Identifying cancer cachexia in patients without weight loss information: machine learning approaches to address a real-world challenge

An External Validation of the Pathologic Fracture Mortality Index for Predicting 30-day Postoperative Morbidity Using 978 Institutional Patients

INTRODUCTION

Skeletal metastases increase the risk of pathologic fractures, causing functional impairment and pain. Predicting morbidity in patients undergoing surgical fixation for these fractures is challenging due to the complexity of metastatic disease. The Pathologic Fracture Mortality Index (PFMI) was developed to predict 30-day postoperative morbidity in long bone fractures caused by metastases. External validation is necessary for clinical use. This study aims to evaluate the following: (1) How well does the PFMI predict 30-day medical, surgical, utilization, and all-cause morbidity after pathologic fracture fixation in an external cohort of patients with long bone metastases? (2) How does the performance of the PFMI compare to established predictive indices including the American Society of Anesthesiologists (ASA) classification score, the modified 5-Item Frailty Index (mF-I5), and the modified Charlson Comorbidity Index (mCCI)?

METHODS

We analyzed 978 patients who underwent internal fixation for pathologic fractures at two urban tertiary centers. The area under the receiver operating characteristic curve (AUC) was calculated for each predictive index to assess their accuracy in predicting 30-day morbidity across medical, surgical, utilization, and all-cause categories.

RESULTS

All four predictive indices demonstrated suboptimal performance, with AUC values ranging from 0.51-0.62, 0.45-0.51, 0.51-0.62, and 0.50-0.57 for medical, surgical, utilization, and all-cause morbidity, respectively. The PFMI outperformed the ASA ( P < 0.001), mF-I5 ( P = 0.018), and mCCI ( P = 0.034) in predicting utilization morbidity. It also better predicted medical ( P = 0.021) and all-cause ( P = 0.009) morbidity than ASA but did not outperform mF-I5 or mCCI in these areas. The PFMI did not surpass any indices in surgical morbidity.

CONCLUSION

None of the indices reached the ideal AUC of 0.80 for any morbidity type, emphasizing the need for refinement. Updating these tools with contemporary data and exploring new prognostic factors is critical to improve morbidity risk stratification in metastatic bone disease.

Early identification of potentially reversible cancer cachexia using explainable machine learning driven by body weight dynamics: a multicenter cohort study

BACKGROUND

Cachexia is associated with multiple adverse outcomes in cancer. However, clinical decision-making for oncology patients at the cachexia stage presents significant challenges.

OBJECTIVES

This study aims to develop a machine learning (ML) model to identify potentially reversible cancer cachexia (PRCC).

METHODS

This was a multicenter cohort study. Cachexia was retrospectively diagnosed using Fearon's framework. PRCC was defined as a diagnosis of cancer cachexia at baseline that turned negative 1 mo later. Body weight dynamics accessible upon patient admission were screened and modeled to predict PRCC. Multiple ML models were trained and cross-validated using 70% of the data to predict PRCC, with the remaining 30% reserved for model evaluation. The interpretability and clinical usefulness of the optimal model were assessed, and external validation was performed in an independent cohort of 238 patients.

RESULTS

The study enrolled 1983 men and 1784 women (median age = 58 y). PRCC was identified in 1983 patients (52.6%). Breast cancer exhibited the highest rate of PRCC (72.1%), whereas cachexia associated with various gastrointestinal cancers was less likely to be reversed. Weight change (WC) from 6 mo ago to 1 mo ago, WC from 1 mo ago to baseline (-1 to 0), and baseline body mass index were selected for modeling. A multilayer perceptron model showed good performance to predict PRCC in the holdout test set [area under the curve (95% confidence interval): 0.887 (0.866, 0.907); accuracy: 0.836; sensitivity: 0.859; specificity: 0.812] and the external validation set [area under the curve (95% confidence interval): 0.863 (0.778, 0.948)]. The WC -1 to 0 showed the highest impact on model output. The model was demonstrated to be clinically useful and statistically relevant.

CONCLUSIONS

This study presents an explainable ML model for the early identification of PRCC that utilizes simple body weight dynamics. The findings showcase the potential of this approach in improving the management of cancer cachexia to optimize patient outcomes.

Diagnostic Criteria for Cancer-Associated Cachexia: Insights from a Multicentre Cohort Study

BACKGROUND

To explore the association between cachexia, as defined by different diagnostic criteria, and the risk of mortality in individuals with cancer. We also examined which diagnostic criteria are more feasible and appropriate for cancer-associated cachexia in clinical practice.

METHODS

A multicentre cohort study was conducted, which involved 5769 participants with cancer. The diagnosis of cachexia was made by applying the initial Fearon criteria (with the appendicular skeletal muscle mass index [ASMI]) and six modified criteria: (1) evaluating the muscle mass through the mid-upper-arm muscle area (MAMA), (2) fat-free mass index (FFMI), (3) calf circumference (CC), (4) hand grip strength (HGS), (5) neutrophil-to-lymphocyte ratio (NLR) and (6) omission of reduced muscle mass. The correlations between cancer cachexia diagnosed by different definitions and survival were assessed using Kaplan-Meier analyses and multivariable-adjusted Cox models. The sensitivity, specificity, positive likelihood ratios, negative likelihood ratios, AUC value, Youden index and weighted kappa coefficient were calculated for each set of criteria.

RESULTS

The final analysis included 5110 patients diagnosed with 15 different types of cancer, with a median age of 56. Out of these, 2490 (48.7%) were male. The prevalence of cancer cachexia based on the Fearon criteria was 26.5%, ranging from 21.8% to 32.2% with the six modified criteria. Following adjustment for age, sex, clinical stage and cancer site, cachexia defined by Fearon criteria was associated with a noteworthy increase in mortality (HR, 1.275; 95% CI, 1.136-1.430; p < 0.001), ranging from 1.237 (95% CI, 1.106-1.383; p < 0.001) to 1.382 (95% CI, 1.226-1.557; p < 0.001) by the six modified criteria. All six modified criteria presented adequate performance indicators (all p < 0.001), with sensitivity ranging from 82.4% (95% CI, 80.2%-84.3%) to 90.7% (95% CI, 89.0%-92.2%), specificity ranging from 86.9% (95% CI, 85.7%-87.9%) to 100.0% (95% CI, 99.9%-100.0%) and AUC ranging from 0.860 (95% CI, 0.850-0.869) to 0.932 (95% CI, 0.925-0.939). The modified criteria also showed strong (Fearon criteria with NLR: κ = 0.673, 95% CI, 0.651-0.695) to almost perfect (Fearon criteria without reduced muscle mass [RMM]: κ = 0.873, 95% CI, 0.857-0.888) consistency with the original Fearon criteria.

CONCLUSIONS

Cachexia defined by the Fearon criteria and the six modified criteria can predict the survival of cancer patients. All criteria provided a precise diagnosis and were feasible to use in clinical settings.

Development and validation of a prognostic model for cachexia in postoperative gastric cancer patients with low nutritional risk: a dual-center retrospective cohort study

BACKGROUND

Gastric cancer can lead to excessive catabolism in patients. After undergoing gastric surgery, patients may experience additional unintended weight loss, resulting in severe malnutrition and potentially cachexia.

METHODS

We selected and incorporated patients from two centers. Cohort 1 (n = 1393) served as the development cohort, while cohort 2 (n = 501) was designated as an external validation cohort. Within cohort 1, 70% of the patients were utilized for model training, with the remaining 30% reserved for internal validation. The training set initially underwent univariate logistic regression, followed by multivariate logistic regression. The factors ultimately incorporated were used to construct the model and create nomograms. The discriminative ability was assessed using ROC curves in all three datasets, calibration curves were used to evaluate consistency, and decision curves analysis was performed to assess the clinical application value.

RESULTS

The model incorporated 12 factors, specifically: age (OR = 1.07), preoperative BMI (OR = 0.89), surgery type (Total Gastrectomy (TG), OR = 1.83), chemotherapy (yes, OR = 1.52), stage (III, OR = 1.40), anastomotic obstruction (yes, OR = 6.85), Postsurgical Gastroparesis Syndrome (PGS) (yes, OR = 2.27), albumin (OR = 0.95), hemoglobin (OR = 0.98), triglycerides (OR = 0.36), CRP (OR = 1.07), and Neutrophil to Lymphocyte Ratio (NLR) (OR = 1.05). The model demonstrated robust performance in ROC with AUC values of 0.805 in the training set, 0.767 in the validation set, and 0.795 in Cohort 2. Calibration curves in all three datasets exhibited a high degree of concordance between actual and predicted probabilities. Decision curve analysis (DCA) indicated that the model holds substantial clinical utility across all three datasets.

CONCLUSIONS

We have developed a predictive model for cachexia in patients undergoing gastric cancer surgery. This model enables healthcare professionals to accurately estimate the risk of cachexia in postoperative patients with nutritional deficits, allowing for timely nutritional interventions to enhance patient quality of life and prognosis.

Utilizing machine learning approaches to investigate the relationship between cystatin C and serious complications in esophageal cancer patients after esophagectomy

BACKGROUND

The purpose of this study is to investigate the relationship between preoperative cystatin C levels and the risk of serious postoperative complications in esophageal cancer (EC) patients, utilizing advanced machine learning (ML) methodologies.

METHODS

We conducted an observational cohort study, involving 524 EC patients from December 2014 to July 2022. ML models, including logistic regression (LR) and multilayer perceptron (MLP), were applied to investigate the relationship between cystatin C and the serious postoperative complications. The predictive value of cystatin C was evaluated using receiver operating characteristic (ROC) analysis. Based on a restricted cubic spline (RCS) method, the potential nonlinear association was scrutinized.

RESULTS

The morbidity of serious postoperative complications was 8.78%. Bleeding volume, operating time, NRS2002 score, PONS score, and cystatin C were significantly associated with serious postoperative complications. The MLP model demonstrated superior predictive accuracy (AUC = 0.775, 95% CI: 0.701-0.849) compared to the LR model (AUC = 0.714, 95% CI: 0.630-0.798) and cystatin C alone (AUC = 0.612, 95% CI: 0.526-0.699). High cystatin C level independently predicted serious postoperative complications in EC patients. A positive and linear association was found between cystatin C and serious complications.

CONCLUSION

This research uncovers a notable correlation between cystatin C and the severe complications in EC patients after esophagectomy. Employing ML techniques offers a robust method for forecasting patient outcomes and emphasizes the potential of cystatin C as a predictive biomarker in medical practice.

Using 3D facial information to predict malnutrition and consequent complications

BACKGROUND

Phase angle (PhA) correlates with body composition and could predict the nutrition status of patients and disease prognosis. We aimed to explore the feasibility of predicting PhA-diagnosed malnutrition using facial image information based on deep learning (DL).

METHODS

From August 2021 to April 2022, inpatients were enrolled from surgery, gastroenterology, and oncology departments in a tertiary hospital. Subjective global assessment was used as the gold standard of malnutrition diagnosis. The highest Youden index value was selected as the PhA cutoff point. We developed a multimodal DL framework to automatically analyze the three-dimensional (3D) facial data and accurately determine patients' PhA categories. The framework was trained and validated using a cross-validation approach and tested on an independent dataset.

RESULTS

Four hundred eighty-two patients were included in the final dataset, including 176 with malnourishment. In male patients, the PhA value with the highest Youden index was 5.55°, and the area under the receiver operating characteristic curve (AUC) = 0.68; in female patients, the PhA value with the highest Youden index was 4.88°, and AUC = 0.69. Inpatients with low PhA had higher incidence of infectious complications during the hospital stay (P = 0.003). The DL model trained with 4096 points extracted from 3D facial data had the best performance. The algorithm showed fair performance in predicting PhA, with an AUC of 0.77 and an accuracy of 0.74.

CONCLUSION

Predicting the PhA of inpatients from facial images is feasible and can be used for malnutrition assessment and prognostic prediction.

An Artificial Intelligence Approach for Test-Free Identification of Sarcopenia

BACKGROUND

The diagnosis of sarcopenia relies extensively on human and equipment resources and requires individuals to personally visit medical institutions. The objective of this study was to develop a test-free, self-assessable approach to identify sarcopenia by utilizing artificial intelligence techniques and representative real-world data.

METHODS

This multicentre study enrolled 11 661 middle-aged and older adults from a national survey initialized in 2011. Follow-up data from the baseline cohort collected in 2013 (n = 9403) and 2015 (n = 10 356) were used for validation. Sarcopenia was retrospectively diagnosed using the Asian Working Group for Sarcopenia 2019 framework. Baseline age, sex, height, weight and 20 functional capacity (FC)-related binary indices (activities of daily living = 6, instrumental activities of daily living = 5 and other FC indices = 9) were considered as predictors. Multiple machine learning (ML) models were trained and cross-validated using 70% of the baseline data to predict sarcopenia. The remaining 30% of the baseline data, along with two follow-up datasets (n = 9403 and n = 10 356, respectively), were used to assess model performance.

RESULTS

The study included 5634 men and 6027 women (median age = 57.0 years). Sarcopenia was identified in 1288 (11.0%) individuals. Among the 20 FC indices, the running/jogging 1 km item showed the highest predictive value for sarcopenia (AUC [95%CI] = 0.633 [0.620-0.647]). From the various ML models assessed, a 24-variable gradient boosting classifier (GBC) model was selected. This GBC model demonstrated favourable performance in predicting sarcopenia in the holdout data (AUC [95%CI] = 0.831 [0.808-0.853], accuracy = 0.889, recall = 0.441, precision = 0.475, F1 score = 0.458, Kappa = 0.396 and Matthews correlation coefficient = 0.396). Further model validation on the temporal scale using two longitudinal datasets also demonstrated good performance (AUC [95%CI]: 0.833 [0.818-0.848] and 0.852 [0.840-0.865], respectively). The model's built-in feature importance ranking and the SHapley Additive exPlanations method revealed that lifting 5 kg and running/jogging 1 km were relatively important variables among the 20 FC items contributing to the model's predictive capacity, respectively. The calibration curve of the model indicated good agreement between predictions and actual observations (Hosmer and Lemeshow p = 0.501, 0.451 and 0.374 for the three test sets, respectively), and decision curve analysis supported its clinical usefulness. The model was implemented as an online web application and exported as a deployable binary file, allowing for flexible, individualized risk assessment.

CONCLUSIONS

We developed an artificial intelligence model that can assist in the identification of sarcopenia, particularly in settings lacking the necessary resources for a comprehensive diagnosis. These findings offer potential for improving decision-making and facilitating the development of novel management strategies of sarcopenia.

Machine learning to identify precachexia and cachexia: a multicenter, retrospective cohort study

BACKGROUND

Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge.

OBJECTIVE

This study aimed to detect cancer precachexia using a simple method and distinguish the different characteristics of precachexia and cachexia.

METHODS

We included 3896 participants in this study. We used all baseline characteristics as input variables and trained machine learning (ML) models to calculate the importance of the variables. After filtering the variables based on their importance, the models were retrained. The best model was selected based on the receiver operating characteristic value. Subsequently, we used the same method and process to identify patients with precachexia in a noncachexia population using the same method and process.

RESULTS

Participants in this study included 2228 men (57.2%) and 1668 women (42.8%), of whom 471 were diagnosed with precachexia, 1178 with cachexia, and the remainder with noncachexia. The most important characteristics of cachexia were eating changes, arm circumference, high-density lipoprotein (HDL) level, and C-reactive protein albumin ratio (CAR). The most important features distinguishing precachexia were eating changes, serum creatinine, HDL, handgrip strength, and CAR. The two logistic regression models for screening for cachexia and diagnosing precachexia had the highest area under the curve values of 0.830 and 0.701, respectively. Calibration and decision curves showed that the models had good accuracy.

CONCLUSION

We developed two models for identifying precachexia and cachexia, which will help clinicians detect and diagnose precachexia.

Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China

BACKGROUND

Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators.

METHODS

Patients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients.

RESULTS

A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis.

CONCLUSION

The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients.

Exploring the optimal indicator of short-term peridiagnosis weight dynamics to predict cancer survival: A multicentre cohort study

BACKGROUND

Body weight and its changes have been associated with cancer outcomes. However, the associations of short-term peridiagnosis weight dynamics in standardized, clinically operational time frames with cancer survival remain largely unknown. This study aimed to screen for and evaluate the optimal indicator of short-term peridiagnosis weight dynamics to predict overall survival (OS) in patients with cancer.

METHODS

This multicentre cohort study prospectively collected data from 7460 patients pathologically diagnosed with cancer between 2013 and 2019. Body weight data were recorded 1 month before, at the time of and 1 month following diagnosis. By permuting different types (point value in kg, point height-adjusted value in kg/m2, absolute change in kg or relative change in percentage) and time frames (prediagnosis, postdiagnosis or peridiagnosis), we generated 12 different weight-related indicators and compared their prognostic performance using Harrell's C-index, integrated discrimination improvement, continuous net reclassification improvement and time-dependent C-index. We analysed associations of peridiagnosis relative weight change (RWC) with OS using restricted cubic spine (RCS), Kaplan-Meier analysis and multivariable-adjusted Cox regression models.

RESULTS

The study enrolled 5012 males and 2448 females, with a median age of 59 years. During a median follow-up of 37 months, 1026 deaths occurred. Peridiagnosis (1 month before diagnosis to 1 month following diagnosis) RWC showed higher prognostic performance (Harrell's C-index = 0.601, 95% confidence interval [CI] = [0.583, 0.619]) than other types of indicators including body mass index (BMI), absolute weight change, absolute BMI change, prediagnosis RWC and postdiagnosis RWC in the study population (all P < 0.05). Time-dependent C-index analysis also indicated that peridiagnosis RWC was optimal for predicting OS. The multivariable-adjusted RCS analysis revealed an N-shaped non-linear association between peridiagnosis RWC and OS (PRWC < 0.001, Pnon-linear < 0.001). Univariate survival analysis showed that the peridiagnosis RWC groups could represent distinct mortality risk stratifications (P < 0.001). Multivariable survival analysis showed that, compared with the maintenance group (weight change < 5%), the significant (gain >10%, hazard ratio [HR] = 0.530, 95% CI = [0.413, 0.680]) and moderate (gain 5-10%, HR = 0.588, 95% CI = [0.422, 0.819]) weight gain groups were both associated with improved OS. In contrast, the moderate (loss 5-10%, HR = 1.219, 95% CI = [1.029, 1.443]) and significant (loss >10%, HR = 1.280, 95% CI = [1.095, 1.497]) weight loss groups were both associated with poorer OS.

CONCLUSIONS

The prognostic performance of peridiagnosis RWC is superior to other weight-related indicators in patients with cancer. The findings underscore the importance of expanding the surveillance of body weight from at diagnosis to both past and future, and conducting it within clinically operational time frames, in order to identify and intervene with patients who are at risk of weight change-related premature deaths.

The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information

BACKGROUND & AIMS

The key step of the Global Leadership Initiative on Malnutrition (GLIM) is nutritional risk screening, while the most appropriate screening tool for colorectal cancer (CRC) patients is yet unknown. The GLIM diagnosis relies on weight loss information, and bias or even failure to recall patients' historical weight can cause misestimates of malnutrition. We aimed to compare the suitability of several screening tools in GLIM diagnosis, and establish machine learning (ML) models to predict malnutrition in CRC patients without weight loss information.

METHODS

This multicenter cohort study enrolled 4487 CRC patients. The capability of GLIM diagnoses combined with four screening tools in predicting survival probability was compared by Kaplan-Meier curves, and the most accurate one was selected as the malnutrition reference standard. Participants were randomly assigned to a training cohort (n = 3365) and a validation cohort (n = 1122). Several ML approaches were adopted to establish models for predicting malnutrition without weight loss data. We estimated feature importance and reserved the top 30% of variables for retraining simplified models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess and compare model performance.

RESULTS

NRS-2002 was the most suitable screening tool for GLIM diagnosis in CRC patients, with the highest hazard ratio (1.59; 95% CI, 1.43-1.77). A total of 2076 (46.3%) patients were malnourished diagnosed by GLIM combined with NRS-2002. The simplified random forest (RF) model outperformed other models with an AUC of 0.830 (95% CI, 0.805-0.854), and accuracy, sensitivity and specificity were 0.775, 0.835 and 0.742, respectively. We deployed an online application based on the simplified RF model to accurately estimate malnutrition probability in CRC patients without weight loss information (https://zzuwtt1998.shinyapps.io/dynnomapp/).

CONCLUSIONS

Nutrition Risk Screening 2002 was the optimal initial nutritional risk screening tool in the GLIM process. The RF model outperformed other models, and an online prediction tool was developed to properly identify patients at high risk of malnutrition.

Bridging gaps in cancer cachexia Care: Current insights and future perspectives

Cachexia is characterized by severe weight loss and skeletal muscle depletion, and is a threat to cancer patients by worsening their prognosis. International guidelines set indications for the screening and diagnosis of cancer cachexia and suggest interventions (nutritional support, physical exercise, and pharmacological treatments). Nevertheless, real-life experience not always aligns with such indications. We aimed to review the current state of the field and the main advancements, with a focus on real-life clinical practice from the perspectives of oncologists, nutrition professionals, and radiologists. Pragmatic solutions are proposed to improve the current management of the disease, emphasizing the importance of increasing awareness of clinical nutrition's benefits, fostering multidisciplinary collaboration, promoting early identification of at-risk patients, and leveraging available resources. Given the distinct needs of patients who are receiving oncologic anti-cancer treatments and those in the follow-up phase, the use of tailored approaches is encouraged. The pivotal role of healthcare professionals in managing patients in active treatment is highlighted, while patient and caregiver empowerment should be strengthened in the follow-up phase. Telemedicine and web-based applications represent valuable tools for continuous monitoring of patients, facilitating timely and personalized intervention through effective communication between patients and healthcare providers. These actions can potentially improve the outcomes, well-being, and survival of cancer patients with cachexia.

Longitudinal Muscle Biopsies Reveal Inter- and Intra-Subject Variability in Cancer Cachexia: Paving the Way for Biopsy-Guided Tailored Treatment

In the rapidly evolving landscape of cancer cachexia research, the development and refinement of diagnostic and predictive biomarkers constitute an ongoing challenge. This study aims to introduce longitudinal muscle biopsies as a potential framework for disease monitoring and treatment. The initial feasibility and safety assessment was performed for healthy mice and rats that received two consecutive muscle biopsies. The assessment was performed by utilizing three different tools. Subsequently, the protocol was also applied in leiomyosarcoma tumor-bearing rats. Longitudinal muscle biopsies proved to be a safe and feasible technique, especially in rat models. The application of this protocol to tumor-bearing rats further affirmed its tolerability and feasibility, while microscopic evaluation of the biopsies demonstrated varying levels of muscle atrophy with or without leukocyte infiltration. In this tumor model, sequential muscle biopsies confirmed the variability of the cancer cachexia evolution among subjects and at different time-points. Despite the abundance of promising cancer cachexia data during the past decade, the full potential of muscle biopsies is not being leveraged. Sequential muscle biopsies throughout the disease course represent a feasible and safe tool that can be utilized to guide precision treatment and monitor the response in cancer cachexia research.

Impact of Nutritional Status of Patients with Head and Neck Squamous Cell Carcinoma on the Expression Profile of Ghrelin, Irisin, and Titin

The goal of this paper was the evaluation of the changes in the expression profile of irisin, ghrelin, and titin in the carcinoma tissue and in the blood of patients with head and neck squamous cell carcinoma (HNSCC), including determining the profile of their expression in relation to patient nutrition. The study included 56 patients with diagnosed squamous cell carcinoma of HNSCC in the T3 and T4 stages of the disease. Healthy control tissue specimens were collected from an area 10 mm outside the histologically negative margin. In turn, the blood and serum from the control group came from healthy volunteers treated for non-oncologic reasons (n = 70). The molecular analysis allowed us to determine the profile of irisin, ghrelin, and titin methylation, evaluate their expression on the level of mRNA (quantitative Reverse Transcription Polymerase Chain Reaction; qRT-PCR) and protein (Enzyme-Linked Immunosorbent Assay Reaction; ELISA) in the carcinoma tissue and the margin of healthy tissue, as well as in serum of patients in the study and control groups. At the start of our observations, a Body Mass Index (BMI) < 18.5 was noted in 42 of the patients, while six months after the treatment a BMI < 18.5 was noted in 29 patients. We also noted a decrease in the expression of irisin, ghrelin, and titin both on the level of mRNA and protein, as well as a potential regulation of their expression via DNA methylation. There is no convincing evidence that the proteins assayed in the present work are specific with regard to HNSSC.

Association of possible sarcopenia with all-cause mortality in patients with solid cancer: A nationwide multicenter cohort study

OBJECTIVES

The concept of possible sarcopenia (PS) was recently introduced to enable timely intervention in settings without the technologies required to make a full diagnosis of sarcopenia. This study aimed to investigate the association between PS and all-cause mortality in patients with solid cancer.

DESIGN

Retrospective observational study.

SETTING AND PARTICIPANTS

13,736 patients with 16 types of solid cancer who were ≥18 years old.

MEASUREMENTS

The presence of both a low calf circumference (men <34 cm or women <33 cm) and low handgrip strength (men <28 kg or women <18 kg) was considered to indicate PS. Harrell's C-index was used to assess prognostic value and the association of PS with mortality was estimated by calculating multivariable-adjusted hazard ratios (HRs).

RESULTS

The study enrolled 7207 men and 6529 women (median age = 57.8 years). During a median follow-up of 43 months, 3150 deaths occurred. PS showed higher Harrell's C-index (0.549, 95%CI = [0.541, 0.557]) than the low calf circumference (0.541, 95%CI = [0.531, 0.551], P = 0.037) or low handgrip strength (0.542, 95%CI = [0.532, 0.552], P = 0.026). PS was associated with increased mortality risk in both univariate (HR = 1.587, 95%CI = [1.476, 1.708]) and multivariable-adjusted models (HR = 1.190, 95%CI = [1.094, 1.293]). Sensitivity analyses showed that the association of PS with mortality was robust in different covariate subgroups, which also held after excluding those patients who died within the first 3 months (HR = 1.162, 95%CI = [1.060, 1.273]), 6 months (HR = 1.150, 95%CI = [1.039, 1.274]) and 12 months (HR = 1.139, 95%CI = [1.002, 1.296]) after enrollment.

CONCLUSION

PS could independently and robustly predict all-cause mortality in patients with solid cancer. These findings imply the importance of including PS assessment in routine cancer care to provide significant prognostic information to help mitigate sarcopenia-related premature deaths.

Development and validation of an online dynamic nomogram system for predicting cancer cachexia among inpatients: a real-world cohort study in China

BACKGROUND

Early recognition of cachexia is essential for ensuring the prompt intervention and treatment of cancer patients. However, the diagnosis of cancer cachexia (CC) usually is delayed. This study aimed to establish an accurate and high-efficiency diagnostic system for CC.

METHODS

A total of 4834 cancer inpatients were enrolled in the INSCOC project from July 2013 to June 2020. All cancer patients in the study were randomly assigned to a development cohort (n=3384, 70%) and a validation cohort (n=1450, 30%). The least absolute shrinkage and selection operator (LASSO) method and multivariable logistic regression were used to identify the independent predictors for developing the dynamic nomogram. Discrimination and calibration were adopted to evaluate the ability of nomogram. A decision curve analysis (DCA) was used to evaluate clinical use.

RESULTS

We combined 5 independent predictive factors (age, NRS2002, PG-SGA, QOL by the QLQ-C30, and cancer categories) to establish the online dynamic nomogram system. The C-index, sensitivity, and specificity of the nomo-system to predict CC was 0.925 (95%CI, 0.916-0.934, P < 0.001), 0.826, and 0.862 in the development set, while the values were 0.923 (95%CI, 0.909-0.937, P < 0.001), 0.854, and 0.829 in the validation set. In addition, the calibration curves of the diagnostic nomogram also presented good agreement with the actual situation. DCA showed that the model is clinically useful and can increase the clinical benefit in cancer patients.

CONCLUSIONS

This study developed an online dynamic nomogram system with outstanding accuracy to help clinicians and dieticians estimate the probability of cachexia. This simple-to-use online nomogram can increase the clinical benefit in cancer patients and is expected to be widely adopted.