Development and external validation of a machine learning-based prediction model for the cancer-related fatigue diagnostic screening in adult cancer patients: a cross-sectional study in China

Du, Lin; Du, Junjia; Yang, Min; Xu, Qinqin; Huang, Jiannan; Tan, Wenxi; Wang, Lisheng; Nie, Wenbo; Zhao, Lijing

doi:10.1007/s00520-022-07570-w

Cited by 7 publications

(5 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of the 42 included studies, 10 unique symptoms were reported as outcome variables in the predictions. Those included were xerostomia (9/42, 14%) [ 27 , 30 , 34 , 49 - 51 , 55 , 58 , 60 ], depression (8/42, 13%) [ 22 , 33 , 37 , 41 , 43 , 45 , 52 , 59 ], pain (8/42, 13%) [ 20 , 25 , 26 , 35 , 37 , 40 , 42 , 56 ], fatigue (6/42, 10%) [ 23 , 24 , 29 , 38 , 42 , 44 ], anxiety (3/42, 5%) [ 33 , 46 , 52 ], sleep disturbance or insomnia (3/42, 5%) [ 26 , 28 , 52 ], nausea or vomiting (3/42, 5%) [ 17 , 26 , 29 ], weight loss (2/42, 3%) [ 47 , 53 ], cognitive impairment (2/42, 3%) [ 21 , 36 ], and diarrhea (2/42, 3%) [ 29 , 42 ].…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Zeinali,

Youn,

Albashayreh

et al. 2024

JMIR Cancer

View full text Add to dashboard Cite

Background People with cancer frequently experience severe and distressing symptoms associated with cancer and its treatments. Predicting symptoms in patients with cancer continues to be a significant challenge for both clinicians and researchers. The rapid evolution of machine learning (ML) highlights the need for a current systematic review to improve cancer symptom prediction. Objective This systematic review aims to synthesize the literature that has used ML algorithms to predict the development of cancer symptoms and to identify the predictors of these symptoms. This is essential for integrating new developments and identifying gaps in existing literature. Methods We conducted this systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. We conducted a systematic search of CINAHL, Embase, and PubMed for English records published from 1984 to August 11, 2023, using the following search terms: cancer, neoplasm, specific symptoms, neural networks, machine learning, specific algorithm names, and deep learning. All records that met the eligibility criteria were individually reviewed by 2 coauthors, and key findings were extracted and synthesized. We focused on studies using ML algorithms to predict cancer symptoms, excluding nonhuman research, technical reports, reviews, book chapters, conference proceedings, and inaccessible full texts. Results A total of 42 studies were included, the majority of which were published after 2017. Most studies were conducted in North America (18/42, 43%) and Asia (16/42, 38%). The sample sizes in most studies (27/42, 64%) typically ranged from 100 to 1000 participants. The most prevalent category of algorithms was supervised ML, accounting for 39 (93%) of the 42 studies. Each of the methods—deep learning, ensemble classifiers, and unsupervised ML—constituted 3 (3%) of the 42 studies. The ML algorithms with the best performance were logistic regression (9/42, 17%), random forest (7/42, 13%), artificial neural networks (5/42, 9%), and decision trees (5/42, 9%). The most commonly included primary cancer sites were the head and neck (9/42, 22%) and breast (8/42, 19%), with 17 (41%) of the 42 studies not specifying the site. The most frequently studied symptoms were xerostomia (9/42, 14%), depression (8/42, 13%), pain (8/42, 13%), and fatigue (6/42, 10%). The significant predictors were age, gender, treatment type, treatment number, cancer site, cancer stage, chemotherapy, radiotherapy, chronic diseases, comorbidities, physical factors, and psychological factors. Conclusions This review outlines the algorithms used for predicting symptoms in individuals with cancer. Given the diversity of symptoms people with cancer experience, analytic approaches that can handle complex and nonlinear relationships are critical. This knowledge can pave the way for crafting algorithms tailored to a specific symptom. In addition, to improve prediction precision, future research should compare cutting-edge ML strategies such as deep learning and ensemble methods with traditional statistical models.

show abstract

Section: Resultsmentioning

confidence: 99%

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Zeinali,

Youn,

Albashayreh

et al. 2024

JMIR Cancer

View full text Add to dashboard Cite

show abstract

“…However, further research in biomarker profiles considering a larger range of parameters as well as machine learning techniques may be promising approaches 35 . First attempts to develop fatigue prediction models in cancer patients based on clinical or genetic data are published 41,42 . However, as fatigue is a subjective experience that might be best estimated by the patients themselves, prediction of general fatigue might be rather less relevant for the patients than the specification of the fatigue subtype with a subsequent targeted treatment.…”

Section: Discussionmentioning

confidence: 99%

Cancer‐related fatigue: Towards a more targeted approach based on classification by biomarkers and psychological factors

Schmidt,

Maurer,

Behrens

et al. 2023

Intl Journal of Cancer

View full text Add to dashboard Cite

Cancer‐related fatigue is a frequent, burdensome and often insufficiently treated symptom. A more targeted treatment of fatigue is urgently needed. Therefore, we examined biomarkers and clinical factors to identify fatigue subtypes with potentially different pathophysiologies. The study population comprised disease‐free breast cancer survivors of a German population‐based case‐control study who were re‐assessed on average 6 (FU1, n = 1871) and 11 years (FU2, n = 1295) after diagnosis. At FU1 and FU2, we assessed fatigue with the 20‐item multidimensional Fatigue Assessment Questionnaire and further factors by structured telephone‐interviews. Serum samples collected at FU1 were analyzed for IL‐1ß, IL‐2, IL‐4, IL‐6, IL‐10, TNF‐a, GM‐CSF, IL‐5, VEGF‐A, SAA, CRP, VCAM‐1, ICAM‐1, leptin, adiponectin and resistin. Exploratory cluster analyses among survivors with fatigue at FU1 and no history of depression yielded three clusters (CL1, CL2 and CL3). CL1 (n = 195) on average had high levels of TNF‐α, IL1‐β, IL‐6, resistin, VEGF‐A and GM‐CSF, and showed high BMI and pain levels. Fatigue in CL1 manifested rather in physical dimensions. Contrarily, CL2 (n = 78) was characterized by high leptin level and had highest cognitive fatigue. CL3 (n = 318) did not show any prominent characteristics. Fatigued survivors with a history of depression (n = 214) had significantly higher physical, emotional and cognitive fatigue and showed significantly less amelioration of fatigue from FU1 to FU2 than survivors without depression. In conclusion, from the broad phenotype “cancer‐related fatigue” we were able to delineate subgroups characterized by biomarkers or history of depression. Future investigations may take these subtypes into account, ultimately enabling a better targeted therapy of fatigue.

show abstract

“…The data set for the cross-validation consists of training sets and test sets. The model first learns the classification on the training set [36,37]. The test set can measure the performance of the classification.…”

Section: K-fold Cross-validationmentioning

confidence: 99%

Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis

Tang

2023

EAI Endorsed Trans e-Learn

View full text Add to dashboard Cite

As the widespread transmission of COVID-19 has continued to influence human health since late 2019, more intersections between artificial intelligence and the medical field have arisen. For CT images, manual differentiation between COVID-19-infected and healthy control images is not as effective and fast as AI. This study performed experiments on a dataset containing 640 samples, 320 of which were COVID-19-infected, and the rest were healthy controls. This experiment combines the gray-level co-occurrence matrix (GLCM) and random vector function link (RVFL). The role of GLCM and RVFL is to extract image features and classify images, respectively. The experimental results of my proposed GLCM-RVFL model are validated using K-fold cross-validation, and the indicators are 78.81±1.75%, 77.08±0.68%, 77.46±0.73%, 54.22±1.35%, and 77.48±0.74% for sensitivity, accuracy, F1-score, MCC, and FMI, respectively, which also confirms that the proposed model performs well on the COVID-19 detection task. After comparing with six state-of-the-art COVID-19 detection, I ensured that my model achieved higher performance.

show abstract

Development and external validation of a machine learning-based prediction model for the cancer-related fatigue diagnostic screening in adult cancer patients: a cross-sectional study in China

Cited by 7 publications

References 48 publications

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Cancer‐related fatigue: Towards a more targeted approach based on classification by biomarkers and psychological factors

Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis

Contact Info

Product

Resources

About