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Cancer Management and Research Volume 14, 2022 - Issue
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Original Research

A Predictive Model for Qualitative Evaluation of PG-SGA in Tumor Patients Through Machine Learning

Xiangliang Liu1 Cancer Center, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of China
,
Yuguang Li2 College of Instrumentation and Electrical Engineering, Jilin University, Changchun, Jilin, People’s Republic of China
,
Wei Ji1 Cancer Center, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of China
,
Kaiwen Zheng1 Cancer Center, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of ChinaORCID Iconhttps://orcid.org/0000-0001-9801-4897
ORCID Icon,
Jin Lu1 Cancer Center, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of China
,
Yixin Zhao1 Cancer Center, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of China
,
Wenxin Zhang3 Department of Cancer Radiotherapy and Chemotherapy, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China
,
Mingyang Liu2 College of Instrumentation and Electrical Engineering, Jilin University, Changchun, Jilin, People’s Republic of ChinaCorrespondence[email protected]
,
Jiuwei Cui1 Cancer Center, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of China
&
Wei Li1 Cancer Center, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of ChinaCorrespondence[email protected]
 show all
Pages 1431-1441 | Published online: 12 Apr 2022

References

  • Bauer J, Capra S, Ferguson M. Use of the scored Patient-Generated Subjective Global Assessment (PG-SGA) as a nutrition assessment tool in patients with cancer. Eur J Clin Nutr. 2002;56(8):779–785. doi:10.1038/sj.ejcn.1601412
  • Achilonu OJ, Fabian J, Bebington B, et al. Predicting colorectal cancer recurrence and patient survival using supervised machine learning approach: a South African population-based study. Front Public Health. 2021;9:694306. doi:10.3389/fpubh.2021.694306
  • Huang Y, Chen W, Zhang X, et al. Prediction of tumor shrinkage pattern to neoadjuvant chemotherapy using a multiparametric MRI-based machine learning model in patients with breast cancer. Front Bioeng Biotechnol. 2021;9:662749. doi:10.3389/fbioe.2021.662749
  • Wen R, Zheng K, Zhang Q, et al. Machine learning-based random forest predicts anastomotic leakage after anterior resection for rectal cancer. J Gastrointest Oncol. 2021;12(3):921–932. doi:10.21037/jgo-20-436
  • Kang J, Choi YJ, Kim I-K, et al. LASSO-based machine learning algorithm for prediction of lymph node metastasis in T1 colorectal cancer. Cancer Res Treat. 2021;53(3):773–783. doi:10.4143/crt.2020.974
  • Mateu-de Antonio J, Echeverria-Esnal D, Barceló-Vidal J, et al. Propensity-score matched comparative study on effects of intravenous human serum albumin administration in critically Ill adult patients receiving parenteral nutrition. JPEN J Parenter Enteral Nutr. 2019;43(3):357–363. doi:10.1002/jpen.1425
  • Ozmen S, Timur O, Calik I, et al. Neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) may be superior to C-reactive protein (CRP) for predicting the occurrence of differentiated thyroid cancer. Endocr Regul. 2017;51(3):131–136. doi:10.1515/enr-2017-0013
  • de van der Schueren MAE, de Smoker M, Leistra E, et al. The association of weight loss with one-year mortality in hospital patients, stratified by BMI and FFMI subgroups. Clin Nutr. 2018;37(5):1518–1525. doi:10.1016/j.clnu.2017.08.024
  • Rabito EI, Marcadenti A, da Silva Fink J, Figueira L, Silva FM. Nutritional risk screening 2002, short nutritional assessment questionnaire, malnutrition screening tool and malnutrition universal screening tool are good predictors of nutrition risk in an emergency service. Nutr Clin Pract. 2017;32(4):526–532.
  • Jager-Wittenaar H, Ottery FD. Assessing nutritional status in cancer: role of the patient-generated subjective global assessment. Curr Opin Clin Nutr Metab Care. 2017;20(5):322–329. doi:10.1097/MCO.0000000000000389
  • Jensen GL, Cederholm T, Correia MITD, et al. GLIM criteria for the diagnosis of malnutrition: a consensus report from the Global Clinical Nutrition Community. J Parenter Enteral Nutr. 2019;43(1):32–40. doi:10.1002/jpen.1440
  • Jin S, Lu Q, Sun Y, et al. Nutrition impact symptoms and weight loss in head and neck cancer during radiotherapy: a longitudinal study. BMJ Support Palliat Care. 2021;11(1):17–24. doi:10.1136/bmjspcare-2019-002077
  • Farhangfar A, Makarewicz M, Ghosh S, et al. Nutrition impact symptoms in a population cohort of head and neck cancer patients: multivariate regression analysis of symptoms on oral intake, weight loss and survival. Oral Oncol. 2014;50(9):877–883. doi:10.1016/j.oraloncology.2014.06.009
  • Ganzer H, Touger-Decker R, Parrott JS, et al. Symptom burden in head and neck cancer: impact upon oral energy and protein intake. Support Care Cancer. 2013;21(2):495–503. doi:10.1007/s00520-012-1542-4
  • Kubrak C, Olson K, Jha N, et al. Nutrition impact symptoms: key determinants of reduced dietary intake, weight loss, and reduced functional capacity of patients with head and neck cancer before treatment. Head Neck. 2010;32(3):290–300. doi:10.1002/hed.21174
  • Ivanova GS, Golovkov AA, Petrova IS, et al. Withdrawal notice: investigation of the use of neural networks for diagnosing breast cancer on mammograms. Curr Med Imaging. 2021;17. doi:10.2174/1573405617666210707155835
  • Piva F, Tartari F, Giulietti M, et al. Predicting future cancer burden in the United States by artificial neural networks. Future Oncol. 2021;17(2):159–168. doi:10.2217/fon-2020-0359

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