Predicting outcomes in anal cancer patients using multi-centre data and distributed learning – A proof-of-concept study

Choudhury, Ananya; Lønne, P.; Samuel, Robert; Guren, Marianne G.; Berbée, Maaike; Brown, Peter; Lilley, J.; Soest, Johan van; Dekker, André; Gilbert, Alexandra; Malinen, Eirik; Wee, Leonard; Appelt, Ane L

doi:10.1016/j.radonc.2021.03.013

Cited by 24 publications

(9 citation statements)

References 30 publications

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“…The resulting model was able to better identify pancreas from both datasets than models trained only on one site and validated on the other. Concluding with similar results, [53] tested several deep learning architectures for federated thyroid images classification, and Choudhury et al [54] used data from 3 different sites to create a prediction model for patients with anal cancer, an extremely rare form of cancer, who received radical chemoradiotherapy. The large and diverse group of examples given here demonstrates the robustness and versatility of the Federated Learning paradigm, as well as its ability to improve automated analysis on more rare cancer cases [51,53,54].…”

Section: Federated Learning Algorithmsmentioning

confidence: 84%

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

Chowdhury

Kassem

Padoy

et al. 2022

Lecture Notes in Computer Science

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Machine learning has revolutionized every facet of human life, while also becoming more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare, with numerous applications and intelligent systems achieving clinical level expertise. However, building robust and generalizable systems relies on training algorithms in a centralized fashion using large, heterogeneous datasets. In medicine, these datasets are time consuming to annotate and difficult to collect centrally due to privacy concerns. Recently, Federated Learning has been proposed as a distributed learning technique to alleviate many of these privacy concerns by providing a decentralized training paradigm for models using large, distributed data. This new approach has become the defacto way of building machine learning models in multiple industries (e.g. edge computing, smartphones). Due to its strong potential, Federated Learning is also becoming a popular training method in healthcare, where patient privacy is of paramount concern. In this paper we performed an extensive literature review to identify state-of-the-art Federated Learning applications for cancer research and clinical oncology analysis. Our objective is to provide readers with an overview of the evolving Federated Learning landscape, with a focus on applications and algorithms in oncology space. Moreover, we hope that this review will help readers to identify potential needs and future directions for research and development.

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Section: Federated Learning Algorithmsmentioning

confidence: 84%

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

Chowdhury

Kassem

Padoy

et al. 2022

Lecture Notes in Computer Science

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“…47,48 Distributed learning approaches with AI support have been used to conduct population-based studies on routine data and build decision support models. 49,50 Image banking combined with predictive/prescriptive AI is a cost-effective and efficient alternative to identify signatures for response, toxicity, and outcome prediction after cancer treatment. [51][52][53] Natural Language Processing…”

Section: Data Annotation Radiomics and Response Predictionmentioning

confidence: 99%

Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy

Krishnamurthy

Mummudi

Goda

et al. 2022

JCO Global Oncology

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The radiotherapy (RT) process from planning to treatment delivery is a multistep, complex operation involving numerous levels of human-machine interaction and requiring high precision. These steps are labor-intensive and time-consuming and require meticulous coordination between professionals with diverse expertise. We reviewed and summarized the current status and prospects of artificial intelligence and machine learning relevant to the various steps in RT treatment planning and delivery workflow specifically in low- and middle-income countries (LMICs). We also searched the PubMed database using the search terms (Artificial Intelligence OR Machine Learning OR Deep Learning OR Automation OR knowledge-based planning AND Radiotherapy) AND (list of Low- and Middle-Income Countries as defined by the World Bank at the time of writing this review). The search yielded a total of 90 results, of which results with first authors from the LMICs were chosen. The reference lists of retrieved articles were also reviewed to search for more studies. No language restrictions were imposed. A total of 20 research items with unique study objectives conducted with the aim of enhancing RT processes were examined in detail. Artificial intelligence and machine learning can improve the overall efficiency of RT processes by reducing human intervention, aiding decision making, and efficiently executing lengthy, repetitive tasks. This improvement could permit the radiation oncologist to redistribute resources and focus on responsibilities such as patient counseling, education, and research, especially in resource-constrained LMICs.

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“…However, the vast majority of patients are not enrolled in clinical trials and therefore have not consented to their data being stored in a central national or international database. This problem can be addressed by distributed learning [6][7][8][9], whereby data are stored locally in each centre where it is generated and only aggregated data, such as model parameters, are shared between centres. These parameters can then be updated iteratively for an overarching model through distributed learning communication servers.…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

“…Thus, this specific problem is not apparent in two large distributed learning studies on lung cancer that utilise logistic regression and a Bayesian network to predict events at a given time [8,21]. However, for distributed learning using the Cox model, such as Choudhury et al [9], there is a potential to leak patient data unless specific actions are taken to prohibit such problems.…”

Section: Distributed Learning Validationmentioning

confidence: 99%

Open-source distributed learning validation for a larynx cancer survival model following radiotherapy

Hansen

Price

Field

et al. 2022

Radiotherapy and Oncology

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Predicting outcomes in anal cancer patients using multi-centre data and distributed learning – A proof-of-concept study

Cited by 24 publications

References 30 publications

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy

Open-source distributed learning validation for a larynx cancer survival model following radiotherapy

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