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Volpe S, Pepa M, Zaffaroni M, Bellerba F, Santamaria R, Marvaso G, Isaksson LJ, Gandini S, Starzyńska A, Leonardi MC, Orecchia R, Alterio D, Jereczek-Fossa BA. Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist. Front Oncol 2021; 11:772663. [PMID: 34869010 PMCID: PMC8637856 DOI: 10.3389/fonc.2021.772663] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 10/25/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND AND PURPOSE Machine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT). MATERIALS AND METHODS Electronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1. RESULTS Forty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation). DISCUSSION AND CONCLUSION The range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young. Overall, if not safe yet, ML is most probably a bet worth making.
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Affiliation(s)
- Stefania Volpe
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Federica Bellerba
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Riccardo Santamaria
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Giulia Marvaso
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Lars Johannes Isaksson
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Sara Gandini
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Anna Starzyńska
- Department of Oral Surgery, Medical University of Gdańsk, Gdańsk, Poland
| | - Maria Cristina Leonardi
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Roberto Orecchia
- Scientific Directorate, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Daniela Alterio
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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Vrtovec T, Močnik D, Strojan P, Pernuš F, Ibragimov B. Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods. Med Phys 2020; 47:e929-e950. [PMID: 32510603 DOI: 10.1002/mp.14320] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 05/27/2020] [Accepted: 05/29/2020] [Indexed: 02/06/2023] Open
Abstract
Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor cells while sparing the healthy tissues. For this purpose, target volumes and OARs have to be delineated and segmented from medical images. As manual delineation is a tedious and time-consuming task subjected to intra/interobserver variability, computerized auto-segmentation has been developed as an alternative. The field of medical imaging and RT planning has experienced an increased interest in the past decade, with new emerging trends that shifted the field of H&N OAR auto-segmentation from atlas-based to deep learning-based approaches. In this review, we systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date, and provided critical discussions and recommendations from various perspectives: image modality - both computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OAR - the spinal cord, brainstem, and major salivary glands are the most studied OARs, but additional experiments should be conducted for several less studied soft tissue structures; image database - several image databases with the corresponding ground truth are currently available for methodology evaluation, but should be augmented with data from multiple observers and multiple institutions; methodology - current methods have shifted from atlas-based to deep learning auto-segmentation, which is expected to become even more sophisticated; ground truth - delineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metrics - the Dice coefficient as the standard volumetric overlap metrics should be accompanied with at least one distance metrics, and combined with clinical acceptability scores and risk assessments; segmentation performance - the best performing methods achieve clinically acceptable auto-segmentation for several OARs, however, the dosimetric impact should be also studied to provide clinically relevant endpoints for RT planning.
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Affiliation(s)
- Tomaž Vrtovec
- Faculty Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana, SI-1000, Slovenia
| | - Domen Močnik
- Faculty Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana, SI-1000, Slovenia
| | - Primož Strojan
- Institute of Oncology Ljubljana, Zaloška cesta 2, Ljubljana, SI-1000, Slovenia
| | - Franjo Pernuš
- Faculty Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana, SI-1000, Slovenia
| | - Bulat Ibragimov
- Faculty Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana, SI-1000, Slovenia.,Department of Computer Science, University of Copenhagen, Universitetsparken 1, Copenhagen, D-2100, Denmark
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Gurney-Champion OJ, Kieselmann JP, Wong KH, Ng-Cheng-Hin B, Harrington K, Oelfke U. A convolutional neural network for contouring metastatic lymph nodes on diffusion-weighted magnetic resonance images for assessment of radiotherapy response. Phys Imaging Radiat Oncol 2020; 15:1-7. [PMID: 33043156 PMCID: PMC7536306 DOI: 10.1016/j.phro.2020.06.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 06/09/2020] [Accepted: 06/09/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND AND PURPOSE Retrieving quantitative parameters from magnetic resonance imaging (MRI), e.g. for early assessment of radiotherapy treatment response, necessitates contouring regions of interest, which is time-consuming and prone to errors. This becomes more pressing for daily imaging on MRI-guided radiotherapy systems. Therefore, we trained a deep convolutional neural network to automatically contour involved lymph nodes on diffusion-weighted (DW) MRI of head and neck cancer (HNC) patients receiving radiotherapy. MATERIALS AND METHODS DW-images from 48 HNC patients (18 induction-chemotherapy + chemoradiotherapy; 30 definitive chemoradiotherapy) with 68 involved lymph nodes were obtained on a diagnostic 1.5 T MR-scanner prior to and 2-3 timepoints throughout treatment. A radiation oncologist delineated the lymph nodes on the b = 50 s/mm2 images. A 3D U-net was trained to contour involved lymph nodes. Its performance was evaluated in all 48 patients using 8-fold cross-validation and calculating the Dice similarity coefficient (DSC) and the absolute difference in median apparent diffusion coefficient (ΔADC) between the manual and generated contours. Additionally, the performance was evaluated in an independent dataset of three patients obtained on a 1.5 T MR-Linac. RESULTS In the definitive chemoradiotherapy patients (n = 96 patients/lymphnodes/timepoints) the DSC was 0.87 (0.81-0.91) [median (1st-3rd quantiles)] and ΔADC was 1.9% (0.8-3.4%) and both remained stable throughout treatment. The network performed worse in the patients receiving induction-chemotherapy (n = 65), with DSC = 0.80 (0.71-0.87) and ΔADC = 3.3% (1.6-8.0%). The network performed well on the MR-Linac data (n = 8) with DSC = 0.80 (0.75-0.82) and ΔADC = 4.0% (0.6-9.1%). CONCLUSIONS We established accurate automatic contouring of involved lymph nodes for HNC patients on diagnostic and MR-Linac DW-images.
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Affiliation(s)
- Oliver J. Gurney-Champion
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Jennifer P. Kieselmann
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Kee H. Wong
- The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Brian Ng-Cheng-Hin
- Targeted Therapy Team, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Kevin Harrington
- Targeted Therapy Team, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Uwe Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
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Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach. Sci Rep 2019; 9:4126. [PMID: 30858409 PMCID: PMC6411877 DOI: 10.1038/s41598-019-40584-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 02/13/2019] [Indexed: 01/08/2023] Open
Abstract
In radiotherapy, computed tomography (CT) datasets are mostly used for radiation treatment planning to achieve a high-conformal tumor coverage while optimally sparing healthy tissue surrounding the tumor, referred to as organs-at-risk (OARs). Based on CT scan and/or magnetic resonance images, OARs have to be manually delineated by clinicians, which is one of the most time-consuming tasks in the clinical workflow. Recent multi-atlas (MA) or deep-learning (DL) based methods aim to improve the clinical routine by an automatic segmentation of OARs on a CT dataset. However, so far no studies investigated the performance of these MA or DL methods on dual-energy CT (DECT) datasets, which have been shown to improve the image quality compared to conventional 120 kVp single-energy CT. In this study, the performance of an in-house developed MA and a DL method (two-step three-dimensional U-net) was quantitatively and qualitatively evaluated on various DECT-derived pseudo-monoenergetic CT datasets ranging from 40 keV to 170 keV. At lower energies, the MA method resulted in more accurate OAR segmentations. Both the qualitative and quantitative metric analysis showed that the DL approach often performed better than the MA method.
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Yang X, Rossi P, Ogunleye T, Jani AB, Curran WJ, Liu T. A New CT Prostate Segmentation for CT-Based HDR Brachytherapy. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2014; 9036:90362K. [PMID: 25821388 DOI: 10.1117/12.2043695] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
High-dose-rate (HDR) brachytherapy has become a popular treatment modality for localized prostate cancer. Prostate HDR treatment involves placing 10 to 20 catheters (needles) into the prostate gland, and then delivering radiation dose to the cancerous regions through these catheters. These catheters are often inserted with transrectal ultrasound (TRUS) guidance and the HDR treatment plan is based on the CT images. The main challenge for CT-based HDR planning is to accurately segment prostate volume in CT images due to the poor soft tissue contrast and additional artifacts introduced by the catheters. To overcome these limitations, we propose a novel approach to segment the prostate in CT images through TRUS-CT deformable registration based on the catheter locations. In this approach, the HDR catheters are reconstructed from the intra-operative TRUS and planning CT images, and then used as landmarks for the TRUS-CT image registration. The prostate contour generated from the TRUS images captured during the ultrasound-guided HDR procedure was used to segment the prostate on the CT images through deformable registration. We conducted two studies. A prostate-phantom study demonstrated a submillimeter accuracy of our method. A pilot study of 5 prostate-cancer patients was conducted to further test its clinical feasibility. All patients had 3 gold markers implanted in the prostate that were used to evaluate the registration accuracy, as well as previous diagnostic MR images that were used as the gold standard to assess the prostate segmentation. For the 5 patients, the mean gold-marker displacement was 1.2 mm; the prostate volume difference between our approach and the MRI was 7.2%, and the Dice volume overlap was over 91%. Our proposed method could improve prostate delineation, enable accurate dose planning and delivery, and potentially enhance prostate HDR treatment outcome.
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Affiliation(s)
- Xiaofeng Yang
- Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Peter Rossi
- Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Tomi Ogunleye
- Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Ashesh B Jani
- Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Walter J Curran
- Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Tian Liu
- Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, GA 30322
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