*Result*: Applications of machine learning and natural language processing to neurocognitive outcomes in posttreatment cancer survivors: a scoping review.
Title:
Applications of machine learning and natural language processing to neurocognitive outcomes in posttreatment cancer survivors: a scoping review.
Authors:
Alfaro S; Yale University, New Haven, CT, USA. salome.alfaro@yale.edu., Liu J; Yale University, New Haven, CT, USA., Naranjo Ortiz C; Yale Cancer Center, Yale University, New Haven, CT, USA., Alfaro A; Duke University, Durham, NC, USA., Lustberg M; Medical Oncology, Yale Cancer Center, Yale Medicine, New Haven, CT, USA.
Source:
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer [Support Care Cancer] 2026 Jan 06; Vol. 34 (1), pp. 71. Date of Electronic Publication: 2026 Jan 06.
Publication Type:
Journal Article; Scoping Review
Language:
English
Journal Info:
Publisher: Springer International Country of Publication: Germany NLM ID: 9302957 Publication Model: Electronic Cited Medium: Internet ISSN: 1433-7339 (Electronic) Linking ISSN: 09414355 NLM ISO Abbreviation: Support Care Cancer Subsets: MEDLINE
Imprint Name(s):
Original Publication: Berlin : Springer International, c1993-
MeSH Terms:
References:
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Van Dyk K, Ganz PA (2021) Cancer-related cognitive impairment in patients with a history of breast cancer. JAMA 326(17):1736–1737. (PMID: 3465242410.1001/jama.2021.13309)
Duan R, Wen Z, Zhang T, Liu J, Feng T, Ren T (2025) Advances in risk prediction models for cancer-related cognitive impairment. Clin Exp Med 25(1):74. (PMID: 400479521188531910.1007/s10238-025-01590-6)
Toledano N, Donison V, Sigal A, Mayo S, Alibhai SMH, Puts M (2025) Prevalence of pre-existing cognitive impairment in patients treated for cancer and the impact of cancer treatment on cognitive outcomes: a scoping review. J Geriatr Oncol 16(4):102235. (PMID: 4015848510.1016/j.jgo.2025.102235)
Ahles TA, Saykin AJ, McDonald BC, Li Y, Furstenberg CT, Hanscom BS et al (2010) Longitudinal assessment of cognitive changes associated with adjuvant treatment for breast cancer: impact of age and cognitive reserve. J Clin Oncol 28(29):4434–4440. (PMID: 20837957298863510.1200/JCO.2009.27.0827)
Guida JL, Ahles TA, Belsky D, Campisi J, Cohen HJ, DeGregori J et al (2019) Measuring aging and identifying aging phenotypes in cancer survivors. JNCI J Natl Cancer Inst 111(12):1245–1254. (PMID: 3132142610.1093/jnci/djz136)
Armenian SH, Gibson CJ, Rockne RC, Ness KK (2019) Premature aging in young cancer survivors. JNCI J Natl Cancer Inst 111(3):226–232. (PMID: 3071544610.1093/jnci/djy229)
Haywood D, Chan A, Chan RJ, Dauer E, Dhillon HM, Henneghan AM et al (2025) Accounting for unmet needs resulting from cancer-related cognitive impairment. J Cancer Surviv.
Sharafeldin N, Richman J, Bosworth A, Chen Y, Singh P, Patel SK et al (2020) Clinical and genetic risk prediction of cognitive impairment after blood or marrow transplantation for hematologic malignancy. J Clin Oncol 38(12):1312–1321. (PMID: 32083992826538710.1200/JCO.19.01085)
Chen VC-H, Lin T-Y, Yeh D-C, Chai J-W, Weng J-C (2020) Functional and structural connectome features for machine learning chemo-brain prediction in women treated for breast cancer with chemotherapy. Brain Sci 10(11):851. (PMID: 33198294769651210.3390/brainsci10110851)
Chen VC-H, Lin T-Y, Yeh D-C, Chai J-W, Weng J-C (2019) Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning. Magn Reson Med 81(5):3304–3313. (PMID: 3041793310.1002/mrm.27607)
Colliva C, Rivi V, Sarti P, Cobelli I, Blom JMC (2024) Exploring sex-based neuropsychological outcomes in pediatric brain cancer survivors: a pilot study. Diseases. https://doi.org/10.3390/diseases12110289. (PMID: 10.3390/diseases121102893958996311592787)
de Ruiter MB, Deardorff RL, Blommaert J, Chen BT, Dumas JA, Schagen SB et al (2023) Brain gray matter reduction and premature brain aging after breast cancer chemotherapy: a longitudinal multicenter data pooling analysis. Brain Imaging Behav 17(5):507–518. (PMID: 372564941065222210.1007/s11682-023-00781-7)
Henneghan AM, Palesh O, Harrison M, Kesler SR (2018) Identifying cytokine predictors of cognitive functioning in breast cancer survivors up to 10 years post chemotherapy using machine learning. J Neuroimmunol 320:38–47. (PMID: 29759139603068710.1016/j.jneuroim.2018.04.012)
Henneghan AM, Gibbons C, Harrison RA, Edwards ML, Rao V, Blayney DW et al (2020) Predicting patient reported outcomes of cognitive function using connectome-based predictive modeling in breast cancer. Brain Topogr 33(1):135–142. (PMID: 3174568910.1007/s10548-019-00746-4)
Henneghan A, Rao V, Harrison RA, Karuturi M, Blayney DW, Palesh O et al (2020) Cortical brain age from pre-treatment to post-chemotherapy in patients with breast cancer. Neurotox Res 37(4):788–799. (PMID: 31900898708981710.1007/s12640-019-00158-z)
Huang T, Ngan CK, Cheung YT, Marcotte M, Cabrera B (2025) A hybrid deep learning-based feature selection approach for supporting early detection of long-term behavioral outcomes in survivors of cancer: cross-sectional study. JMIR Bioinform Biotechnol 6:e65001. (PMID: 400808201195070010.2196/65001)
Kesler SR, Rao A, Blayney DW, Oakley-Girvan IA, Karuturi M, Palesh O (2017) Predicting long-term cognitive outcome following breast cancer with pre-treatment resting state fMRI and random forest machine learning. Front Hum Neurosci 11:555. (PMID: 29187817569482510.3389/fnhum.2017.00555)
Kesler SR, Petersen ML, Rao V, Harrison RA, Palesh O (2020) Functional connectome biotypes of chemotherapy-related cognitive impairment. J Cancer Surviv 14(4):483–493. (PMID: 32157609795831110.1007/s11764-020-00863-1)
Kesler SR, Henneghan AM, Thurman W, Rao V (2022) Identifying themes for assessing cancer-related cognitive impairment: topic modeling and qualitative content analysis of public online comments. JMIR Cancer 8(2):e34828. (PMID: 35612878917845010.2196/34828)
Kline C, Stoller S, Byer L, Samuel D, Lupo JM, Morrison MA et al (2022) An integrated analysis of clinical, genomic, and imaging features reveals predictors of neurocognitive outcomes in a longitudinal cohort of pediatric cancer survivors, enriched with CNS tumors (Rad ART Pro). Front Oncol 12:874317. (PMID: 35814456925998110.3389/fonc.2022.874317)
Lemos R, Areias-Marques S, Ferreira P, O’Brien P, Beltrán-Jaunsarás ME, Ribeiro G et al (2022) A prospective observational study for a federated artificial intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocol. BMC Psychiatry 22(1):817. (PMID: 36544126976903410.1186/s12888-022-04446-5)
Lin K-Y, Chen VC-H, Tsai Y-H, McIntyre RS, Weng J-C (2021) Classification and visualization of chemotherapy-induced cognitive impairment in volumetric convolutional neural networks. J Pers Med 11(10):1025. (PMID: 34683166853886210.3390/jpm11101025)
Lin P-H, Kuo P-H (2022) Ensemble learning based functional independence ability estimator for pediatric brain tumor survivors. Health Inform J 28(4):14604582221140976. (PMID: 10.1177/14604582221140975)
Luo X, Gandhi P, Storey S, Zhang Z, Han Z, Huang K (2021) A computational framework to analyze the associations between symptoms and cancer patient attributes post chemotherapy using EHR data. IEEE J Biomed Health Inform 25(11):4098–4109. (PMID: 3461392210.1109/JBHI.2021.3117238)
McDeed AP, Van Dyk K, Zhou X, Zhai W, Ahles TA, Bethea TN et al (2024) Prediction of cognitive decline in older breast cancer survivors: the thinking and living with cancer study. JNCI Cancer Spectr 8(2). https://doi.org/10.1093/jncics/pkae019.
Mulholland MM, Stuifbergen A, De La Torre Schutz A, Franco Rocha OY, Blayney DW, Kesler SR (2024) Evidence of compensatory neural hyperactivity in a subgroup of breast cancer survivors treated with chemotherapy and its association with brain aging. Front Aging Neurosci 16:1421703. (PMID: 397231531166869210.3389/fnagi.2024.1421703)
Zamanipoor Najafabadi AH, van der Meer PB, Boele FW, Taphoorn MJB, Klein M, Peerdeman SM et al (2021) Determinants and predictors for the long-term disease burden of intracranial meningioma patients. J Neurooncol 151(2):201–210. (PMID: 3307332610.1007/s11060-020-03650-1)
Oyefiade A, Erdman L, Goldenberg A, Malkin D, Bouffet E, Taylor MD et al (2019) PPAR and GST polymorphisms may predict changes in intellectual functioning in medulloblastoma survivors. J Neurooncol 142(1):39–48. (PMID: 3060770910.1007/s11060-018-03083-x)
Saito M, Hiramoto I, Yano M, Watanabe A, Kodama H (2022) Influence of self-efficacy on cancer-related fatigue and health-related quality of life in young survivors of childhood cancer. Int J Environ Res Public Health 19(3):1467. (PMID: 35162489883492610.3390/ijerph19031467)
Van Dyk K, Ahn J, Zhou X, Zhai W, Ahles TA, Bethea TN et al (2022) Associating persistent self-reported cognitive decline with neurocognitive decline in older breast cancer survivors using machine learning: the Thinking and Living with Cancer study. J Geriatr Oncol 13(8):1132–1140. (PMID: 360301731001620210.1016/j.jgo.2022.08.005)
Voon NS, Manan HA, Yahya N (2024) Remote assessment of cognition and quality of life following radiotherapy for nasopharyngeal carcinoma: deep-learning-based predictive models and MRI correlates. J Cancer Surviv 18(4):1297–1308. (PMID: 3701077710.1007/s11764-023-01371-8)
Wang M, Wang J, Li X, Xu X, Zhao Q, Li Y (2022) A predictive model for postoperative cognitive dysfunction in elderly patients with gastric cancer: a retrospective study. Am J Transl Res 14(1):679–686. (PMID: 351738868829645)
Wang L, Zhu Y, Wu L, Zhuang Y, Zeng J, Zhou F (2022) Classification of chemotherapy-related subjective cognitive complaints in breast cancer using brain functional connectivity and activity: a machine learning analysis. J Clin Med 11(8):2267. (PMID: 35456359902778710.3390/jcm11082267)
Bacas E, Kahhalé I, Raamana PR, Pablo JB, Anand AS, Hanson JL (2023) Probing multiple algorithms to calculate brain age: examining reliability, relations with demographics, and predictive power. Hum Brain Mapp 44(9):3481–3492. (PMID: 370172421020379110.1002/hbm.26292)
Wagner LI, Sweet J, Butt Z, Lai JS, Cella D (2009) Measuring patient self-reported cognitive function: development of the functional assessment of cancer therapy–cognitive function instrument. J Support Oncol 7:W32–W39.
Liu Y, Li R-L, Chen L, Zhao F-Y, Su Y-L, Jin S et al (2024) Construction and validation of a risk-prediction model for chemotherapy-related cognitive impairment in patients with breast cancer. J Cancer Surviv. https://doi.org/10.1007/s11764-024-01566-7. (PMID: 10.1007/s11764-024-01566-739636574)
Mandelblatt JS, Clapp JD, Luta G, Faul LA, Tallarico MD, McClendon TD et al (2016) Long-term trajectories of self-reported cognitive function in a cohort of older survivors of breast cancer: CALGB 369901 (Alliance). Cancer 122(22):3555–3563. (PMID: 2744735910.1002/cncr.30208)
Xu J, Yang Y, Hu D (2023) Predictors of cognitive impairment in patients undergoing ileostomy for colorectal cancer: a retrospective analysis. PeerJ 11:e15405. (PMID: 373048891024961910.7717/peerj.15405)
Taphoorn MJ, Klein M (2004) Cognitive deficits in adult patients with brain tumours. Lancet Neurol 3(3):159–168. (PMID: 1498053110.1016/S1474-4422(04)00680-5)
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Phillips NS, Mulrooney DA, Williams AM, Liu W, Khan RB, Ehrhardt MJ et al (2023) Neurocognitive impairment associated with chronic morbidity in long-term survivors of Hodgkin lymphoma. Blood Adv 7(23):7270–7278. (PMID: 377296181071116810.1182/bloodadvances.2023010567)
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Duan R, Wen Z, Zhang T, Liu J, Feng T, Ren T (2025) Advances in risk prediction models for cancer-related cognitive impairment. Clin Exp Med 25(1):74. (PMID: 400479521188531910.1007/s10238-025-01590-6)
Toledano N, Donison V, Sigal A, Mayo S, Alibhai SMH, Puts M (2025) Prevalence of pre-existing cognitive impairment in patients treated for cancer and the impact of cancer treatment on cognitive outcomes: a scoping review. J Geriatr Oncol 16(4):102235. (PMID: 4015848510.1016/j.jgo.2025.102235)
Ahles TA, Saykin AJ, McDonald BC, Li Y, Furstenberg CT, Hanscom BS et al (2010) Longitudinal assessment of cognitive changes associated with adjuvant treatment for breast cancer: impact of age and cognitive reserve. J Clin Oncol 28(29):4434–4440. (PMID: 20837957298863510.1200/JCO.2009.27.0827)
Guida JL, Ahles TA, Belsky D, Campisi J, Cohen HJ, DeGregori J et al (2019) Measuring aging and identifying aging phenotypes in cancer survivors. JNCI J Natl Cancer Inst 111(12):1245–1254. (PMID: 3132142610.1093/jnci/djz136)
Armenian SH, Gibson CJ, Rockne RC, Ness KK (2019) Premature aging in young cancer survivors. JNCI J Natl Cancer Inst 111(3):226–232. (PMID: 3071544610.1093/jnci/djy229)
Haywood D, Chan A, Chan RJ, Dauer E, Dhillon HM, Henneghan AM et al (2025) Accounting for unmet needs resulting from cancer-related cognitive impairment. J Cancer Surviv.
Sharafeldin N, Richman J, Bosworth A, Chen Y, Singh P, Patel SK et al (2020) Clinical and genetic risk prediction of cognitive impairment after blood or marrow transplantation for hematologic malignancy. J Clin Oncol 38(12):1312–1321. (PMID: 32083992826538710.1200/JCO.19.01085)
Chen VC-H, Lin T-Y, Yeh D-C, Chai J-W, Weng J-C (2020) Functional and structural connectome features for machine learning chemo-brain prediction in women treated for breast cancer with chemotherapy. Brain Sci 10(11):851. (PMID: 33198294769651210.3390/brainsci10110851)
Chen VC-H, Lin T-Y, Yeh D-C, Chai J-W, Weng J-C (2019) Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning. Magn Reson Med 81(5):3304–3313. (PMID: 3041793310.1002/mrm.27607)
Colliva C, Rivi V, Sarti P, Cobelli I, Blom JMC (2024) Exploring sex-based neuropsychological outcomes in pediatric brain cancer survivors: a pilot study. Diseases. https://doi.org/10.3390/diseases12110289. (PMID: 10.3390/diseases121102893958996311592787)
de Ruiter MB, Deardorff RL, Blommaert J, Chen BT, Dumas JA, Schagen SB et al (2023) Brain gray matter reduction and premature brain aging after breast cancer chemotherapy: a longitudinal multicenter data pooling analysis. Brain Imaging Behav 17(5):507–518. (PMID: 372564941065222210.1007/s11682-023-00781-7)
Henneghan AM, Palesh O, Harrison M, Kesler SR (2018) Identifying cytokine predictors of cognitive functioning in breast cancer survivors up to 10 years post chemotherapy using machine learning. J Neuroimmunol 320:38–47. (PMID: 29759139603068710.1016/j.jneuroim.2018.04.012)
Henneghan AM, Gibbons C, Harrison RA, Edwards ML, Rao V, Blayney DW et al (2020) Predicting patient reported outcomes of cognitive function using connectome-based predictive modeling in breast cancer. Brain Topogr 33(1):135–142. (PMID: 3174568910.1007/s10548-019-00746-4)
Henneghan A, Rao V, Harrison RA, Karuturi M, Blayney DW, Palesh O et al (2020) Cortical brain age from pre-treatment to post-chemotherapy in patients with breast cancer. Neurotox Res 37(4):788–799. (PMID: 31900898708981710.1007/s12640-019-00158-z)
Huang T, Ngan CK, Cheung YT, Marcotte M, Cabrera B (2025) A hybrid deep learning-based feature selection approach for supporting early detection of long-term behavioral outcomes in survivors of cancer: cross-sectional study. JMIR Bioinform Biotechnol 6:e65001. (PMID: 400808201195070010.2196/65001)
Kesler SR, Rao A, Blayney DW, Oakley-Girvan IA, Karuturi M, Palesh O (2017) Predicting long-term cognitive outcome following breast cancer with pre-treatment resting state fMRI and random forest machine learning. Front Hum Neurosci 11:555. (PMID: 29187817569482510.3389/fnhum.2017.00555)
Kesler SR, Petersen ML, Rao V, Harrison RA, Palesh O (2020) Functional connectome biotypes of chemotherapy-related cognitive impairment. J Cancer Surviv 14(4):483–493. (PMID: 32157609795831110.1007/s11764-020-00863-1)
Kesler SR, Henneghan AM, Thurman W, Rao V (2022) Identifying themes for assessing cancer-related cognitive impairment: topic modeling and qualitative content analysis of public online comments. JMIR Cancer 8(2):e34828. (PMID: 35612878917845010.2196/34828)
Kline C, Stoller S, Byer L, Samuel D, Lupo JM, Morrison MA et al (2022) An integrated analysis of clinical, genomic, and imaging features reveals predictors of neurocognitive outcomes in a longitudinal cohort of pediatric cancer survivors, enriched with CNS tumors (Rad ART Pro). Front Oncol 12:874317. (PMID: 35814456925998110.3389/fonc.2022.874317)
Lemos R, Areias-Marques S, Ferreira P, O’Brien P, Beltrán-Jaunsarás ME, Ribeiro G et al (2022) A prospective observational study for a federated artificial intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocol. BMC Psychiatry 22(1):817. (PMID: 36544126976903410.1186/s12888-022-04446-5)
Lin K-Y, Chen VC-H, Tsai Y-H, McIntyre RS, Weng J-C (2021) Classification and visualization of chemotherapy-induced cognitive impairment in volumetric convolutional neural networks. J Pers Med 11(10):1025. (PMID: 34683166853886210.3390/jpm11101025)
Lin P-H, Kuo P-H (2022) Ensemble learning based functional independence ability estimator for pediatric brain tumor survivors. Health Inform J 28(4):14604582221140976. (PMID: 10.1177/14604582221140975)
Luo X, Gandhi P, Storey S, Zhang Z, Han Z, Huang K (2021) A computational framework to analyze the associations between symptoms and cancer patient attributes post chemotherapy using EHR data. IEEE J Biomed Health Inform 25(11):4098–4109. (PMID: 3461392210.1109/JBHI.2021.3117238)
McDeed AP, Van Dyk K, Zhou X, Zhai W, Ahles TA, Bethea TN et al (2024) Prediction of cognitive decline in older breast cancer survivors: the thinking and living with cancer study. JNCI Cancer Spectr 8(2). https://doi.org/10.1093/jncics/pkae019.
Mulholland MM, Stuifbergen A, De La Torre Schutz A, Franco Rocha OY, Blayney DW, Kesler SR (2024) Evidence of compensatory neural hyperactivity in a subgroup of breast cancer survivors treated with chemotherapy and its association with brain aging. Front Aging Neurosci 16:1421703. (PMID: 397231531166869210.3389/fnagi.2024.1421703)
Zamanipoor Najafabadi AH, van der Meer PB, Boele FW, Taphoorn MJB, Klein M, Peerdeman SM et al (2021) Determinants and predictors for the long-term disease burden of intracranial meningioma patients. J Neurooncol 151(2):201–210. (PMID: 3307332610.1007/s11060-020-03650-1)
Oyefiade A, Erdman L, Goldenberg A, Malkin D, Bouffet E, Taylor MD et al (2019) PPAR and GST polymorphisms may predict changes in intellectual functioning in medulloblastoma survivors. J Neurooncol 142(1):39–48. (PMID: 3060770910.1007/s11060-018-03083-x)
Saito M, Hiramoto I, Yano M, Watanabe A, Kodama H (2022) Influence of self-efficacy on cancer-related fatigue and health-related quality of life in young survivors of childhood cancer. Int J Environ Res Public Health 19(3):1467. (PMID: 35162489883492610.3390/ijerph19031467)
Van Dyk K, Ahn J, Zhou X, Zhai W, Ahles TA, Bethea TN et al (2022) Associating persistent self-reported cognitive decline with neurocognitive decline in older breast cancer survivors using machine learning: the Thinking and Living with Cancer study. J Geriatr Oncol 13(8):1132–1140. (PMID: 360301731001620210.1016/j.jgo.2022.08.005)
Voon NS, Manan HA, Yahya N (2024) Remote assessment of cognition and quality of life following radiotherapy for nasopharyngeal carcinoma: deep-learning-based predictive models and MRI correlates. J Cancer Surviv 18(4):1297–1308. (PMID: 3701077710.1007/s11764-023-01371-8)
Wang M, Wang J, Li X, Xu X, Zhao Q, Li Y (2022) A predictive model for postoperative cognitive dysfunction in elderly patients with gastric cancer: a retrospective study. Am J Transl Res 14(1):679–686. (PMID: 351738868829645)
Wang L, Zhu Y, Wu L, Zhuang Y, Zeng J, Zhou F (2022) Classification of chemotherapy-related subjective cognitive complaints in breast cancer using brain functional connectivity and activity: a machine learning analysis. J Clin Med 11(8):2267. (PMID: 35456359902778710.3390/jcm11082267)
Bacas E, Kahhalé I, Raamana PR, Pablo JB, Anand AS, Hanson JL (2023) Probing multiple algorithms to calculate brain age: examining reliability, relations with demographics, and predictive power. Hum Brain Mapp 44(9):3481–3492. (PMID: 370172421020379110.1002/hbm.26292)
Wagner LI, Sweet J, Butt Z, Lai JS, Cella D (2009) Measuring patient self-reported cognitive function: development of the functional assessment of cancer therapy–cognitive function instrument. J Support Oncol 7:W32–W39.
Liu Y, Li R-L, Chen L, Zhao F-Y, Su Y-L, Jin S et al (2024) Construction and validation of a risk-prediction model for chemotherapy-related cognitive impairment in patients with breast cancer. J Cancer Surviv. https://doi.org/10.1007/s11764-024-01566-7. (PMID: 10.1007/s11764-024-01566-739636574)
Mandelblatt JS, Clapp JD, Luta G, Faul LA, Tallarico MD, McClendon TD et al (2016) Long-term trajectories of self-reported cognitive function in a cohort of older survivors of breast cancer: CALGB 369901 (Alliance). Cancer 122(22):3555–3563. (PMID: 2744735910.1002/cncr.30208)
Xu J, Yang Y, Hu D (2023) Predictors of cognitive impairment in patients undergoing ileostomy for colorectal cancer: a retrospective analysis. PeerJ 11:e15405. (PMID: 373048891024961910.7717/peerj.15405)
Taphoorn MJ, Klein M (2004) Cognitive deficits in adult patients with brain tumours. Lancet Neurol 3(3):159–168. (PMID: 1498053110.1016/S1474-4422(04)00680-5)
Jim HSL, Jennewein SL, Quinn GP, Reed DR, Small BJ (2018) Cognition in adolescent and young adults diagnosed with cancer: an understudied problem. J Clin Oncol 36(27):2752–2754. (PMID: 30040524701041710.1200/JCO.2018.78.0627)
Phillips NS, Mulrooney DA, Williams AM, Liu W, Khan RB, Ehrhardt MJ et al (2023) Neurocognitive impairment associated with chronic morbidity in long-term survivors of Hodgkin lymphoma. Blood Adv 7(23):7270–7278. (PMID: 377296181071116810.1182/bloodadvances.2023010567)
Figueroa Gray MS, Shapiro L, Dorsey CN, Randall S, Casperson M, Chawla N et al (2024) A patient-centered conceptual model of AYA cancer survivorship care informed by a qualitative interview study. Cancers 16(17):3073. (PMID: 392729311139414410.3390/cancers16173073)
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Contributed Indexing:
Keywords: Adolescent and young adults (AYA); Cancer survivorship; Cancer-related cognitive impairment; Machine learning; Natural language processing; Neurocognitive impairment
Entry Date(s):
Date Created: 20260106 Date Completed: 20260106 Latest Revision: 20260121
Update Code:
20260130
DOI:
10.1007/s00520-025-10293-3
PMID:
41495338
Database:
MEDLINE
*Further Information*
*Purpose: This scoping review explores how machine learning (ML) and natural language processing (NLP) are used to detect, characterize, and predict neurocognitive symptoms in cancer survivors across age groups. The review had two goals: (1) to compare ML and NLP applications in understanding cancer-related cognitive impairment (CRCI) among age-stratified survivors and (2) to identify research gaps that could inform future survivorship care.
Methods: Following PRISMA-ScR guidelines, a comprehensive literature search was conducted across PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar from 2014 to 2025. Studies were included if they used ML or NLP to assess neurocognitive outcomes in posttreatment cancer survivors. Studies without defined ML/NLP methods, a survivorship focus, or peer review were excluded.
Results: The final review included 27 studies with 3584 participants. Most studies used supervised ML models such as random forest and support vector machines. Key applications included predicting patient-reported outcomes and identifying biomarkers via neuroimaging. Most studies focused on adult survivors, with limited research in older adult (n = 4), AYA (n = 1), and pediatric (n = 3) populations specifically, despite their high risk for long-term CRCI.
Conclusion: ML and NLP show promise for CRCI detection. Future research should prioritize developing age-specific ML/NLP models for underrepresented populations, particularly older adults, AYA, and pediatric survivors, while establishing standardized validation frameworks. Additionally, interdisciplinary collaboration and integration into clinical workflows will be essential for effective implementation.
(© 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)*
*Declarations. Competing interest: The authors declare no competing interests.*