Evidence map›Paper›PMID 42483565›Full record

ReviewDigital health

Predicting cancer treatment outcomes using machine learning enabled clinical decision support systems.

Adib Hossain, Md Mohaimin Rashid, Towsif Alam, Muslima Begom Riipa, Mahafuj Hassan, Md Ahsan Ullah Imran, Nur Mohammad, Fahad Ahmed, Mst Rina Parvin

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Adib HossainDepartment of Business Analytics, Trine University, IN, USA.
Md Mohaimin RashidDepartment of Business Administration, International American University, CA, USA.
Towsif AlamDepartment of Marketing Analytics and Insights, Wright State University, OH, USA.
Muslima Begom RiipaDepartment of Business Administration, International American University, CA, USA.
Mahafuj HassanDepartment of Business Administration, International American University, CA, USA.
Md Ahsan Ullah ImranDepartment of Business Administration, Westcliff University, CA, USA.
Nur MohammadDepartment of Information Technology, Westcliff University, CA, USA.
Fahad AhmedDepartment of Science in Engineering Management, Trine University, IN, USA.
Mst Rina ParvinPublic Health Services, Action Research Bangladesh, Dhaka, Bangladesh.ORCID https://orcid.org/0000-0003-0111-6163

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cancer treatment poses significant challenges due to variability in patient responses, disease progression, and therapy outcomes. Traditional decision-making frameworks often fall short in integrating complex data streams, underscoring the need for intelligent systems. Machine learning (ML)-enabled clinical decision support systems (CDSS) offer a promising solution by enabling personalized, predictive, and data-driven oncology care. Aim: This scoping review aimed to explore how ML-powered CDSS are applied to predict treatment outcomes across different types of cancer. It sought to identify the types of machine learning models used, data modalities involved, predictive endpoints targeted, and the extent of clinical implementation and validation. Methods: A systematic search was conducted across six databases covering studies from 2010 to 2025. Eligible studies included those deploying ML algorithms within CDSS frameworks for outcome prediction in oncology. Data extraction followed a structured charting process, and studies were assessed using the Mixed Methods Appraisal Tool (MMAT). Findings were synthesized narratively and through thematic categorization. Results: A total of 32 studies were included. Predictive objectives ranged from survival estimation and therapy response to toxicity risk and recurrence detection. ML techniques varied from decision trees and vector machines to deep learning models such as convolutional neural networks. While technical performance was promising, few studies demonstrated external validation or integration into clinical workflows. Interpretability, ethical considerations, and patient involvement were frequently underreported. Conclusion: ML-enabled CDSS have shown significant potential in predicting cancer treatment outcomes, yet their adoption in practice remains limited. Advancing these systems requires focus on validation, interpretability, data integration, and ethical design to bridge the gap between innovation and clinical utility.

Indexed as

cancer treatmentclinical decision support systemsmachine learningoncology informaticsoutcome prediction

Identifiers

PMID42483565
PMCPMC13385615

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.