Role of Baseline Computed-Tomography-Evaluated Body Composition in Predicting Outcome and Toxicity from First-Line Therapy in Advanced Gastric Cancer Patients

Automated 3D-Body Composition Analysis as a Predictor of Survival in Patients With Idiopathic Pulmonary Fibrosis

PURPOSE

Idiopathic pulmonary fibrosis (IPF) is the most common interstitial lung disease, with a median survival time of 2 to 5 years. The focus of this study is to establish a novel imaging biomarker.

MATERIALS AND METHODS

In this study, 79 patients (19% female) with a median age of 70 years were studied retrospectively. Fully automated body composition analysis (BCA) features (bone, muscle, total adipose tissue, intermuscular, and intramuscular adipose tissue) were combined into Sarcopenia, Fat, and Myosteatosis indices and compared between patients with a survival of more or less than 2 years. In addition, we divided the cohort at the median (high=≥ median, low=

RESULTS

A high Sarcopenia and Fat index and low Myosteatosis index were associated with longer median survival (35 vs. 16 mo for high vs. low Sarcopenia index, P=0.066; 44 vs. 14 mo for high vs. low Fat index, P<0.001; and 33 vs. 14 mo for low vs. high Myosteatosis index, P=0.0056) and better 5-year survival rates (34.0% vs. 23.6% for high vs. low Sarcopenia index; 47.3% vs. 9.2% for high vs. low Fat index; and 11.2% vs. 42.7% for high vs. low Myosteatosis index). Adjusted multivariate Cox regression showed a significant impact of the Fat (HR=0.71, P=0.01) and Myosteatosis (HR=1.12, P=0.005) on overall survival.

CONCLUSION

The fully automated BCA provides biomarkers with a predictive value for the overall survival in patients with IPF.

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Affiliation(s)

Luca Salhöfer Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Francesco Bonella Department of Pneumology, Center for Interstitial and Rare Lung Diseases, University Hospital Essen, Essen, Germany
Mathias Meetschen Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Lale Umutlu Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Michael Forsting Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Benedikt Michael Schaarschmidt Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Marcel Klaus Opitz Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Jens Kleesiek Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Rene Hosch Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Sven Koitka Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Vicky Parmar Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Felix Nensa Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Johannes Haubold Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany

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Convolutional Neural Network-Based Automated Segmentation of Skeletal Muscle and Subcutaneous Adipose Tissue on Thigh MRI in Muscular Dystrophy Patients

We aim to develop a deep learning-based algorithm for automated segmentation of thigh muscles and subcutaneous adipose tissue (SAT) from T1-weighted muscle MRIs from patients affected by muscular dystrophies (MDs). From March 2019 to February 2022, adult and pediatric patients affected by MDs were enrolled from Azienda Ospedaliera Universitaria Pisana, Pisa, Italy (Institution 1) and the IRCCS Stella Maris Foundation, Calambrone-Pisa, Italy (Institution 2), respectively. All patients underwent a bilateral thighs MRI including an axial T1 weighted in- and out-of-phase (dual-echo). Both muscles and SAT were manually and separately segmented on out-of-phase image sets by a radiologist with 6 years of experience in musculoskeletal imaging. A U-Net1 and U-Net3 were built to automatically segment the SAT, all the thigh muscles together and the three muscular compartments separately. The dataset was randomly split into the on train, validation, and test set. The segmentation performance was assessed through the Dice similarity coefficient (DSC). The final cohort included 23 patients. The estimated DSC for U-Net1 was 96.8%, 95.3%, and 95.6% on train, validation, and test set, respectively, while the estimated accuracy for U-Net3 was 94.1%, 92.9%, and 93.9%. Both of the U-Nets achieved a median DSC of 0.95 for SAT segmentation. The U-Net1 and the U-Net3 achieved an optimal agreement with manual segmentation for the automatic segmentation. The so-developed neural networks have the potential to automatically segment thigh muscles and SAT in patients affected by MDs.

Development and validation of a preoperative CT-based risk scoring system for predicting recurrence-free survival in patients undergoing curative surgery for gastric cancer

PURPOSE

The objective of this study was to establish and validate a preoperative risk scoring system that incorporated both clinical and computed tomography(CT) variables to predict recurrence-free survival (RFS) in gastric cancer(GC) patients who underwent curative resection.

METHOD

We retrospectively included consecutive patients with surgically confirmed GC who underwent preoperative CT scans between October 2017 and January 2022. Multivariate Cox regression analysis was employed in the derivation set to identify clinical and CT variables associated with RFS and to construct a risk score. This risk score was subsequently validated in an independent test set.

RESULTS

A total of 346 patients were included in the study, with 213 in the derivation set and 133 in the test set. Five variables, namely ctEMVI, ctBorrmann, visceral obesity, sarcopenia, and NLR, were independently associated with RFS. In the test set, the preoperative risk score exhibited a c-index of 0.741, which outperformed the predictive accuracy of pathological tumor staging (c-index of 0.673, p = 0.021) at various time points. The preoperative risk score effectively stratified patients into low and high-risk groups.

CONCLUSION

The developed preoperative risk scoring system demonstrated the ability to predict RFS following curative resection in GC patients.

Abdominal Visceral-to-Subcutaneous Fat Volume Ratio Predicts Survival and Response to First-Line Palliative Chemotherapy in Patients with Advanced Gastric Cancer

Prognosis in advanced gastric cancer (aGC) is predicted by clinical factors, such as stage, performance status, metastasis location, and the neutrophil-to-lymphocyte ratio. However, the role of body composition and sarcopenia in aGC survival remains debated. This study aimed to evaluate how abdominal visceral and subcutaneous fat volumes, psoas muscle volume, and the visceral-to-subcutaneous (VF/SF) volume ratio impact overall survival (OS) and progression-free survival (PFS) in aGC patients receiving first-line palliative chemotherapy. We retrospectively examined CT scans of 65 aGC patients, quantifying body composition parameters (BCPs) in 2D and 3D. Normalized 3D BCP volumes were determined, and the VF/SF ratio was computed. Survival outcomes were analyzed using the Cox Proportional Hazard model between the upper and lower halves of the distribution. Additionally, response to first-line chemotherapy was compared using the χ2 test. Patients with a higher VF/SF ratio (N = 33) exhibited significantly poorer OS (p = 0.02) and PFS (p < 0.005) and had a less favorable response to first-line chemotherapy (p = 0.033), with a lower Disease Control Rate (p = 0.016). Notably, absolute BCP measures and sarcopenia did not predict survival. In conclusion, radiologically assessed VF/SF volume ratio emerged as a robust and independent predictor of both survival and treatment response in aGC patients.

Low skeletal muscle mass predicts treatment response in oncology: a meta-analysis

OBJECTIVES

Low skeletal muscle mass (LSMM) predicts relevant clinical outcomes in oncologic patients. The purpose of this study was to perform a meta-analysis of data regarding associations between LSMM and treatment response (TR) in oncology.

METHODS

MEDLINE, Cochrane, and SCOPUS databases were screened for relationships between LSMM and TR in oncologic patients up to November 2022. Overall, 35 studies met the inclusion criteria. The meta-analysis was performed using RevMan 5.4 software.

RESULTS

The collected 35 studies comprised 3858 patients. In 1682 patients (43.6%), LSMM was diagnosed. In the overall sample, LSMM predicted a negatively objective response rate (ORR), OR = 0.70, 95% CI = (0.54-0.91), p = 0.007, and disease control rate (DCR), OR = 0.69, 95% CI = (0.50-0.95), p = 0.02. In the curative setting, LSMM predicted a negatively ORR, OR = 0.24, 95% CI = (0.12-0.50), p = 0.0001, but not DCR, OR = 0.60, 95% CI = (0.31-1.18), p = 0.14. In palliative treatment with conventional chemotherapies, LSMM did not predict ORR: OR = 0.94, 95% CI (0.57-1.55), p = 0.81, and DCR: OR = 1.13, 95% CI (0.38-3.40), p = 0.82. In palliative treatment with tyrosine kinase inhibitors (TKI), LSMM did not predict TR: ORR, OR = 0.74, 95% CI (0.44-1.26), p = 0.27, and DCR, OR = 1.04, 95% CI (0.53-2.05), p = 0.90. In palliative immunotherapy, LSMM tended to predict ORR, OR = 0.74, 95% CI = (0.54-1.01), p = 0.06, and predicted DCR, OR = 0.53, 95% CI = (0.37-0.76), p = 0.0006.

CONCLUSION

LSMM is a risk factor for poor TR in curative chemotherapy in the adjuvant and/or neoadjuvant setting. LSMM is a risk factor for treatment failure in treatment with immunotherapy. Finally, LSMM does not influence TR in palliative treatment with conventional chemotherapy and/or TKIs.

KEY POINTS

• Low skeletal muscle mass (LSMM) predicts treatment response (TR) to chemotherapy in the adjuvant and/or neoadjuvant setting. • LSMM predicts TR in immunotherapy. • LSMM does not influence TR in palliative chemotherapy.

Nutrition care is an integral part of patient-centred medical care: a European consensus

While healthcare is becoming more patient-centred, evidence-based nutrition interventions are still not accessible to all patients with cancer. As nutrition interventions directly improve clinical and socioeconomic outcomes, patient-centred care is not complete without nutrition care. While awareness of the negative impact of malnutrition on clinical outcomes, quality of life, and functional and emotional wellbeing in cancer is growing, there is relatively poor awareness amongst patients, clinicians, policymakers, and payers that nutrition interventions -particularly those begun in the early stages of the disease course- are an effective method for improving such outcomes. The European Beating Cancer Plan recognises the need for a holistic approach to cancer but lacks actionable recommendations to implement integrated nutrition cancer care at member state level. When considering nutrition care as a human right, the impact on quality of life and functional status must be prioritized, as these may be equally as important to patients, especially in advanced cancer where improvements in clinical outcomes such as survival or tumour burden may not be attainable. We formulate actions needed at the regional and the European level to ensure integrated nutrition care for all patients with cancer. The 4 main Take Home Messages are as follows: 1. The goals of Europe's Beating Cancer Plan cannot be achieved without integrating nutrition across the cancer care continuum. 2. Malnutrition negatively impacts clinical outcomes and has socioeconomic consequences for patients and healthcare systems. 3. Championing integrating nutrition care into cancer care is therefore the duty and ethical responsibility of clinicians (Hippocratic Oath-primum non nocere) and 4. Nutrition care is a cost effective, evidence-based therapy.

Pre-Therapeutic Sarcopenia among Cancer Patients: An Up-to-Date Meta-Analysis of Prevalence and Predictive Value during Cancer Treatment

This study will address the prevalence of pre-therapeutic sarcopenia (PS) and its clinical impact during cancer treatment among adult cancer patients ≥ 18 years of age. A meta-analysis (MA) with random-effect models was performed via a MEDLINE systematic review, according to the PRISMA statement, focusing on articles published before February 2022 that reported observational studies and clinical trials on the prevalence of PS and the following outcomes: overall survival (OS), progression-free survival (PFS), post-operative complications (POC), toxicities (TOX), and nosocomial infections (NI). A total of 65,936 patients (mean age: 45.7-85 y) with various cancer sites and extensions and various treatment modes were included. Mainly defined by CT scan-based loss of muscle mass only, the pooled prevalence of PS was 38.0%. The pooled relative risks were 1.97, 1.76, 2.70, 1.47, and 1.76 for OS, PFS, POC, TOX, and NI, respectively (moderate-to-high heterogeneity, I²: 58-85%). Consensus-based algorithm definitions of sarcopenia, integrating low muscle mass and low levels of muscular strength and/or physical performance, lowered the prevalence (22%) and heterogeneity (I² < 50%). They also increased the predictive values with RRs ranging from 2.31 (OS) to 3.52 (POC). PS among cancer patients is prevalent and strongly associated with poor outcomes during cancer treatment, especially when considering a consensus-based algorithm approach.

Sarcopenia as a risk factor of progression-free survival in patients with metastases: a systematic review and meta-analysis

BACKGROUND

Metastasis of cancer causes more than 90% of cancer deaths and is severely damaging to human health. In recent years, several studies have linked sarcopenia to shorter survival in patients with metastatic cancer. Several predictive models exist to predict mortality in patients with metastatic cancer, but have reported limited accuracy.

METHODS

We systematically searched Medline, EMBASE, and the Cochrane Library for articles published on or before October 14, 2022. Pooled Hazard Ratio (HR) estimates with 95% confidence intervals (CIs) were calculated using a random effects model. The primary outcome was an increased risk of death or tumor progression in patients with metastatic cancer, which is expressed as progression-free survival (PFS). In addition, we performed subgroup analyses and leave-one-out sensitivity analyses to explore the main sources of heterogeneity and the stability of the results.

RESULTS

Sixteen retrospective cohort studies with 1,675 patients were included in the 888 papers screened. The results showed that sarcopenia was associated with lower progression-free survival (HR = 1.56, 95% CI = 1.19-2.03, I2 = 76.3%, P < 0.001). This result was further confirmed by trim-and-fill procedures and leave-one-out sensitivity analysis.

CONCLUSIONS

This study suggests that sarcopenia may be a risk factor for reduced progression-free survival in patients with metastatic cancer. Further studies are still needed to explain the reason for this high heterogeneity in outcome.

TRIAL REGISTRATION

CRD42022325910.

Adiposity and cancer survival: a systematic review and meta-analysis

PURPOSE

The increasing availability of clinical imaging tests (especially CT and MRI) that directly quantify adipose tissue has led to a rapid increase in studies examining the relationship of visceral, subcutaneous, and overall adiposity to cancer survival. To summarize this emerging body of literature, we conducted a systematic review and meta-analysis of imaging-measured as well as anthropometric proxies for adipose tissue distribution and cancer survival across a wide range of cancer types.

METHODS

Using keywords related to adiposity, cancer, and survival, we conducted a systematic search of the literature in PubMed and MEDLINE, Embase, and Web of Science Core Collection databases from database inception to 30 June 2021. We used a random-effect method to calculate pooled hazard ratios (HR) and corresponding 95% confidence intervals (CI) within each cancer type and tested for heterogeneity using Cochran's Q test and the I2 test.

RESULTS

We included 203 records for this review, of which 128 records were utilized for quantitative analysis among 10 cancer types: breast, colorectal, gastroesophageal, head and neck, hepatocellular carcinoma, lung, ovarian, pancreatic, prostate, and renal cancer. We found that imaging-measured visceral, subcutaneous, and total adiposity were not significantly associated with increased risk of overall mortality, death from primary cancer, or cancer progression among patients diagnosed with these 10 cancer types; however, we found significant or high heterogeneity for many cancer types. For example, heterogeneity was similarly high when the pooled HRs (95% CI) for overall mortality associated with visceral adiposity were essentially null as in 1.03 (0.55, 1.92; I2 = 58%) for breast, 0.99 (0.81, 1.21; I2 = 71%) for colorectal, versus when they demonstrated a potential increased risk 1.17 (0.85, 1.60; I2 = 78%) for hepatocellular carcinoma and 1.62 (0.90, 2.95; I2 = 84%) for renal cancer.

CONCLUSION

Greater adiposity at diagnosis (directly measured by imaging) is not associated with worse survival among cancer survivors. However, heterogeneity and other potential limitations were noted across studies, suggesting differences in study design and adiposity measurement approaches, making interpretation of meta-analyses challenging. Future work to standardize imaging measurements and data analyses will strengthen research on the role of adiposity in cancer survival.

Microglia Pyroptosis: A Candidate Target for Neurological Diseases Treatment

In addition to its profound implications in the fight against cancer, pyroptosis have important role in the regulation of neuronal injury. Microglia are not only central members of the immune regulation of the central nervous system (CNS), but are also involved in the development and homeostatic maintenance of the nervous system. Under various pathological overstimulation, microglia pyroptosis contributes to the massive release of intracellular inflammatory mediators leading to neuroinflammation and ultimately to neuronal damages. In addition, microglia pyroptosis lead to further neurological damage by decreasing the ability to cleanse harmful substances. The pathogenic roles of microglia in a variety of CNS diseases such as neurodegenerative diseases, stroke, multiple sclerosis and depression, and many other neurological disorders have been gradually unveiled. In the context of different neurological disorders, inhibition of microglia pyroptosis by targeting NOD-like receptor family pyrin domain containing (NLRP) 3, caspase-1 and gasdermins (GSDMs) by various chemical agents as well as natural products significantly improve the symptoms or outcome in animal models. This study will provide new ideas for immunomodulatory treatment of CNS diseases.

CT-determined low skeletal muscle mass predicts worse overall survival of gastric cancer in patients with cachexia

BACKGROUND

There were controversies for the association between computed tomography (CT)-determined low skeletal muscle mass (SMM) and overall survival (OS) in gastric cancer (GC). In this study, we investigated whether cachexia could be a potential confounding variable for this issue.

METHODS

We retrospectively collected the patients of GC in our institution between July 2016 and January 2021. Preoperative SMM was determined by analyzing the skeletal muscle index of L3 with abdominal CT, and the cut-offs for low SMM were defined as <52.4 (men) and < 38.5 cm² /m² (women), respectively. Overall survival (OS) was the primary endpoint.

RESULTS

Of the 255 included GC patients, 117 (46%) were classified as having low SMM. Those with low SMM were associated with a higher level of circulating interleukin 6 and C reactive protein but a lower level of albumin than those of normal SMM. The univariate analysis showed that low SMM, tumor-node-metastasis (TNM) stage, body mass index (BMI), postoperative chemotherapy, and cachexia were significantly associated with OS, while in the multivariate analysis, only low SMM and TNM stage were significantly associated with OS. Kaplan-Meier survival curves with log-rank tests indicated that low SMM significantly predicted worse OS of GC. After grouping by cachexia, the low SMM significantly predicted worse OS in patients with cachexia instead of those without cachexia.

CONCLUSIONS

CT-determined low SMM predicts worse OS of GC in patients with cachexia instead of those without cachexia, and greater attention should be paid to such patients with synchronous low SMM and cachexia.