). Furthermore, research has focused on the body composition as a predictor of response and toxicity to cancer immune checkpoint inhibitors ( 21 ).…”

Section: Introductionmentioning

confidence: 99%

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Body composition and inflammation variables as the potential prognostic factors in epithelial ovarian cancer treated with Olaparib

Guo,

Tang,

et al. 2024

Front. Oncol.

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BackgroundEpithelial ovarian cancer (EOC) is a significant cause of mortality among gynecological cancers. While Olaparib, a PARP inhibitor, has demonstrated efficacy in EOC maintenance therapy, individual responses vary. This study aims to assess the prognostic significance of body composition and systemic inflammation markers in EOC patients undergoing initial Olaparib treatment.MethodsA retrospective analysis was conducted on 133 EOC patients initiating Olaparib therapy. Progression-free survival (PFS) was assessed through Kaplan-Meier analysis and Cox proportional hazards regression. Pre-treatment computed tomography images were utilized to evaluate body composition parameters including subcutaneous adipose tissue index (SATI), visceral adipose tissue index (VATI), skeletal muscle area index (SMI), and body mineral density (BMD). Inflammatory markers, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), serum albumin, and hemoglobin levels, were also measured.ResultsThe median follow-up duration was 16 months (range: 5-49 months). Survival analysis indicated that high SATI, high VATI, high SMI, high BMD, low NLR, and low PLR were associated with decreased risk of disease progression (all p < 0.05). Multivariate analysis identified several factors independently associated with poor PFS, including second or further lines of therapy (HR = 2.16; 95% CI = 1.09-4.27, p = 0.027), low VATI (HR = 3.79; 95% CI = 1.48-9.70, p = 0.005), low SMI (HR = 2.52; 95% CI = 1.11-5.72, p = 0.027), low BMD (HR = 2.36; 95% CI = 1.22-4.54, p = 0.010), and high NLR (HR = 0.31; 95% CI = 0.14-0.69, p = 0.004). Subgroup analysis in serous adenocarcinoma patients revealed distinct prognostic capabilities of SATI, VATI, SMI, PLR, and NLRConclusionBody composition and inflammation variables hold promise as predictors of therapeutic response to Olaparib in EOC patients. Understanding their prognostic significance could facilitate tailored treatment strategies, potentially improving patient outcomes.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Body composition and inflammation variables as the potential prognostic factors in epithelial ovarian cancer treated with Olaparib

Guo,

Tang,

et al. 2024

Front. Oncol.

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“…They assessed sarcopenia using skeletal muscle index (SMI) that has been measured at the level of L3 in the preoperative CT scan [ 13 ]. In addition, a recent study on patients undergoing appendectomy reported that psoas’ CT attenuation was an independent protective factor for IH, yet PMI and sarcopenia did not show a significant effect on hernia development [ 26 ]. Only a few other studies have evaluated the role of sarcopenia in patients undergoing hernia repair.…”

Section: Discussionmentioning

confidence: 99%

Sarcopenia and body fat change as risk factors for radiologic incisional hernia following robotic nephrectomy

et al. 2023

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Objective To assess the effect of body muscle and fat metrics on the development of radiologic incisional hernia (IH) following robotic nephrectomy. Materials and Methods We retrospectively reviewed the records of patients who underwent robotic nephrectomy for kidney tumors between 2011 and 2017. All pre- and postoperative CTs were re-reviewed by experienced radiologists for detection of radiologic IH and calculation of the following metrics using Synapse 3D software: cross-sectional psoas muscle mass at the level of L3 and L4 as well as subcutaneous and visceral fat areas. Sarcopenia was defined as psoas muscle index below the lowest quartile. Cox proportional hazard model was constructed to examine the association between muscle and fat metrics and the risk of developing radiologic IH. Results A total of 236 patients with a median (IQR) age of 64 (54–70) years were included in this study. In a median (IQR) follow-up of 23 (14–38) months, 62 (26%) patients developed radiologic IH. On Cox proportional hazard model, we were unable to detect an association between sarcopenia and risk of IH development. In terms of subcutaneous fat change from pre-op, both lower and higher values were associated with IH development (HR (95% CI) 2.1 (1.2–3.4), p = 0.01 and 2.4 (1.4–4.1), p < 0.01 for < Q1 and ≥ Q3, respectively). Similar trend was found for visceral fat area changes from pre-op with a HR of 2.8 for < Q1 and 1.8 for ≥ Q3. Conclusion Both excessive body fat gain and loss are associated with development of radiologic IH in patients undergoing robotic nephrectomy.

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“…Cox proportional hazard (CoxPH) models have gained popularity for outcome prediction. Nomograms, predictive tools based on the Cox proportional hazard model that integrate important predictors, have been widely utilized for risk quanti cation and prognosis assessment in various cancer types [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of Deep Learning Models for Ovarian Clear Cell Carcinoma Survival

Liu,

yang,

Zhao

et al. 2023

Preprint

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Background Ovarian clear cell carcinoma (OCCC) is a rare and distinct histologic subtype of epithelial ovarian carcinomas. Few studies have explored the use of deep learning models for predicting survival in OCCC. Our study aims to compare the performance of deep learning models with a multivariate Cox regression model in predicting survival for OCCC patients. Methods In this population-based cohort study, we extracted 926 patients diagnosed with OCCC between 2010 and 2017 from the Surveillance, epidemiology, and end results (SEER) database. Three algorithms, including DeepSurv and neural multi-task logistic regression (NMTLR) based on neural networks, and RSF based on ensemble learning, were chosen for training. Additionally, a Cox proportional hazard model was constructed for comparison purposes. The algorithm was externally validated on an independent test cohort, comprising 134 OCCC patients diagnosed between January 2005 and July 2021 in Qilu Hospital of Shandong University. The model's performance was assessed using the C-index and IBS (Integrated Brier Score), while the accuracy of predicting 1-, 3-, and 5-year survival was evaluated using ROC and AUC. Furthermore, a user-friendly interface was developed to facilitate the use of deep learning models for predicting survival. Results The deep learning model has demonstrated promising results in predicting overall survival (OS) for OCCC patients, outperforming the Cox proportional hazard model. DeepSurv consistently exhibited superior prediction performance compared to the Cox proportional hazard model in both the SEER training set (C-index: 0.781 vs. 0.724) and the independent China test set (C-index: 0.836 vs. 0.829). Additionally, the DeepSurv model displayed significantly higher AUC values for 3-year and 5-year OS in the China cohort when compared to the Cox proportional hazard model (AUC for 3-year OS: 0.844 vs. 0.836; AUC for 5-year OS: 0.821 vs. 0.817). Moreover, we developed a user-friendly graphical interface that allows for visualization of the deep learning model. Conclusions This study appears that deep learning models hold more promising than traditional linear regression models in predicting OS in OCCC patients. However, it is important to note that further large-scale, real-world studies are required to validate and substantiate this model.

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Nomogram of Combining CT-Based Body Composition Analyses and Prognostic Inflammation Score: Prediction of Survival in Advanced Epithelial Ovarian Cancer Patients

Cited by 9 publications

References 35 publications

Body composition and inflammation variables as the potential prognostic factors in epithelial ovarian cancer treated with Olaparib

Body composition and inflammation variables as the potential prognostic factors in epithelial ovarian cancer treated with Olaparib

Sarcopenia and body fat change as risk factors for radiologic incisional hernia following robotic nephrectomy

Development and Validation of Deep Learning Models for Ovarian Clear Cell Carcinoma Survival

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