A systematic review on machine learning and deep learning techniques in cancer survival prediction

Pandey, Deepa; Gunavathi, C.

doi:10.1016/j.pbiomolbio.2022.07.004

Cited by 25 publications

(17 citation statements)

References 41 publications

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“…When utilized in NLP, neural models can develop a more complex understanding of language, such as the presence of words with respect to each other, even when not adjacent . We were unable to find prior work using neural NLP methods to predict the survival of patients with general cancer using oncologist consultation documents, nor were such works identified in recent reviews of neural network applications in cancer, in general medical applications, or in a recent review of machine learning techniques used in cancer survival prediction …”

Section: Introductionmentioning

confidence: 95%

“…4 We were unable to find prior work using neural NLP methods to predict the survival of patients with general cancer using oncologist consultation documents, nor were such works identified in recent reviews of neural network applications in cancer, 13 in general medical applications, 11 or in a recent review of machine learning techniques used in cancer survival prediction. 18 Our work sought to develop and evaluate neural NLP models that predict the survival of patients with general cancer using their initial oncologist consultation, without the use of structured data. By using this common document without structured data, we hoped to build models that would not be constrained by requiring the collection and processing of specific data.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing

Nunez

Leung²,

Ho³

et al. 2023

JAMA Netw Open

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ImportancePredicting short- and long-term survival of patients with cancer may improve their care. Prior predictive models either use data with limited availability or predict the outcome of only 1 type of cancer.ObjectiveTo investigate whether natural language processing can predict survival of patients with general cancer from a patient’s initial oncologist consultation document.Design, Setting, and ParticipantsThis retrospective prognostic study used data from 47 625 of 59 800 patients who started cancer care at any of the 6 BC Cancer sites located in the province of British Columbia between April 1, 2011, and December 31, 2016. Mortality data were updated until April 6, 2022, and data were analyzed from update until September 30, 2022. All patients with a medical or radiation oncologist consultation document generated within 180 days of diagnosis were included; patients seen for multiple cancers were excluded.ExposuresInitial oncologist consultation documents were analyzed using traditional and neural language models.Main Outcomes and MeasuresThe primary outcome was the performance of the predictive models, including balanced accuracy and receiver operating characteristics area under the curve (AUC). The secondary outcome was investigating what words the models used.ResultsOf the 47 625 patients in the sample, 25 428 (53.4%) were female and 22 197 (46.6%) were male, with a mean (SD) age of 64.9 (13.7) years. A total of 41 447 patients (87.0%) survived 6 months, 31 143 (65.4%) survived 36 months, and 27 880 (58.5%) survived 60 months, calculated from their initial oncologist consultation. The best models achieved a balanced accuracy of 0.856 (AUC, 0.928) for predicting 6-month survival, 0.842 (AUC, 0.918) for 36-month survival, and 0.837 (AUC, 0.918) for 60-month survival, on a holdout test set. Differences in what words were important for predicting 6- vs 60-month survival were found.Conclusions and RelevanceThese findings suggest that models performed comparably with or better than previous models predicting cancer survival and that they may be able to predict survival using readily available data without focusing on 1 cancer type.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing

Nunez

Leung²,

Ho³

et al. 2023

JAMA Netw Open

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“…In Table 1, a comprehensive analysis of the scope of review articles indicates that existing studies can be classified into three distinct groups. I) 9 review papers primarily focus on the application of DL algorithms in survival prediction 47,[49][50][51][52][53][54][55][56] , II) 7 review papers summarise the application of ML algorithms in survival prediction 37,48,[57][58][59][60][61] , and 6 review papers summarise survival prediction methods from three different categories namely statistical, ML, and DL methods [44][45][46][62][63][64] .…”

Section: A Look-back Into Existing Review Studiesmentioning

confidence: 99%

“…These articles primarily aim to summarize the latest trends and developments in data modalities, feature engineering methods, and AI models specifically related to survival prediction. However, the focus of these reviews is often constrained to either a singular disease or multiple subtypes of cancer, highlighting a limited scope within the broader landscape of survival prediction research 37,[44][45][46][47][48] . More comprehensive details about the scope of existing review articles in terms of contributions and drawbacks are summarised in Table 1 and section .…”

Section: Introductionmentioning

confidence: 99%

“…While the scope of survival prediction extends beyond multiple diseases, existing review papers fall short to summarize current trends of data modalities, feature engineering approaches and survival prediction models. For example, Deepa et al 44 specifically address the primary categories of data modalities used for survival prediction, namely multiomics and clinical data. However, the review does not extensively explore trends and patterns related to the 9 different omics types i.e., gene expression (mRNA), micro RNA (miRNA), methylation, copy number variation (CNV), whole exome sequencing (WES), long noncoding RNA (lncRNA), mutation, metabolic, and proteomics, or clinical features associated with distinct cancer subtypes.…”

Section: Introductionmentioning

confidence: 99%

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Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and Databases

Abbasi,

Asim,

Ahmed

et al. 2024

Preprint

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Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, the need of the hour is to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 22 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 74 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.

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Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer

Meng,

Bi,

Fulham

et al. 2023

Lecture Notes in Computer Science

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A systematic review on machine learning and deep learning techniques in cancer survival prediction

Cited by 25 publications

References 41 publications

Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing

Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing

Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and Databases

Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer

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