Deep-Learning Assessed Muscular Hypodensity Independently Predicts Mortality in DLBCL Patients Younger Than 60 Years

Prevalence and prognostic value of baseline sarcopenia in hematologic malignancies: a systematic review

Background

The correlation between sarcopenia and hematological malignancy prognosis is still controversial. Design: A systematic review and meta-analysis. Objectives: To explore sarcopenia's prevalence and prognostic value in hematologic malignancies.

Data sources and methods

We searched Embase, MEDLINE, and Cochrane Library through Ovid SP using an appropriate search strategy on August 28, 2022, and updated the search results on January 9, 2023. Study quality was assessed using the Newcastle-Ottawa scale. The pooled prevalence of sarcopenia was calculated with a 95% confidence interval (CI). Relationships between sarcopenia and prognostic value were expressed as hazard ratio (HR) and 95% CI. HR means the probability of something undesirable, i.e., death or disease progression.

Results

The search identified more than 3992 studies, and 21 (3354 patients, median or mean age ranging from 36 to 78 years) were finally included. The risk of bias in the studies was low to medium. All included studies were diagnosed based on low muscle mass (LMM). Muscle mass was assessed mainly through imaging technologies, and different cut-offs were applied to determine LMM. The prevalence of sarcopenia was 44.5%, which could fluctuate by age. Subgroup analysis showed that older people had a higher sarcopenic rate than the non-elderly group. Sarcopenia resulted in an inferior prognosis [overall survival: HR 1.821, 95% CI 1.415-2.343; progression-free survival: HR 1.703, 95% CI 1.128-2.571).

Conclusion

Sarcopenia has a prevalence of over 30% in malignant hematologic patients and is associated with a poorer prognosis. Future studies with a standardized sarcopenia diagnostic criterion were needed to investigate sarcopenia's prevalence and prognostic effects in hematologic malignancies.

Prevalence and effect on survival of pre-treatment sarcopenia in patients with hematological malignancies: a meta-analysis

Background & aims

Evidence regarding the prevalence of pre-treatment sarcopenia and its impact on survival in patients with hematological malignancies (HM) varies across studies. We conducted a systematic review and meta-analysis to summarize this discrepancy.

Methods

PubMed, Embase and Cochrane library were systematically searched for relevant studies. Outcomes assessed were: prevalence of pre-treatment sarcopenia, overall survival (OS), progression-free survival (PFS) and complete response (CR). Weighted mean proportion, odds ratios (ORs) and hazard ratios (HRs) were estimated using a fixed-effects and a random-effects model.

Results

A total of 27 retrospective cohort studies involving 4,991 patients were included in this study. The prevalence of pre-treatment sarcopenia was 37.0% (95% CI: 32.0%-42.0%) in HM patients <60 years and 51.0% (95% CI: 45.0%-57.0%) in≥60 years. Patients with leukemia had the lowest prevalence, compared with those with other HM (38.0%; 95% CI: 33.0%-43.0%; P = 0.010). The presence of sarcopenia was independently associated with poor OS (HR = 1.57, 95% CI = 1.41-1.75) and PFS (HR = 1.50, 95% CI = 1.22-1.83) throughout treatment period, which may be partially attributed to decreased CR (OR = 0.54, 95% CI = 0.41-0.72), particularly for BMI ≥ 25 (P = 0.020) and males (P = 0.020).

Conclusion

Sarcopenia is highly prevalent in patients with HM and an adverse prognostic factor for both survival and treatment efficacy. HM and sarcopenia can aggravate each other. We suggest that in future clinical work, incorporating sarcopenia into risk scores will contribute to guide patient stratification and therapeutic strategy, particularly for the elderly.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier (CRD42023392550).

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.

The application of machine learning to imaging in hematological oncology: A scoping review

Background

Here, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps.

Methods

The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. The inclusion criteria were (i) pediatric and adult patients with suspected or confirmed hematological malignancy undergoing imaging (population); (ii) any study using ML techniques to derive models using radiological images to apply to the clinical management of these patients (concept); and (iii) original research articles conducted in any setting globally (context). Quality Assessment of Diagnostic Accuracy Studies 2 criteria were used to assess diagnostic and segmentation studies, while the Newcastle-Ottawa scale was used to assess the quality of observational studies.

Results

Of 53 eligible studies, 33 applied diverse ML techniques to diagnose hematological malignancies or to differentiate them from other diseases, especially discriminating gliomas from primary central nervous system lymphomas (n=18); 11 applied ML to segmentation tasks, while 9 applied ML to prognostication or predicting therapeutic responses, especially for diffuse large B-cell lymphoma. All studies reported discrimination statistics, but no study calculated calibration statistics. Every diagnostic/segmentation study had a high risk of bias due to their case-control design; many studies failed to provide adequate details of the reference standard; and only a few studies used independent validation.

Conclusion

To deliver validated ML-based models to radiologists managing hematological malignancies, future studies should (i) adhere to standardized, high-quality reporting guidelines such as the Checklist for Artificial Intelligence in Medical Imaging; (ii) validate models in independent cohorts; (ii) standardize volume segmentation methods for segmentation tasks; (iv) establish comprehensive prospective studies that include different tumor grades, comparisons with radiologists, optimal imaging modalities, sequences, and planes; (v) include side-by-side comparisons of different methods; and (vi) include low- and middle-income countries in multicentric studies to enhance generalizability and reduce inequity.

Assessment of Functional and Nutritional Status and Skeletal Muscle Mass for the Prognosis of Critically Ill Solid Cancer Patients

Simple and accessible prognostic factors are paramount for solid cancer patients experiencing life-threatening complications. The aim of this study is to appraise the impact of functional and nutritional status and skeletal muscle mass in this population. We conducted a retrospective (2007−2020) single-center study by enrolling adult patients with solid cancers requiring unplanned ICU admission. Performance status, body weight, and albumin level were collected at ICU admission and over six months. Skeletal muscle mass was assessed at ICU admission by measuring muscle areas normalized by height (SMI). Four-hundred and sixty-two patients were analyzed, mainly with gastro-intestinal (34.8%) and lung (29.9%) neoplasms. Moreover, 92.8% of men and 67.3% of women were deemed cachectic. In the multivariate analysis, performance status at ICU admission (CSH 1.74 [1.27−2.39], p < 0.001) and the six month increase in albumin level (CSH 0.38 [0.16−0.87], p = 0.02) were independent predictors of ICU mortality. In the subgroup of mechanically ventilated patients, the psoas SMI was independently associated with ICU mortality (CSH 0.82 [0.67−0.98], p = 0.04). Among the 368 ICU-survivors, the performance status at ICU admission (CSH 1.34 [1.14−1.59], p < 0.001) and the six-month weight loss (CSH 1.33 [1.17−2.99], p = 0.01) were associated with a one-year mortality rate. Most cancer patients displayed cachexia at ICU admission. Time courses of nutritional parameters may aid the prediction of short- and long-term outcomes.

Pectoralis major muscle quantification by cardiac MRI is a strong predictor of major events in HF

Clinical overt cardiac cachexia is a late ominous sign in patients with heart failure (HF) and reduced left ventricular ejection fraction (LVEF). The main goal of this study was to assess the feasibility and prognostic significance of muscle mass quantification by cardiac magnetic resonance (CMR) in HF with reduced LVEF. HF patients with LVEF < 40% (HFrEF) referred for CMR were retrospectively identified in a single center. Key exclusion criteria were primary muscle disease, known infiltrative myocardial disease and intracardiac devices. Pectoralis major muscles were measured on standard axial images at the level of the 3rd rib anteriorly. Time to all-cause death or HF hospitalization was the primary endpoint. A total of 298 HF patients were included (mean age 64 ± 12 years; 76% male; mean LVEF 30 ± 8%). During a median follow-up of 22 months (IQR: 12-33), 67 (22.5%) patients met the primary endpoint (33 died and 45 had at least 1 HF hospitalization). In multivariate analysis, LVEF [Hazard Ratio (HR): 0.950; 95% Confidence Interval (CI): 0.917-0.983; p = 0.003), NYHA class I-II vs III-IV (HR: 0.480; CI: 0.272-0.842; p = 0.010), creatinine (HR: 2.653; CI: 1.548-4.545; p < 0.001) and pectoralis major area (HR: 0.873; 95% CI: 0.821-0.929; p < 0.001) were independent predictors of the primary endpoint, when adjusted for gender and NT-pro-BNP levels. Pectoralis major size measured by CMR in HFrEF was independently associated with a higher risk of death or HF hospitalization. Further studies to establish appropriate age and gender-adjusted cut-offs of muscle areas are needed to identify high-risk subgroups.