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Berenato S, Williams M, Woodley O, Möhler C, Evans E, Millin AE, Wheeler PA. Novel dosimetric validation of a commercial CT scanner based deep learning automated contour solution for prostate radiotherapy. Phys Med 2024; 122:103339. [PMID: 38718703 DOI: 10.1016/j.ejmp.2024.103339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 06/13/2024] Open
Abstract
PURPOSE OAR delineation accuracy influences: (i) a patient's optimised dose distribution (PD), (ii) the reported doses (RD) presented at approval, which represent plan quality. This study utilised a novel dosimetric validation methodology, comprehensively evaluating a new CT-scanner-based AI contouring solution in terms of PD and RD within an automated planning workflow. METHODS 20 prostate patients were selected to evaluate AI contouring for rectum, bladder, and proximal femurs. Five planning 'pipelines' were considered; three using AI contours with differing levels of manual editing (nominally none (AIStd), minor editing in specific regions (AIMinEd), and fully corrected (AIFullEd)). Remaining pipelines were manual delineations from two observers (MDOb1, MDOb2). Automated radiotherapy plans were generated for each pipeline. Geometric and dosimetric agreement of contour sets AIStd, AIMinEd, AIFullEd and MDOb2 were evaluated against the reference set MDOb1. Non-inferiority of AI pipelines was assessed, hypothesising that compared to MDOb1, absolute deviations in metrics for AI contouring were no greater than that from MDOb2. RESULTS Compared to MDOb1, organ delineation time was reduced by 24.9 min (96 %), 21.4 min (79 %) and 12.2 min (45 %) for AIStd, AIMinEd and AIFullEd respectively. All pipelines exhibited generally good dosimetric agreement with MDOb1. For RD, median deviations were within ± 1.8 cm3, ± 1.7 % and ± 0.6 Gy for absolute volume, relative volume and mean dose metrics respectively. For PD, respective values were within ± 0.4 cm3, ± 0.5 % and ± 0.2 Gy. Statistically (p < 0.05), AIMinEd and AIFullEd were dosimetrically non-inferior to MDOb2. CONCLUSIONS This novel dosimetric validation demonstrated that following targeted minor editing (AIMinEd), AI contours were dosimetrically non-inferior to manual delineations, reducing delineation time by 79 %.
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Affiliation(s)
- Salvatore Berenato
- Velindre Cancer Centre, Radiotherapy Physics Department, Cardiff, Wales, United Kingdom
| | - Matthew Williams
- Velindre Cancer Centre, Radiotherapy Physics Department, Cardiff, Wales, United Kingdom
| | - Owain Woodley
- Velindre Cancer Centre, Radiotherapy Physics Department, Cardiff, Wales, United Kingdom
| | | | - Elin Evans
- Velindre Cancer Centre, Medical Directorate, Cardiff, Wales, United Kingdom
| | - Anthony E Millin
- Velindre Cancer Centre, Radiotherapy Physics Department, Cardiff, Wales, United Kingdom
| | - Philip A Wheeler
- Velindre Cancer Centre, Radiotherapy Physics Department, Cardiff, Wales, United Kingdom.
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Polymeri E, Johnsson ÅA, Enqvist O, Ulén J, Pettersson N, Nordström F, Kindblom J, Trägårdh E, Edenbrandt L, Kjölhede H. Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography. Adv Radiat Oncol 2024; 9:101383. [PMID: 38495038 PMCID: PMC10943520 DOI: 10.1016/j.adro.2023.101383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/30/2023] [Indexed: 03/19/2024] Open
Abstract
Purpose Meticulous manual delineations of the prostate and the surrounding organs at risk are necessary for prostate cancer radiation therapy to avoid side effects to the latter. This process is time consuming and hampered by inter- and intraobserver variability, all of which could be alleviated by artificial intelligence (AI). This study aimed to evaluate the performance of AI compared with manual organ delineations on computed tomography (CT) scans for radiation treatment planning. Methods and Materials Manual delineations of the prostate, urinary bladder, and rectum of 1530 patients with prostate cancer who received curative radiation therapy from 2006 to 2018 were included. Approximately 50% of those CT scans were used as a training set, 25% as a validation set, and 25% as a test set. Patients with hip prostheses were excluded because of metal artifacts. After training and fine-tuning with the validation set, automated delineations of the prostate and organs at risk were obtained for the test set. Sørensen-Dice similarity coefficient, mean surface distance, and Hausdorff distance were used to evaluate the agreement between the manual and automated delineations. Results The median Sørensen-Dice similarity coefficient between the manual and AI delineations was 0.82, 0.95, and 0.88 for the prostate, urinary bladder, and rectum, respectively. The median mean surface distance and Hausdorff distance were 1.7 and 9.2 mm for the prostate, 0.7 and 6.7 mm for the urinary bladder, and 1.1 and 13.5 mm for the rectum, respectively. Conclusions Automated CT-based organ delineation for prostate cancer radiation treatment planning is feasible and shows good agreement with manually performed contouring.
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Affiliation(s)
- Eirini Polymeri
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Radiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Åse A. Johnsson
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Radiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Olof Enqvist
- Department of Electrical Engineering, Region Västra Götaland, Chalmers University of Technology, Gothenburg, Sweden
- Eigenvision AB, Malmö, Sweden
| | | | - Niclas Pettersson
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Fredrik Nordström
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jon Kindblom
- Department of Oncology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Elin Trägårdh
- Department of Clinical Physiology and Nuclear Medicine, Lund University and Skåne University Hospital, Malmö, Sweden
| | - Lars Edenbrandt
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Henrik Kjölhede
- Department of Urology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Urology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
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Rayn K, Gokhroo G, Jeffers B, Gupta V, Chaudhari S, Clark R, Magliari A, Beriwal S. Multicenter Study of Pelvic Nodal Autosegmentation Algorithm of Siemens Healthineers: Comparison of Male Versus Female Pelvis. Adv Radiat Oncol 2024; 9:101326. [PMID: 38405314 PMCID: PMC10885554 DOI: 10.1016/j.adro.2023.101326] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 07/18/2023] [Indexed: 02/27/2024] Open
Abstract
Purpose The autosegmentation algorithm of Siemens Healthineers version VA 30 (AASH) (Siemens Healthineers, Erlangen, Germany) was trained and developed in the male pelvis, with no published data on its usability in the female pelvis. This is the first multi-institutional study to describe and evaluate an artificial intelligence algorithm for autosegmentation of the pelvic nodal region by gender. Methods and Materials We retrospectively evaluated AASH pelvic nodal autosegmentation in both male and female patients treated at our network of institutions. The automated pelvic nodal contours generated by AASH were evaluated by 1 board-certified radiation oncologist. A 4-point scale was used for each nodal region contour: a score of 4 is clinically usable with minimal edits; a score of 3 requires minor edits (missing nodal contour region, cutting through vessels, or including bowel loops) in 3 or fewer computed tomography slices; a score of 2 requires major edits, as previously defined but in 4 or more computed tomography slices; and a score of 1 requires complete recontouring of the region. Pelvic nodal regions included the right and left side of the common iliac, external iliac, internal iliac, obturator, and midline presacral nodes. In addition, patients were graded based on their lowest nodal contour score. Statistical analysis was performed using Fisher exact tests and Yates-corrected χ2 tests. Results Fifty-two female and 51 male patients were included in the study, representing a total of 468 and 447 pelvic nodal regions, respectively. Ninety-six percent and 99% of contours required minor edits at most (score of 3 or 4) for female and male patients, respectively (P = .004 using Fisher exact test; P = .007 using Yates correction). No nodal regions had a statistically significant difference in scores between female and male patients. The percentage of patients requiring no more than minor edits was 87% (45 patients) and 92% (47 patients) for female and male patients, respectively (P = .53 using Fisher exact test; P = .55 using Yates correction). Conclusions AASH pelvic nodal autosegmentation performed very well in both male and female pelvic nodal regions, although with better male pelvic nodal autosegmentation. As autosegmentation becomes more widespread, it may be important to have equal representation from all sexes in training and validation of autosegmentation algorithms.
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Affiliation(s)
- Kareem Rayn
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, New York
- Varian Medical Systems Inc, Palo Alto, California
| | | | - Brian Jeffers
- Columbia University Vagelos College of Physicians and Surgeons, New York, New York
| | - Vibhor Gupta
- American Oncology Institute, Hyderabad, CA, India
| | | | - Ryan Clark
- Varian Medical Systems Inc, Palo Alto, California
| | | | - Sushil Beriwal
- Varian Medical Systems Inc, Palo Alto, California
- Division of Radiation Oncology, Allegheny Health Network Cancer Institute, Pittsburgh, Pennsylvania
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Wang E, Yen A, Hrycushko B, Wang S, Lin J, Zhong X, Dohopolski M, Nwachukwu C, Iqbal Z, Albuquerque K. The accuracy of artificial intelligence deformed nodal structures in cervical online cone-beam-based adaptive radiotherapy. Phys Imaging Radiat Oncol 2024; 29:100546. [PMID: 38369990 PMCID: PMC10869256 DOI: 10.1016/j.phro.2024.100546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/31/2024] [Accepted: 02/01/2024] [Indexed: 02/20/2024] Open
Abstract
Background and Purpose Online cone-beam-based adaptive radiotherapy (ART) adjusts for anatomical changes during external beam radiotherapy. However, limited cone-beam image quality complicates nodal contouring. Despite this challenge, artificial-intelligence guided deformation (AID) can auto-generate nodal contours. Our study investigated the optimal use of such contours in cervical online cone-beam-based ART. Materials and Methods From 136 adaptive fractions across 21 cervical cancer patients with nodal disease, we extracted 649 clinically-delivered and AID clinical target volume (CTV) lymph node boost structures. We assessed geometric alignment between AID and clinical CTVs via dice similarity coefficient, and 95% Hausdorff distance, and geometric coverage of clinical CTVs by AID planning target volumes by false positive dice. Coverage of clinical CTVs by AID contour-based plans was evaluated using D100, D95, V100%, and V95%. Results Between AID and clinical CTVs, the median dice similarity coefficient was 0.66 and the median 95 % Hausdorff distance was 4.0 mm. The median false positive dice of clinical CTV coverage by AID planning target volumes was 0. The median D100 was 1.00, the median D95 was 1.01, the median V100% was 1.00, and the median V95% was 1.00. Increased nodal volume, fraction number, and daily adaptation were associated with reduced clinical CTV coverage by AID-based plans. Conclusion In one of the first reports on pelvic nodal ART, AID-based plans could adequately cover nodal targets. However, physician review is required due to performance variation. Greater attention is needed for larger, daily-adapted nodes further into treatment.
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Affiliation(s)
- Ethan Wang
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Allen Yen
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Brian Hrycushko
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Siqiu Wang
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Jingyin Lin
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Xinran Zhong
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Michael Dohopolski
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Chika Nwachukwu
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Zohaib Iqbal
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
| | - Kevin Albuquerque
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States
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Solbakken AM, Sellevold S, Spasojevic M, Julsrud L, Emblemsvåg HL, Reims HM, Sørensen O, Thorgersen EB, Fauske L, Ågren JSM, Brennhovd B, Ryder T, Larsen SG, Flatmark K. Navigation-Assisted Surgery for Locally Advanced Primary and Recurrent Rectal Cancer. Ann Surg Oncol 2023; 30:7602-7611. [PMID: 37481493 PMCID: PMC10562504 DOI: 10.1245/s10434-023-13964-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/03/2023] [Indexed: 07/24/2023]
Abstract
BACKGROUND In some surgical disciplines, navigation-assisted surgery has become standard of care, but in rectal cancer, indications for navigation and the utility of different technologies remain undetermined. METHODS The NAVI-LARRC prospective study (NCT04512937; IDEAL Stage 2a) evaluated feasibility of navigation in patients with locally advanced primary (LARC) and recurrent rectal cancer (LRRC). Included patients had advanced tumours with high risk of incomplete (R1/R2) resection, and navigation was considered likely to improve the probability of complete resection (R0). Tumours were classified according to pelvic compartmental involvement, as suggested by the Royal Marsden group. The BrainlabTM navigation platform was used for preoperative segmentation of tumour and pelvic anatomy, and for intraoperative navigation with optical tracking. R0 resection rates, surgeons' experiences, and adherence to the preoperative resection plan were assessed. RESULTS Seventeen patients with tumours involving the posterior/lateral compartments underwent navigation-assisted procedures. Fifteen patients required abdominosacral resection, and 3 had resection of the sciatic nerve. R0 resection was obtained in 6/8 (75%) LARC and 6/9 (69%) LRRC cases. Preoperative segmentation was time-consuming (median 3.5 h), but intraoperative navigation was accurate. Surgeons reported navigation to be feasible, and adherence to the resection plan was satisfactory. CONCLUSIONS Navigation-assisted surgery using optical tracking was feasible. The preoperative planning was time-consuming, but intraoperative navigation was accurate and resulted in acceptable R0 resection rates. Selected patients are likely to benefit from navigation-assisted surgery.
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Affiliation(s)
- Arne M Solbakken
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Simen Sellevold
- Department of Orthopaedic Oncology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Milan Spasojevic
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Lars Julsrud
- Department of Radiology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Hanne-Line Emblemsvåg
- Department of Radiology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Henrik M Reims
- Department of Pathology, Rikshospitalet, Oslo University Hospital, Oslo, Norway
| | - Olaf Sørensen
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Ebbe B Thorgersen
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Lena Fauske
- Department of Oncology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
- Department of Interdisciplinary Health Sciences, Institute of Health and Society, University of Oslo, Oslo, Norway
| | | | - Bjørn Brennhovd
- Department of Urology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Truls Ryder
- Department of Oncologic Plastic Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Stein G Larsen
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Kjersti Flatmark
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Tumour Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
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Vivancos Bargalló H, Stick LB, Korreman SS, Kronborg C, Nielsen MM, Borgen AC, Offersen BV, Nørrevang O, Kallehauge JF. Classification of laterality and mastectomy/lumpectomy for breast cancer patients for improved performance of deep learning auto segmentation. Acta Oncol 2023; 62:1546-1550. [PMID: 37584197 DOI: 10.1080/0284186x.2023.2245965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/03/2023] [Indexed: 08/17/2023]
Affiliation(s)
- Helena Vivancos Bargalló
- Medical Physics department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | | | - Stine Sofia Korreman
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Camilla Kronborg
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Mathias M Nielsen
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | | | - Birgitte Vrou Offersen
- Department of Experimental Clinical Oncologyy, Aarhus University Hospital, Aarhus, Denmark
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Ole Nørrevang
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Jesper F Kallehauge
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Palazzo G, Mangili P, Deantoni C, Fodor A, Broggi S, Castriconi R, Ubeira Gabellini MG, del Vecchio A, Di Muzio NG, Fiorino C. Real-world validation of Artificial Intelligence-based Computed Tomography auto-contouring for prostate cancer radiotherapy planning. Phys Imaging Radiat Oncol 2023; 28:100501. [PMID: 37920450 PMCID: PMC10618761 DOI: 10.1016/j.phro.2023.100501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/04/2023] Open
Abstract
Background and purpose Artificial Intelligence (AI)-based auto-contouring for treatment planning in radiotherapy needs extensive clinical validation, including the impact of editing after automatic segmentation. The aims of this study were to assess the performance of a commercial system for Clinical Target Volumes (CTVs) (prostate/seminal vesicles) and selected Organs at Risk (OARs) (rectum/bladder/femoral heads + femurs), evaluating also inter-observer variability (manual vs automatic + editing) and the reduction of contouring time. Materials and methods Two expert observers contoured CTVs/OARs of 20 patients in our Treatment Planning System (TPS). Computed Tomography (CT) images were sent to the automatic contouring workstation: automatic contours were generated and sent back to TPS, where observers could edit them if necessary. Inter- and intra-observer consistency was estimated using Dice Similarity Coefficients (DSC). Radiation oncologists were also asked to score the quality of automatic contours, ranging from 1 (complete re-contouring) to 5 (no editing). Contouring times (manual vs automatic + edit) were compared. Results DSCs (manual vs automatic only) were consistent with inter-observer variability (between 0.65 for seminal vesicles and 0.94 for bladder); editing further improved performances (range: 0.76-0.94). The median clinical score was 4 (little editing) and it was <4 in 3/2 patients for the two observers respectively. Inter-observer variability of automatic + editing contours improved significantly, being lower than manual contouring (e.g.: seminal vesicles: 0.83vs0.73; prostate: 0.86vs0.83; rectum: 0.96vs0.81). Oncologist contouring time reduced from 17 to 24 min of manual contouring time to 3-7 min of editing time for the two observers (p < 0.01). Conclusion Automatic contouring with a commercial AI-based system followed by editing can replace manual contouring, resulting in significantly reduced time for segmentation and better consistency between operators.
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Affiliation(s)
- Gabriele Palazzo
- Medical Physics, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Paola Mangili
- Medical Physics, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Chiara Deantoni
- Radiotherapy, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Andrei Fodor
- Radiotherapy, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Sara Broggi
- Medical Physics, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | | | | | | | - Nadia G. Di Muzio
- Radiotherapy, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Italy
| | - Claudio Fiorino
- Medical Physics, IRCCS San Raffaele Scientific Institute, Milano, Italy
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Zhu X, Shao L, Liu Z, Liu Z, He J, Liu J, Ping H, Lu J. MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer. J Zhejiang Univ Sci B 2023; 24:663-681. [PMID: 37551554 PMCID: PMC10423970 DOI: 10.1631/jzus.b2200619] [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: 12/01/2022] [Accepted: 04/11/2023] [Indexed: 08/09/2023]
Abstract
Prostate cancer (PCa) is a pernicious tumor with high heterogeneity, which creates a conundrum for making a precise diagnosis and choosing an optimal treatment approach. Multiparametric magnetic resonance imaging (mp-MRI) with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of PCa. Moreover, using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence (AI) and image data processing. Radiomics acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise evaluation. Radiomics models provide a reliable and noninvasive alternative to aid in precision medicine, demonstrating advantages over traditional models based on clinicopathological parameters. The purpose of this review is to provide an overview of related studies of radiomics in PCa, specifically around the development and validation of radiomics models using MRI-derived image features. The current landscape of the literature, focusing mainly on PCa detection, aggressiveness, and prognosis evaluation, is reviewed and summarized. Rather than studies that exclusively focus on image biomarker identification and method optimization, models with high potential for universal clinical implementation are identified. Furthermore, we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical scenario. This review will encourage researchers to design models based on actual clinical needs, as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics.
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Affiliation(s)
- Xuehua Zhu
- Department of Urology, Peking University Third Hospital, Beijing 100191, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100080, China
| | - Zenan Liu
- Department of Urology, Peking University Third Hospital, Beijing 100191, China
| | - Jide He
- Department of Urology, Peking University Third Hospital, Beijing 100191, China
| | - Jiangang Liu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, Beijing 100191, China
| | - Hao Ping
- Department of Urology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
| | - Jian Lu
- Department of Urology, Peking University Third Hospital, Beijing 100191, China.
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Bhattacharjee A, Rabea S, Bhattacharjee A, Elkaeed EB, Murugan R, Selim HMRM, Sahu RK, Shazly GA, Salem Bekhit MM. A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images. Front Oncol 2023; 13:1193746. [PMID: 37333825 PMCID: PMC10272771 DOI: 10.3389/fonc.2023.1193746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/04/2023] [Indexed: 06/20/2023] Open
Abstract
Lung cancer is a fatal disease caused by an abnormal proliferation of cells in the lungs. Similarly, chronic kidney disorders affect people worldwide and can lead to renal failure and impaired kidney function. Cyst development, kidney stones, and tumors are frequent diseases impairing kidney function. Since these conditions are generally asymptomatic, early, and accurate identification of lung cancer and renal conditions is necessary to prevent serious complications. Artificial Intelligence plays a vital role in the early detection of lethal diseases. In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned network for automatic lung and kidney computed tomography multi-class image classification. The proposed model obtained 99.39% accuracy, 99.33% precision, 98% recall, and 98.67% F1-score for lung cancer multi-class classification. Whereas, it attained 100% accuracy, F1 score, recall and precision for kidney disease multi-class classification. Also, the proposed modified Xception model outperformed the original Xception model and the existing methods. Hence, it can serve as a support tool to the radiologists and nephrologists for early detection of lung cancer and chronic kidney disease, respectively.
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Affiliation(s)
- Ananya Bhattacharjee
- Bio-Medical Imaging Laboratory (BIOMIL), Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India
| | - Sameh Rabea
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh, Saudi Arabia
| | - Abhishek Bhattacharjee
- Department of Pharmaceutical Sciences, Assam University (A Central University), Silchar, India
| | - Eslam B. Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh, Saudi Arabia
| | - R. Murugan
- Bio-Medical Imaging Laboratory (BIOMIL), Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India
| | - Heba Mohammed Refat M. Selim
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh, Saudi Arabia
- Microbiology and Immunology Department, Faculty of Pharmacy (Girls); Al-Azhar University, Cairo, Egypt
| | - Ram Kumar Sahu
- Department of Pharmaceutical Sciences, Hemvati Nandan Bahuguna Garhwal University (A Central University), Tehri Garhwal, India
| | - Gamal A. Shazly
- Kayyali Chair for Pharmaceutical Industry, Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mounir M. Salem Bekhit
- Kayyali Chair for Pharmaceutical Industry, Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
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Liang Y, Zhao Q, Hu Z, Bo K, Meyyappan S, Neubert JK, Ding M. Imaging the neural substrate of trigeminal neuralgia pain using deep learning. Front Hum Neurosci 2023; 17:1144159. [PMID: 37275345 PMCID: PMC10232768 DOI: 10.3389/fnhum.2023.1144159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/04/2023] [Indexed: 06/07/2023] Open
Abstract
Trigeminal neuralgia (TN) is a severe and disabling facial pain condition and is characterized by intermittent, severe, electric shock-like pain in one (or more) trigeminal subdivisions. This pain can be triggered by an innocuous stimulus or can be spontaneous. Presently available therapies for TN include both surgical and pharmacological management; however, the lack of a known etiology for TN contributes to the unpredictable response to treatment and the variability in long-term clinical outcomes. Given this, a range of peripheral and central mechanisms underlying TN pain remain to be understood. We acquired functional magnetic resonance imaging (fMRI) data from TN patients who (1) rested comfortably in the scanner during a resting state session and (2) rated their pain levels in real time using a calibrated tracking ball-controlled scale in a pain tracking session. Following data acquisition, the data was analyzed using the conventional correlation analysis and two artificial intelligence (AI)-inspired deep learning methods: convolutional neural network (CNN) and graph convolutional neural network (GCNN). Each of the three methods yielded a set of brain regions related to the generation and perception of pain in TN. There were 6 regions that were identified by all three methods, including the superior temporal cortex, the insula, the fusiform, the precentral gyrus, the superior frontal gyrus, and the supramarginal gyrus. Additionally, 17 regions, including dorsal anterior cingulate cortex (dACC) and the thalamus, were identified by at least two of the three methods. Collectively, these 23 regions are taken to represent signature centers of TN pain and provide target areas for future studies seeking to understand the central mechanisms of TN.
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Affiliation(s)
- Yun Liang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Qing Zhao
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Zhenhong Hu
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Ke Bo
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Sreenivasan Meyyappan
- Center for Mind and Brain, University of California, Davis, Davis, CA, United States
| | - John K. Neubert
- Department of Orthodontics, University of Florida, Gainesville, FL, United States
| | - Mingzhou Ding
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
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Eidex Z, Ding Y, Wang J, Abouei E, Qiu RL, Liu T, Wang T, Yang X. Deep Learning in MRI-guided Radiation Therapy: A Systematic Review. ARXIV 2023:arXiv:2303.11378v2. [PMID: 36994167 PMCID: PMC10055493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed with emphasis placed on underlying methods. Studies are further categorized into the areas of segmentation, synthesis, radiomics, and real time MRI. Finally, clinical implications, current challenges, and future directions are discussed.
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Affiliation(s)
- Zach Eidex
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
- School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Yifu Ding
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Jing Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Elham Abouei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Richard L.J. Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Tian Liu
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Tonghe Wang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
- School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
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Coroamă DM, Dioșan L, Telecan T, Andras I, Crișan N, Medan P, Andreica A, Caraiani C, Lebovici A, Boca B, Bálint Z. Fully automated bladder tumor segmentation from T2 MRI images using 3D U-Net algorithm. Front Oncol 2023; 13:1096136. [PMID: 36969047 PMCID: PMC10033524 DOI: 10.3389/fonc.2023.1096136] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
IntroductionBladder magnetic resonance imaging (MRI) has been recently integrated in the diagnosis pathway of bladder cancer. However, automatic recognition of suspicious lesions is still challenging. Thus, development of a solution for proper delimitation of the tumor and its separation from the healthy tissue is of primordial importance. As a solution to this unmet medical need, we aimed to develop an artificial intelligence-based decision support system, which automatically segments the bladder wall and the tumor as well as any suspect area from the 3D MRI images.MaterialsWe retrospectively assessed all patients diagnosed with bladder cancer, who underwent MRI at our department (n=33). All examinations were performed using a 1.5 Tesla MRI scanner. All images were reviewed by two radiologists, who performed manual segmentation of the bladder wall and all lesions. First, the performance of our fully automated end-to-end segmentation model based on a 3D U-Net architecture (by considering various depths of 4, 5 or 6 blocks) trained in two data augmentation scenarios (on 5 and 10 augmentation datasets per original data, respectively) was tested. Second, two learning setups were analyzed by training the segmentation algorithm with 7 and 14 MRI original volumes, respectively.ResultsWe obtained a Dice-based performance over 0.878 for automatic segmentation of bladder wall and tumors, as compared to manual segmentation. A larger training dataset using 10 augmentations for 7 patients could further improve the results of the U-Net-5 model (0.902 Dice coefficient at image level). This model performed best in terms of automated segmentation of bladder, as compared to U-Net-4 and U-Net-6. However, in this case increased time for learning was needed as compared to U-Net-4. We observed that an extended dataset for training led to significantly improved segmentation of the bladder wall, but not of the tumor.ConclusionWe developed an intelligent system for bladder tumors automated diagnostic, that uses a deep learning model to segment both the bladder wall and the tumor. As a conclusion, low complexity networks, with less than five-layers U-Net architecture are feasible and show good performance for automatic 3D MRI image segmentation in patients with bladder tumors.
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Affiliation(s)
- Diana Mihaela Coroamă
- Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Laura Dioșan
- Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Teodora Telecan
- Department of Urology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Urology, Municipal Clinical Hospital, Cluj-Napoca, Romania
- *Correspondence: Zoltán Bálint, ; Teodora Telecan,
| | - Iulia Andras
- Department of Urology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Urology, Municipal Clinical Hospital, Cluj-Napoca, Romania
| | - Nicolae Crișan
- Department of Urology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Urology, Municipal Clinical Hospital, Cluj-Napoca, Romania
| | - Paul Medan
- Department of Urology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Urology, Municipal Clinical Hospital, Cluj-Napoca, Romania
| | - Anca Andreica
- Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Cosmin Caraiani
- Department of Medical Imaging, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Andrei Lebovici
- Department of Radiology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Radiology, Emergency Clinical County Hospital of Cluj-Napoca, Cluj-Napoca, Romania
| | - Bianca Boca
- Department of Medical Imaging, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Radiology, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, Târgu Mureș, Romania
| | - Zoltán Bálint
- Department of Biomolecular Physics, Faculty of Physics, Babeș-Bolyai University, Cluj-Napoca, Romania
- *Correspondence: Zoltán Bálint, ; Teodora Telecan,
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VilasBoas-Ribeiro I, Franckena M, van Rhoon GC, Hernández-Tamames JA, Paulides MM. Using MRI to measure position and anatomy changes and assess their impact on the accuracy of hyperthermia treatment planning for cervical cancer. Int J Hyperthermia 2022; 40:2151648. [PMID: 36535922 DOI: 10.1080/02656736.2022.2151648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
PURPOSE We studied the differences between planning and treatment position, their impact on the accuracy of hyperthermia treatment planning (HTP) predictions, and the relevance of including true treatment anatomy and position in HTP based on magnetic resonance (MR) images. MATERIALS AND METHODS All volunteers were scanned with an MR-compatible hyperthermia device, including a filled waterbolus, to replicate the treatment setup. In the planning setup, the volunteers were scanned without the device to reproduce the imaging in the current HTP. First, we used rigid registration to investigate the patient position displacements between the planning and treatment setup. Second, we performed HTP for the planning anatomy at both positions and the treatment mimicking anatomy to study the effects of positioning and anatomy on the quality of the simulated hyperthermia treatment. Treatment quality was evaluated using SAR-based parameters. RESULTS We found an average displacement of 2 cm between planning and treatment positions. These displacements caused average absolute differences of ∼12% for TC25 and 10.4%-15.9% in THQ. Furthermore, we found that including the accurate treatment position and anatomy in treatment planning led to an improvement of 2% in TC25 and 4.6%-10.6% in THQ. CONCLUSIONS This study showed that precise patient position and anatomy are relevant since these affect the accuracy of HTP predictions. The major part of improved accuracy is related to implementing the correct position of the patient in the applicator. Hence, our study shows a clear incentive to accurately match the patient position in HTP with the actual treatment.
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Affiliation(s)
- Iva VilasBoas-Ribeiro
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Martine Franckena
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Gerard C van Rhoon
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Applied Radiation and Isotopes, Reactor Institute Delft, Delft University of Technology, Delft, The Netherlands
| | - Juan A Hernández-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Margarethus M Paulides
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.,Care and Cure research lab (EM-4C&C) of the Electromagnetics Group, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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Yang C, Qin LH, Xie YE, Liao JY. Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis. Radiat Oncol 2022; 17:175. [PMID: 36344989 PMCID: PMC9641941 DOI: 10.1186/s13014-022-02148-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 10/16/2022] [Indexed: 11/09/2022] Open
Abstract
Background This paper attempts to conduct a systematic review and meta-analysis of deep learning (DLs) models for cervical cancer CT image segmentation. Methods Relevant studies were systematically searched in PubMed, Embase, The Cochrane Library, and Web of science. The literature on DLs for cervical cancer CT image segmentation were included, a meta-analysis was performed on the dice similarity coefficient (DSC) of the segmentation results of the included DLs models. We also did subgroup analyses according to the size of the sample, type of segmentation (i.e., two dimensions and three dimensions), and three organs at risk (i.e., bladder, rectum, and femur). This study was registered in PROSPERO prior to initiation (CRD42022307071). Results A total of 1893 articles were retrieved and 14 articles were included in the meta-analysis. The pooled effect of DSC score of clinical target volume (CTV), bladder, rectum, femoral head were 0.86(95%CI 0.84 to 0.87), 0.91(95%CI 0.89 to 0.93), 0.83(95%CI 0.79 to 0.88), and 0.92(95%CI 0.91to 0.94), respectively. For the performance of segmented CTV by two dimensions (2D) and three dimensions (3D) model, the DSC score value for 2D model was 0.87 (95%CI 0.85 to 0.90), while the DSC score for 3D model was 0.85 (95%CI 0.82 to 0.87). As for the effect of the capacity of sample on segmentation performance, no matter whether the sample size is divided into two groups: greater than 100 and less than 100, or greater than 150 and less than 150, the results show no difference (P > 0.05). Four papers reported the time for segmentation from 15 s to 2 min. Conclusion DLs have good accuracy in automatic segmentation of CT images of cervical cancer with a less time consuming and have good prospects for future radiotherapy applications, but still need public high-quality databases and large-scale research verification. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-022-02148-6.
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Artificial intelligence and machine learning in cancer imaging. COMMUNICATIONS MEDICINE 2022; 2:133. [PMID: 36310650 PMCID: PMC9613681 DOI: 10.1038/s43856-022-00199-0] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 10/06/2022] [Indexed: 11/16/2022] Open
Abstract
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.
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Belue MJ, Harmon SA, Patel K, Daryanani A, Yilmaz EC, Pinto PA, Wood BJ, Citrin DE, Choyke PL, Turkbey B. Development of a 3D CNN-based AI Model for Automated Segmentation of the Prostatic Urethra. Acad Radiol 2022; 29:1404-1412. [PMID: 35183438 PMCID: PMC9339453 DOI: 10.1016/j.acra.2022.01.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/10/2022] [Accepted: 01/12/2022] [Indexed: 12/15/2022]
Abstract
RATIONALE AND OBJECTIVE The combined use of prostate cancer radiotherapy and MRI planning is increasingly being used in the treatment of clinically significant prostate cancers. The radiotherapy dosage quantity is limited by toxicity in organs with de-novo genitourinary toxicity occurrence remaining unperturbed. Estimation of the urethral radiation dose via anatomical contouring may improve our understanding of genitourinary toxicity and its related symptoms. Yet, urethral delineation remains an expert-dependent and time-consuming procedure. In this study, we aim to develop a fully automated segmentation tool for the prostatic urethra. MATERIALS AND METHODS This study incorporated 939 patients' T2-weighted MRI scans (train/validation/test/excluded: 657/141/140/1 patients), including in-house and public PROSTATE-x datasets, and their corresponding ground truth urethral contours from an expert genitourinary radiologist. The AI model was developed using MONAI framework and was based on a 3D-UNet. AI model performance was determined by Dice score (volume-based) and the Centerline Distance (CLD) between the prediction and ground truth centers (slice-based). All predictions were compared to ground truth in a systematic failure analysis to elucidate the model's strengths and weaknesses. The Wilcoxon-rank sum test was used for pair-wise comparison of group differences. RESULTS The overall organ-adjusted Dice score for this model was 0.61 and overall CLD was 2.56 mm. When comparing prostates with symmetrical (n = 117) and asymmetrical (n = 23) benign prostate hyperplasia (BPH), the AI model performed better on symmetrical prostates compared to asymmetrical in both Dice score (0.64 vs. 0.51 respectively, p < 0.05) and mean CLD (2.3 mm vs. 3.8 mm respectively, p < 0.05). When calculating location-specific performance, the performance was highest at the apex and lowest at the base location of the prostate for Dice and CLD. Dice location dependence: symmetrical (Apex, Mid, Base: 0.69 vs. 0.67 vs. 0.54 respectively, p < 0.05) and asymmetrical (Apex, Mid, Base: 0.68 vs. 0.52 vs. 0.39 respectively, p < 0.05). CLD location dependence: symmetrical (Apex, Mid, Base: 1.43 mm vs. 2.15 mm vs. 3.28 mm, p < 0.05) and asymmetrical (Apex, Mid, Base: 1.83 mm vs. 3.1 mm vs. 6.24 mm, p < 0.05). CONCLUSION We developed a fully automated prostatic urethra segmentation AI tool yielding its best performance in prostate glands with symmetric BPH features. This system can potentially be used to assist treatment planning in patients who can undergo whole gland radiation therapy or ablative focal therapy.
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Affiliation(s)
- Mason J Belue
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Stephanie A Harmon
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Krishnan Patel
- Radiation Oncology Branch (K.P., D.E.C.), National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Asha Daryanani
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Enis Cagatay Yilmaz
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Peter A Pinto
- Urologic Oncology Branch (P.A.P.), National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Bradford J Wood
- Center for Interventional Oncology (B.J.W.), National Cancer Institute, National Institutes of Health, Bethesda, Maryland; Department of Radiology (B.J.W.), Clinical Center, National Institutes of Health, Bethesda, Maryland
| | - Deborah E Citrin
- Radiation Oncology Branch (K.P., D.E.C.), National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Baris Turkbey
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland.
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Watkins WT, Qing K, Han C, Hui S, Liu A. Auto-segmentation for total marrow irradiation. Front Oncol 2022; 12:970425. [PMID: 36110933 PMCID: PMC9468379 DOI: 10.3389/fonc.2022.970425] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 07/21/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To evaluate the accuracy and efficiency of Artificial-Intelligence (AI) segmentation in Total Marrow Irradiation (TMI) including contours throughout the head and neck (H&N), thorax, abdomen, and pelvis. Methods An AI segmentation software was clinically introduced for total body contouring in TMI including 27 organs at risk (OARs) and 4 planning target volumes (PTVs). This work compares the clinically utilized contours to the AI-TMI contours for 21 patients. Structure and image dicom data was used to generate comparisons including volumetric, spatial, and dosimetric variations between the AI- and human-edited contour sets. Conventional volume and surface measures including the Sørensen-Dice coefficient (Dice) and the 95th% Hausdorff Distance (HD95) were used, and novel efficiency metrics were introduced. The clinical efficiency gains were estimated by the percentage of the AI-contour-surface within 1mm of the clinical contour surface. An unedited AI-contour has an efficiency gain=100%, an AI-contour with 70% of its surface<1mm from a clinical contour has an efficiency gain of 70%. The dosimetric deviations were estimated from the clinical dose distribution to compute the dose volume histogram (DVH) for all structures. Results A total of 467 contours were compared in the 21 patients. In PTVs, contour surfaces deviated by >1mm in 38.6% ± 23.1% of structures, an average efficiency gain of 61.4%. Deviations >5mm were detected in 12.0% ± 21.3% of the PTV contours. In OARs, deviations >1mm were detected in 24.4% ± 27.1% of the structure surfaces and >5mm in 7.2% ± 18.0%; an average clinical efficiency gain of 75.6%. In H&N OARs, efficiency gains ranged from 42% in optic chiasm to 100% in eyes (unedited in all cases). In thorax, average efficiency gains were >80% in spinal cord, heart, and both lungs. Efficiency gains ranged from 60-70% in spleen, stomach, rectum, and bowel and 75-84% in liver, kidney, and bladder. DVH differences exceeded 0.05 in 109/467 curves at any dose level. The most common 5%-DVH variations were in esophagus (86%), rectum (48%), and PTVs (22%). Conclusions AI auto-segmentation software offers a powerful solution for enhanced efficiency in TMI treatment planning. Whole body segmentation including PTVs and normal organs was successful based on spatial and dosimetric comparison.
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Affiliation(s)
- William Tyler Watkins
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, United States
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Special Issue: “Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging”. Diagnostics (Basel) 2022; 12:diagnostics12061331. [PMID: 35741141 PMCID: PMC9222049 DOI: 10.3390/diagnostics12061331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 05/25/2022] [Indexed: 02/04/2023] Open
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