1
|
Field M, Vinod S, Delaney GP, Aherne N, Bailey M, Carolan M, Dekker A, Greenham S, Hau E, Lehmann J, Ludbrook J, Miller A, Rezo A, Selvaraj J, Sykes J, Thwaites D, Holloway L. Federated Learning Survival Model and Potential Radiotherapy Decision Support Impact Assessment for Non-small Cell Lung Cancer Using Real-World Data. Clin Oncol (R Coll Radiol) 2024; 36:e197-e208. [PMID: 38631978 DOI: 10.1016/j.clon.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 02/07/2024] [Accepted: 03/11/2024] [Indexed: 04/19/2024]
Abstract
AIMS The objective of this study was to develop a two-year overall survival model for inoperable stage I-III non-small cell lung cancer (NSCLC) patients using routine radiation oncology data over a federated (distributed) learning network and evaluate the potential of decision support for curative versus palliative radiotherapy. METHODS A federated infrastructure of data extraction, de-identification, standardisation, image analysis, and modelling was installed for seven clinics to obtain clinical and imaging features and survival information for patients treated in 2011-2019. A logistic regression model was trained for the 2011-2016 curative patient cohort and validated for the 2017-2019 cohort. Features were selected with univariate and model-based analysis and optimised using bootstrapping. System performance was assessed by the receiver operating characteristic (ROC) and corresponding area under curve (AUC), C-index, calibration metrics and Kaplan-Meier survival curves, with risk groups defined by model probability quartiles. Decision support was evaluated using a case-control analysis using propensity matching between treatment groups. RESULTS 1655 patient datasets were included. The overall model AUC was 0.68. Fifty-eight percent of patients treated with palliative radiotherapy had a low-to-moderate risk prediction according to the model, with survival times not significantly different (p = 0.87 and 0.061) from patients treated with curative radiotherapy classified as high-risk by the model. When survival was simulated by risk group and model-indicated treatment, there was an estimated 11% increase in survival rate at two years (p < 0.01). CONCLUSION Federated learning over multiple institution data can be used to develop and validate decision support systems for lung cancer while quantifying the potential impact of their use in practice. This paves the way for personalised medicine, where decisions can be based more closely on individual patient details from routine care.
Collapse
Affiliation(s)
- M Field
- South Western Sydney Clinical Campus, School of Clinical Medicine, UNSW, Sydney, New South Wales, Australia; Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, New South Wales, Australia.
| | - S Vinod
- South Western Sydney Clinical Campus, School of Clinical Medicine, UNSW, Sydney, New South Wales, Australia; Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, New South Wales, Australia
| | - G P Delaney
- South Western Sydney Clinical Campus, School of Clinical Medicine, UNSW, Sydney, New South Wales, Australia; Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, New South Wales, Australia
| | - N Aherne
- Mid North Coast Cancer Institute, Coffs Harbour, New South Wales, Australia; Rural Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - M Bailey
- Illawarra Cancer Care Centre, Wollongong, New South Wales, Australia
| | - M Carolan
- Illawarra Cancer Care Centre, Wollongong, New South Wales, Australia
| | - A Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - S Greenham
- Mid North Coast Cancer Institute, Coffs Harbour, New South Wales, Australia
| | - E Hau
- Sydney West Radiation Oncology Network, Sydney, Australia; Westmead Clinical School, University of Sydney, Sydney, New South Wales, Australia
| | - J Lehmann
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, New South Wales, Australia; Department of Radiation Oncology, Calvary Mater, Newcastle, New South Wales, Australia; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - J Ludbrook
- Department of Radiation Oncology, Calvary Mater, Newcastle, New South Wales, Australia
| | - A Miller
- Illawarra Cancer Care Centre, Wollongong, New South Wales, Australia
| | - A Rezo
- Canberra Health Services, Canberra, Australian Capital Territory, Australia
| | - J Selvaraj
- South Western Sydney Clinical Campus, School of Clinical Medicine, UNSW, Sydney, New South Wales, Australia; Canberra Health Services, Canberra, Australian Capital Territory, Australia
| | - J Sykes
- Sydney West Radiation Oncology Network, Sydney, Australia; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - D Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia; Radiotherapy Research Group, Leeds Institute for Medical Research, St James's Hospital and the University of Leeds, Leeds, UK
| | - L Holloway
- South Western Sydney Clinical Campus, School of Clinical Medicine, UNSW, Sydney, New South Wales, Australia; Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, New South Wales, Australia; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| |
Collapse
|
2
|
Hwang E, Gorayski P, Thwaites D, Le H, Skelton K, Loong JTK, Langendijk H, Smith E, Yock TI, Ahern V. Minimum data elements for the Australian Particle Therapy Clinical Quality Registry. J Med Imaging Radiat Oncol 2023; 67:668-675. [PMID: 37417796 DOI: 10.1111/1754-9485.13557] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/26/2023] [Indexed: 07/08/2023]
Abstract
INTRODUCTION Construction of the first Australian particle therapy (PT) centre is underway. Establishment of a national registry, to be known as the Australian Particle Therapy Clinical Quality Registry (ASPIRE), has been identified as a mandatory requirement for PT treatment to be reimbursed by the Australian Medicare Benefits Schedule. This study aimed to determine a consensus set of Minimum Data Elements (MDEs) for ASPIRE. METHODS A modified Delphi and expert consensus process was completed. Stage 1 compiled currently operational English-language international PT registries. Stage 2 listed the MDEs included in each of these four registries. Those included in three or four registries were automatically included as a potential MDE for ASPIRE. Stage 3 interrogated the remaining data items, and involved three rounds - an online survey to a panel of experts, followed by a live poll session of PT-interested participants, and finally a virtual discussion forum of the original expert panel. RESULTS One hundred and twenty-three different MDEs were identified across the four international registries. The multi-staged Delphi and expert consensus process resulted in a total of 27 essential MDEs for ASPIRE; 14 patient factors, four tumour factors and nine treatment factors. CONCLUSIONS The MDEs provide the core mandatory data items for the national PT registry. Registry data collection for PT is paramount in the ongoing global effort to accumulate more robust clinical evidence regarding PT patient and tumour outcomes, quantifying the magnitude of clinical benefit and justifying the relatively higher costs of PT investment.
Collapse
Affiliation(s)
- Eunji Hwang
- Department of Radiation Oncology, Sydney West Radiation Oncology Network, Sydney, New South Wales, Australia
- Institute of Medical Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Peter Gorayski
- Department of Radiation Oncology, Royal Adelaide Hospital, Adelaide, South Australia, Australia
- School of Health Sciences, University of South Australia, Adelaide, South Australia, Australia
- Australian Bragg Centre for Proton Therapy and Research, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - David Thwaites
- Institute of Medical Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Hien Le
- Department of Radiation Oncology, Royal Adelaide Hospital, Adelaide, South Australia, Australia
- School of Health Sciences, University of South Australia, Adelaide, South Australia, Australia
- Australian Bragg Centre for Proton Therapy and Research, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Kelly Skelton
- Department of Radiation Oncology, Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Australian Bragg Centre for Proton Therapy and Research, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Jeffrey Tuan Kit Loong
- Department of Radiation Oncology, National Cancer Centre Singapore, Singapore City, Singapore
- Oncology Academic Clinical Program, Duke-NUS Medical School, Singapore City, Singapore
| | - Hans Langendijk
- Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Ed Smith
- The Christie Proton Beam Therapy Centre, The Christie NHS Foundation Trust, Manchester, UK
- Manchester Cancer Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Torunn I Yock
- Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Verity Ahern
- Department of Radiation Oncology, Sydney West Radiation Oncology Network, Sydney, New South Wales, Australia
- Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| |
Collapse
|
3
|
Nigam R, Field M, Harris G, Barton M, Carolan M, Metcalfe P, Holloway L. Automated detection, delineation and quantification of whole-body bone metastasis using FDG-PET/CT images. Phys Eng Sci Med 2023; 46:851-863. [PMID: 37126152 DOI: 10.1007/s13246-023-01258-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 04/11/2023] [Indexed: 05/02/2023]
Abstract
Non-small cell lung cancer (NSCLC) patients with the metastatic spread of disease to the bone have high morbidity and mortality. Stereotactic ablative body radiotherapy increases the progression free survival and overall survival of these patients with oligometastases. FDG-PET/CT, a functional imaging technique combining positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) and computer tomography (CT) provides improved staging and identification of treatment response. It is also associated with reduction in size of the radiotherapy tumour volume delineation compared with CT based contouring in radiotherapy, thus allowing for dose escalation to the target volume with lower doses to the surrounding organs at risk. FDG-PET/CT is increasingly being used for the clinical management of NSCLC patients undergoing radiotherapy and has shown high sensitivity and specificity for the detection of bone metastases in these patients. Here, we present a software tool for detection, delineation and quantification of bone metastases using FDG-PET/CT images. The tool extracts standardised uptake values (SUV) from FDG-PET images for auto-segmentation of bone lesions and calculates volume of each lesion and associated mean and maximum SUV. The tool also allows automatic statistical validation of the auto-segmented bone lesions against the manual contours of a radiation oncologist. A retrospective review of FDG-PET/CT scans of more than 30 candidate NSCLC patients was performed and nine patients with one or more metastatic bone lesions were selected for the present study. The SUV threshold prediction model was designed by splitting the cohort of patients into a subset of 'development' and 'validation' cohorts. The development cohort yielded an optimum SUV threshold of 3.0 for automatic detection of bone metastases using FDG-PET/CT images. The validity of the derived optimum SUV threshold on the validation cohort demonstrated that auto-segmented and manually contoured bone lesions showed strong concordance for volume of bone lesion (r = 0.993) and number of detected lesions (r = 0.996). The tool has various applications in radiotherapy, including but not limited to studies determining optimum SUV threshold for accurate and standardised delineation of bone lesions and in scientific studies utilising large patient populations for instance for investigation of the number of metastatic lesions that can be treated safety with an ablative dose of radiotherapy without exceeding the normal tissue toxicity.
Collapse
Affiliation(s)
- R Nigam
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, 2522, Australia.
- Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia.
- Illawarra Cancer Care Centre, Wollongong Hospital, Wollongong, NSW, 2500, Australia.
| | - M Field
- Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool, NSW, 2170, Australia
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia
| | - G Harris
- Chris O'Brien Lifehouse, Camperdown, NSW, 2050, Australia
| | - M Barton
- Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool, NSW, 2170, Australia
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia
| | - M Carolan
- Illawarra Cancer Care Centre, Wollongong Hospital, Wollongong, NSW, 2500, Australia
| | - P Metcalfe
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia
| | - L Holloway
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool, NSW, 2170, Australia
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia
- Institute of Medical Physics, University of Sydney, Camperdown, NSW, 2505, Australia
| |
Collapse
|
4
|
Offersen BV, Aznar MC, Bacchus C, Coppes RP, Deutsch E, Georg D, Haustermans K, Hoskin P, Krause M, Lartigau EF, Lee AWM, Löck S, Thwaites DI, van der Kogel AJ, van der Heide U, Valentini V, Overgaard J, Baumann M. The role of ESTRO guidelines in achieving consistency and quality in clinical radiation oncology practice. Radiother Oncol 2023; 179:109446. [PMID: 36566990 DOI: 10.1016/j.radonc.2022.109446] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Birgitte Vrou Offersen
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark; Department of Oncology, Aarhus University Hospital, Denmark; Danish Center for Particle Therapy, Aarhus University Hospital, Denmark.
| | - Marianne C Aznar
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, The Christie NHS Foundation Trust, United Kingdom
| | - Carol Bacchus
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rob P Coppes
- Department of Biomedical Sciences of Cells & Systems, Section Molecular Cell Biology, Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Eric Deutsch
- Department of Radiation Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, France
| | - Dieter Georg
- Division Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Karin Haustermans
- Department of Radiation Oncology, University Hospitals Leuven, Belgium
| | - Peter Hoskin
- Mount Vernon Cancer Centre and University of Manchester, United Kingdom
| | - Mechthild Krause
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany
| | - Eric F Lartigau
- Academic Department of Radiotherapy, Oscar Lambret Comprehensive Cancer Center, Lille, France
| | - Anne W M Lee
- Department of Clinical Oncology, Shenzhen Key Laboratory for Cancer Metastasis and Personalized Therapy, University of Hong Kong - Shenzhen Hospital, China
| | - Steffen Löck
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany
| | - David I Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Australia; Radiotherapy Research Group, St James's Hospital and University of Leeds, United Kingdom
| | - Albert J van der Kogel
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, USA
| | - Uulke van der Heide
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Vincenzo Valentini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC Radioterapia Oncologica, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Jens Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark
| | | |
Collapse
|
5
|
Haidar A, Field M, Batumalai V, Cloak K, Al Mouiee D, Chlap P, Huang X, Chin V, Aly F, Carolan M, Sykes J, Vinod SK, Delaney GP, Holloway L. Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach. Cancers (Basel) 2023; 15:cancers15030564. [PMID: 36765523 PMCID: PMC9913464 DOI: 10.3390/cancers15030564] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/01/2023] [Accepted: 01/11/2023] [Indexed: 01/18/2023] Open
Abstract
In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume.
Collapse
Affiliation(s)
- Ali Haidar
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
- Correspondence: or
| | - Matthew Field
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Vikneswary Batumalai
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
- GenesisCare, Alexandria, NSW 2015, Australia
| | - Kirrily Cloak
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Daniel Al Mouiee
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Phillip Chlap
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Xiaoshui Huang
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- University of Sydney, Camperdown, NSW 2006, Australia
| | - Vicky Chin
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Farhannah Aly
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Martin Carolan
- Illawarra Cancer Care Center, Wollongong, NSW 2522, Australia
- University of Wollongong, Wollongong, NSW 2522, Australia
| | - Jonathan Sykes
- University of Sydney, Camperdown, NSW 2006, Australia
- Blacktown Hospital, Blacktown, NSW 2148, Australia
| | - Shalini K. Vinod
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Geoffrey P. Delaney
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Lois Holloway
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
- University of Sydney, Camperdown, NSW 2006, Australia
| |
Collapse
|
6
|
Rønn Hansen C, Price G, Field M, Sarup N, Zukauskaite R, Johansen J, Eriksen JG, Aly F, McPartlin A, Holloway L, Thwaites D, Brink C. Larynx cancer survival model developed through open-source federated learning. Radiother Oncol 2022; 176:179-186. [PMID: 36208652 DOI: 10.1016/j.radonc.2022.09.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 08/12/2022] [Accepted: 09/28/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Federated learning has the potential to perfrom analysis on decentralised data; however, there are some obstacles to survival analyses as there is a risk of data leakage. This study demonstrates how to perform a stratified Cox regression survival analysis specifically designed to avoid data leakage using federated learning on larynx cancer patients from centres in three different countries. METHODS Data were obtained from 1821 larynx cancer patients treated with radiotherapy in three centres. Tumour volume was available for all 786 of the included patients. Parameter selection among eleven clinical and radiotherapy parameters were performed using best subset selection and cross-validation through the federated learning system, AusCAT. After parameter selection, β regression coefficients were estimated using bootstrap. Calibration plots were generated at 2 and 5-years survival, and inner and outer risk groups' Kaplan-Meier curves were compared to the Cox model prediction. RESULTS The best performing Cox model included log(GTV), performance status, age, smoking, haemoglobin and N-classification; however, the simplest model with similar statistical prediction power included log(GTV) and performance status only. The Harrell C-indices for the simplest model were for Odense, Christie and Liverpool 0.75[0.71-0.78], 0.65[0.59-0.71], and 0.69[0.59-0.77], respectively. The values are slightly higher for the full model with C-index 0.77[0.74-0.80], 0.67[0.62-0.73] and 0.71[0.61-0.80], respectively. Smoking during treatment has the same hazard as a ten-years older nonsmoking patient. CONCLUSION Without any patient-specific data leaving the hospitals, a stratified Cox regression model based on data from centres in three countries was developed without data leakage risks. The overall survival model is primarily driven by tumour volume and performance status.
Collapse
Affiliation(s)
- Christian Rønn Hansen
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Denmark; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia.
| | - Gareth Price
- Radiotherapy department, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Matthew Field
- Ingham Institute for Applied Medical Research, Sydney, Australia
| | - Nis Sarup
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark
| | - Ruta Zukauskaite
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Department of Oncology, Odense University Hospital, Odense, Denmark
| | - Jørgen Johansen
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | - Jesper Grau Eriksen
- Department of Oncology, Odense University Hospital, Odense, Denmark; Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark; Department of Oncology, Aarhus University Hospital, Denmark
| | - Farhannah Aly
- Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Andrew McPartlin
- Radiotherapy department, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - David Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia
| | - Carsten Brink
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| |
Collapse
|
7
|
Krishnamurthy R, Mummudi N, Goda JS, Chopra S, Heijmen B, Swamidas J. Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy. JCO Glob Oncol 2022; 8:e2100393. [PMID: 36395438 PMCID: PMC10166445 DOI: 10.1200/go.21.00393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Revathy Krishnamurthy
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Naveen Mummudi
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Jayant Sastri Goda
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Supriya Chopra
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Ben Heijmen
- Division of Medical Physics, Department of Radiation Oncology, Erasmus MC Cancer Institute, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Jamema Swamidas
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| |
Collapse
|
8
|
Field M, I Thwaites D, Carolan M, Delaney GP, Lehmann J, Sykes J, Vinod S, Holloway L. Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer. J Biomed Inform 2022; 134:104181. [PMID: 36055639 DOI: 10.1016/j.jbi.2022.104181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 04/29/2022] [Accepted: 08/20/2022] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Emerging evidence suggests that data-driven support tools have found their way into clinical decision-making in a number of areas, including cancer care. Improving them and widening their scope of availability in various differing clinical scenarios, including for prognostic models derived from retrospective data, requires co-ordinated data sharing between clinical centres, secondary analyses of large multi-institutional clinical trial data, or distributed (federated) learning infrastructures. A systematic approach to utilizing routinely collected data across cancer care clinics remains a significant challenge due to privacy, administrative and political barriers. METHODS An information technology infrastructure and web service software was developed and implemented which uses machine learning to construct clinical decision support systems in a privacy-preserving manner across datasets geographically distributed in different hospitals. The infrastructure was deployed in a network of Australian hospitals. A harmonized, international ontology-linked, set of lung cancer databases were built with the routine clinical and imaging data at each centre. The infrastructure was demonstrated with the development of logistic regression models to predict major cardiovascular events following radiation therapy. RESULTS The infrastructure implemented forms the basis of the Australian computer-assisted theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning. Four radiation oncology departments (across seven hospitals) in New South Wales (NSW) participated in this demonstration study. Infrastructure was deployed at each centre and used to develop a model predicting for cardiovascular admission within a year of receiving curative radiotherapy for non-small cell lung cancer. A total of 10417 lung cancer patients were identified with 802 being eligible for the model. Twenty features were chosen for analysis from the clinical record and linked registries. After selection, 8 features were included and a logistic regression model achieved an area under the receiver operating characteristic (AUROC) curve of 0.70 and C-index of 0.65 on out-of-sample data. CONCLUSION The infrastructure developed was demonstrated to be usable in practice between clinical centres to harmonize routinely collected oncology data and develop models with federated learning. It provides a promising approach to enable further research studies in radiation oncology using real world clinical data.
Collapse
Affiliation(s)
- Matthew Field
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, NSW, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia.
| | - David I Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, NSW, Australia
| | - Martin Carolan
- Illawarra Cancer Care Centre, Wollongong, NSW, Australia
| | - Geoff P Delaney
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, NSW, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
| | - Joerg Lehmann
- Institute of Medical Physics, School of Physics, University of Sydney, NSW, Australia; Department of Radiation Oncology, Calvary Mater Newcastle, NSW, Australia
| | - Jonathan Sykes
- Institute of Medical Physics, School of Physics, University of Sydney, NSW, Australia; Blacktown Haematology and Oncology Cancer Care Centre, Blacktown Hospital, Blacktown, NSW, Australia; Crown Princess Mary Cancer Centre, Westmead Hospital, Westmead, NSW, Australia
| | - Shalini Vinod
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, NSW, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
| | - Lois Holloway
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, NSW, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; Institute of Medical Physics, School of Physics, University of Sydney, NSW, Australia
| |
Collapse
|
9
|
Hansen CR, Price G, Field M, Sarup N, Zukauskaite R, Johansen J, Eriksen JG, Aly F, McPartlin A, Holloway L, Thwaites D, Brink C. Open-source distributed learning validation for a larynx cancer survival model following radiotherapy. Radiother Oncol 2022; 173:319-326. [PMID: 35738481 DOI: 10.1016/j.radonc.2022.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/30/2022] [Accepted: 06/15/2022] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Prediction models are useful to design personalised treatment. However, safe and effective implementation relies on external validation. Retrospective data are available in many institutions, but sharing between institutions can be challenging due to patient data sensitivity and governance or legal barriers. This study validates a larynx cancer survival model performed using distributed learning without any sensitive data leaving the institution. METHODS Open-source distributed learning software based on a stratified Cox proportional hazard model was developed and used to validate the Egelmeer et al. MAASTRO survival model across two hospitals in two countries. The validation optimised a single scaling parameter multiplied by the original predicted prognostic index. All analyses and figures were based on the distributed system, ensuring no information leakage from the individual centres. All applied software is provided as freeware to facilitate distributed learning in other institutions. RESULTS 1745 patients received radiotherapy for larynx cancer in the two centres from Jan 2005 to Dec 2018. Limiting to a maximum of one missing value in the parameters of the survival model reduced the cohort to 1095 patients. The Harrell C-index was 0.74 (CI95%, 0.71-0.76) and 0.70 (0.66-0.75) for the two centres. However, the model needed a scaling update. In addition, it was found that survival predictions of patients undergoing hypofractionation were less precise. CONCLUSION Open-source distributed learning software was able to validate, and suggest a minor update to the original survival model without central access to patient sensitive information. Even without the update, the original MAASTRO survival model of Egelmeer et al. performed reasonably well, providing similar results in this validation as in its original validation.
Collapse
Affiliation(s)
- Christian Rønn Hansen
- Laboratory of Radiation Physics, Odense University Hospital, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Denmark; Institute of Medical Physics, School of Physics, University of Sydney, Australia.
| | - Gareth Price
- Radiotherapy Department, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Matthew Field
- Ingham Institute for Applied Medical Research, Sydney, Australia
| | - Nis Sarup
- Laboratory of Radiation Physics, Odense University Hospital, Denmark
| | - Ruta Zukauskaite
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Department of Oncology, Odense University Hospital, Denmark
| | | | - Jesper Grau Eriksen
- Department of Oncology, Odense University Hospital, Denmark; Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark; Department of Oncology, Aarhus University Hospital, Denmark
| | - Farhannah Aly
- Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Andrew McPartlin
- Radiotherapy Department, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, University of Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - David Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Australia
| | - Carsten Brink
- Laboratory of Radiation Physics, Odense University Hospital, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| |
Collapse
|
10
|
Lee NSY, Shafiq J, Field M, Fiddler C, Varadarajan S, Gandhidasan S, Hau E, Vinod SK. Predicting 2-year survival in stage I-III non-small cell lung cancer: the development and validation of a scoring system from an Australian cohort. Radiat Oncol 2022; 17:74. [PMID: 35418206 PMCID: PMC9008968 DOI: 10.1186/s13014-022-02050-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/06/2022] [Indexed: 12/24/2022] Open
Abstract
Background There are limited data on survival prediction models in contemporary inoperable non-small cell lung cancer (NSCLC) patients. The objective of this study was to develop and validate a survival prediction model in a cohort of inoperable stage I-III NSCLC patients treated with radiotherapy. Methods Data from inoperable stage I-III NSCLC patients diagnosed from 1/1/2016 to 31/12/2017 were collected from three radiation oncology clinics. Patient, tumour and treatment-related variables were selected for model inclusion using univariate and multivariate analysis. Cox proportional hazards regression was used to develop a 2-year overall survival prediction model, the South West Sydney Model (SWSM) in one clinic (n = 117) and validated in the other clinics (n = 144). Model performance, assessed internally and on one independent dataset, was expressed as Harrell’s concordance index (c-index). Results The SWSM contained five variables: Eastern Cooperative Oncology Group performance status, diffusing capacity of the lung for carbon monoxide, histological diagnosis, tumour lobe and equivalent dose in 2 Gy fractions. The SWSM yielded a c-index of 0.70 on internal validation and 0.72 on external validation. Survival probability could be stratified into three groups using a risk score derived from the model. Conclusions A 2-year survival model with good discrimination was developed. The model included tumour lobe as a novel variable and has the potential to guide treatment decisions. Further validation is needed in a larger patient cohort.
Collapse
Affiliation(s)
- Natalie Si-Yi Lee
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Jesmin Shafiq
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.,Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
| | - Matthew Field
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.,Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
| | | | - Suganthy Varadarajan
- Blacktown Cancer and Haematology Centre, Blacktown Hospital, Blacktown, NSW, Australia
| | | | - Eric Hau
- Blacktown Cancer and Haematology Centre, Blacktown Hospital, Blacktown, NSW, Australia.,Crown Princess Mary Cancer Centre, Westmead Hospital, Westmead, NSW, Australia.,University of Sydney, Sydney, NSW, Australia
| | - Shalini Kavita Vinod
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia. .,Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia. .,Cancer Therapy Centre, Liverpool Hospital, Locked Bag 7103, Liverpool BC, NSW, 1871, Australia.
| |
Collapse
|
11
|
Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073223] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.
Collapse
|