Machine learning model for predicting excessive muscle loss during neoadjuvant chemoradiotherapy in oesophageal cancer

Long-term nutrition in patients candidate to neoadjuvant and adjuvant treatments

To improve outcomes, to decrease the rate of local recurrence and development of distant metastases neoadjuvant and adjuvant therapies are employed in cancer patients in forms of radiation, chemo-, endocrine-, targeted-, and immunotherapy or their combination. Nutrition therapy plays important role in all phases of the cancer journey. From neoadjuvant therapy to prehabilitation, early postoperative nutrition, and long-term nutrition care during the adjuvant phase and survivorship determines the survival and quality of life of cancer patients. During the neoadjuvant phase patients may be in poor nutritional condition which can be aggravated by the applied oncological treatment. Beside this apparent threat this period also gives an excellent opportunity to maintain or even improve the nutritional status of the patients by nutrition therapy. After surgery the burdening effects of the operation may jeopardize the execution of adjuvant therapy. After early postoperative feeding a long-term nutrition strategy should be developed for cancer patients in order to avoid nutritional deterioration during the usually lengthy postoperative therapy. In this narrative review we discuss how preoperative nutritional status and medical nutrition therapy influence the results of surgery and after the operation what is the available evidence about nutritional status and outcome and the potentials to influence them by nutrition therapy.

Proteomics and Machine Learning in the Prediction and Explanation of Low Pectoralis Muscle Area

Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual's risk for developing low muscle mass using proteomics and machine learning. We identified 8 biomarkers associated with low pectoralis muscle area (PMA). We built 3 random forest classification models that used either clinical measures, feature selected biomarkers, or both to predict development of low PMA. The area under the receiver operating characteristic curve for each model was: clinical-only = 0.646, biomarker-only = 0.740, and combined = 0.744. We displayed the heterogenetic nature of an individual's risk for developing low PMA and identified 2 distinct subtypes of participants who developed low PMA. While additional validation is required, our methods for identifying and understanding individual and group risk for low muscle mass could be used to enable developments in the personalized prevention of low muscle mass.

Design of risk prediction model for esophageal cancer based on machine learning approach

Background and aim

Esophageal cancer (EC) is a highly prevalent and progressive disease. Early prediction of EC risk in the population is crucial in preventing this disease and enhancing the overall health of individuals. So far, few studies have been conducted on predicting the EC risk based on the prediction models, and most of them focused on statistical methods. The ML approach obtained efficient predictive insights into the clinical domain. Therefore, this study aims to develop a risk prediction model for EC based on risk factors and by leveraging the ML approach to stratify the high-risk EC people and obtain efficient preventive purposes at the community level.

Material and methods

The current retrospective study was performed from 2018 to 2022 in Sari City based on 3256 EC and non-EC cases. The six selected algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XG-Boost), Bagging, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs), were used to develop the risk prediction model for EC and achieve the preventive purposes.

Results

Comparing the performance efficiency of algorithms revealed that the XG-Boost model gained the best predictability for EC risk with AU-ROC = 0.92 and AU-ROC-test = 0.889 for internal and validation states, respectively. Based on the XG-Boost, the factors, including sex, drinking hot liquids, fruit consumption, achalasia, and vegetable consumption, were considered the five top predictors of EC risk.

Conclusion

This study showed that the XG-Boost could provide insight into the early prediction of the EC risk for people and clinical providers to stratify the high-risk group of EC and achieve preventive measures based on modifying the risk factors associated with EC and other clinical solutions.

Skeletal muscle gauge prediction by a machine learning model in patients with colorectal cancer

OBJECTIVES

Skeletal muscle gauge (SMG) was recently introduced as an imaging indicator of sarcopenia. Computed tomography is essential for measuring SMG; thus, the use of SMG is limited to patients who undergo computed tomography. We aimed to develop a machine learning algorithm using clinical and inflammatory markers to predict SMG in patients with colorectal cancer.

METHODS

The least absolute shrinkage and selection operator regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of the least absolute shrinkage and selection operator model, defined as linear predictor (LP)-SMG, was compared using the area under the receiver operating characteristic curve and decision curve analysis in the test set.

RESULTS

A total of 1094 patients with colorectal cancer were enrolled and randomly categorized into training (n = 656) and test (n = 438) sets. Low SMG was identified in 142 (21.6%) and 90 (20.5%) patients in the training and test sets, respectively. According to multivariable analysis of the test sets, LP-SMG was identified as an independent predictor of low SMG (odds ratio = 1329.431; 95% CI, 271.684-7667.996; P < .001). Its predictive performance was similar in the training and test sets (area under the receiver operating characteristic curve = 0.846 versus 0.869; P = .427). In the test set, LP-SMG had better outcomes in predicting SMG than single clinical variables, such as sex, height, weight, and hemoglobin.

CONCLUSIONS

LP-SMG had superior performance than single variables in predicting low SMG. This machine learning model can be used as a screening tool to detect sarcopenic status without using computed tomography during the treatment period.

Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer

BACKGROUND

Skeletal muscle loss during treatment is associated with poor survival outcomes in patients with ovarian cancer. Although changes in muscle mass can be assessed on computed tomography (CT) scans, this labour-intensive process can impair its utility in clinical practice. This study aimed to develop a machine learning (ML) model to predict muscle loss based on clinical data and to interpret the ML model by applying SHapley Additive exPlanations (SHAP) method.

METHODS

This study included the data of 617 patients with ovarian cancer who underwent primary debulking surgery and platinum-based chemotherapy at a tertiary centre between 2010 and 2019. The cohort data were split into training and test sets based on the treatment time. External validation was performed using 140 patients from a different tertiary centre. The skeletal muscle index (SMI) was measured from pre- and post-treatment CT scans, and a decrease in SMI ≥ 5% was defined as muscle loss. We evaluated five ML models to predict muscle loss, and their performance was determined using the area under the receiver operating characteristic curve (AUC) and F1 score. The features for analysis included demographic and disease-specific characteristics and relative changes in body mass index (BMI), albumin, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). The SHAP method was applied to determine the importance of the features and interpret the ML models.

RESULTS

The median (inter-quartile range) age of the cohort was 52 (46-59) years. After treatment, 204 patients (33.1%) experienced muscle loss in the training and test datasets, while 44 (31.4%) patients experienced muscle loss in the external validation dataset. Among the five evaluated ML models, the random forest model achieved the highest AUC (0.856, 95% confidence interval: 0.854-0.859) and F1 score (0.726, 95% confidence interval: 0.722-0.730). In the external validation, the random forest model outperformed all ML models with an AUC of 0.874 and an F1 score of 0.741. The results of the SHAP method showed that the albumin change, BMI change, malignant ascites, NLR change, and PLR change were the most important factors in muscle loss. At the patient level, SHAP force plots demonstrated insightful interpretation of our random forest model to predict muscle loss.

CONCLUSIONS

Explainable ML model was developed using clinical data to identify patients experiencing muscle loss after treatment and provide information of feature contribution. Using the SHAP method, clinicians may better understand the contributors to muscle loss and target interventions to counteract muscle loss.

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey

This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use.

Normative values and integrated score of functional fitness among Chinese community-dwelling older adults in Suzhou

Objectives: This study was performed to establish the normative values and integrated score of the functional fitness on the basis of the senior fitness test (SFT) among Chinese community-dwelling older adults in Suzhou. Methods: In this cross-sectional descriptive study, 1,122 community-dwelling older adults aged 60 years old and above were recruited at Suzhou, China, by using a multistage stratified sampling method and accepted the SFT measurements. Sex- and age-specific normative values of each index of the SFT were established by using the percentile method. The SFT integrated score was established using factor analysis according to the data of 70% of the participants (construction group) and verified using the error rate from the data of the remaining 30% of the participants (verification group). Results: Normative-referenced percentile values at the 5th, 10th, 25th, 35th, 50th, 65th, 75th, 90th, and 95th percentiles for each index of SFT were established for the men and women among the different age groups. Five indices of the SFT, namely, 2-min step test, 30-s arm curl, 30-s chair stand, chair sit-and-reach, and 8-ft up-and-go (TUGT), gradually declined with age in both sexes (p < .05). The SFT integrated score was calculated as follows: F = 3.8 × 2-min step test + 3.8 × 30-s arm curl + 3.8 × 30-s chair stand + 2.2 × back starch + 2.6 × chair sit-and-reach + 4 × TUGT - .04 × BMI. The formula was verified using the error rate. The error rates of the verification group compared with the construction group in each grade score of SFT were lower than 5%. Conclusion: Based on the data from the community-dwelling older adults in Suzhou, China, the functional fitness normative values for each index of the SFT and the integrated score of SFT were established. The SFT integrated score formula was verified to be reasonable and effective.

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.