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Zhao Y, Wang X, Phan J, Chen X, Lee A, Yu C, Huang K, Court LE, Pan T, Wang H, Wahid KA, Mohamed ASR, Naser M, Fuller CD, Yang J. Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers. Med Phys 2024. [PMID: 38896829 DOI: 10.1002/mp.17260] [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: 11/28/2023] [Revised: 05/22/2024] [Accepted: 06/05/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography (PET), are used in the routine clinic to assist radiation oncologists for accurate GTV delineation. However, the availability of PET imaging may not always be guaranteed. PURPOSE To develop a deep learning segmentation framework for automated GTV delineation of HN cancers using a combination of PET/CT images, while addressing the challenge of missing PET data. METHODS Two datasets were included for this study: Dataset I: 524 (training) and 359 (testing) oropharyngeal cancer patients from different institutions with their PET/CT pairs provided by the HECKTOR Challenge; Dataset II: 90 HN patients(testing) from a local institution with their planning CT, PET/CT pairs. To handle potentially missing PET images, a model training strategy named the "Blank Channel" method was implemented. To simulate the absence of a PET image, a blank array with the same dimensions as the CT image was generated to meet the dual-channel input requirement of the deep learning model. During the model training process, the model was randomly presented with either a real PET/CT pair or a blank/CT pair. This allowed the model to learn the relationship between the CT image and the corresponding GTV delineation based on available modalities. As a result, our model had the ability to handle flexible inputs during prediction, making it suitable for cases where PET images are missing. To evaluate the performance of our proposed model, we trained it using training patients from Dataset I and tested it with Dataset II. We compared our model (Model 1) with two other models which were trained for specific modality segmentations: Model 2 trained with only CT images, and Model 3 trained with real PET/CT pairs. The performance of the models was evaluated using quantitative metrics, including Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff Distance (HD95). In addition, we evaluated our Model 1 and Model 3 using the 359 test cases in Dataset I. RESULTS Our proposed model(Model 1) achieved promising results for GTV auto-segmentation using PET/CT images, with the flexibility of missing PET images. Specifically, when assessed with only CT images in Dataset II, Model 1 achieved DSC of 0.56 ± 0.16, MSD of 3.4 ± 2.1 mm, and HD95 of 13.9 ± 7.6 mm. When the PET images were included, the performance of our model was improved to DSC of 0.62 ± 0.14, MSD of 2.8 ± 1.7 mm, and HD95 of 10.5 ± 6.5 mm. These results are comparable to those achieved by Model 2 and Model 3, illustrating Model 1's effectiveness in utilizing flexible input modalities. Further analysis using the test dataset from Dataset I showed that Model 1 achieved an average DSC of 0.77, surpassing the overall average DSC of 0.72 among all participants in the HECKTOR Challenge. CONCLUSIONS We successfully refined a multi-modal segmentation tool for accurate GTV delineation for HN cancer. Our method addressed the issue of missing PET images by allowing flexible data input, thereby providing a practical solution for clinical settings where access to PET imaging may be limited.
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Affiliation(s)
- Yao Zhao
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Xin Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jack Phan
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Xinru Chen
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Anna Lee
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Cenji Yu
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kai Huang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Tinsu Pan
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - He Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kareem Abdul Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Abdalah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mohamed Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Hoebel KV, Bridge CP, Kim A, Gerstner ER, Ly IK, Deng F, DeSalvo MN, Dietrich J, Huang R, Huang SY, Pomerantz SR, Vagvala S, Rosen BR, Kalpathy-Cramer J. Not without Context-A Multiple Methods Study on Evaluation and Correction of Automated Brain Tumor Segmentations by Experts. Acad Radiol 2024; 31:1572-1582. [PMID: 37951777 DOI: 10.1016/j.acra.2023.10.019] [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: 06/09/2023] [Revised: 10/06/2023] [Accepted: 10/07/2023] [Indexed: 11/14/2023]
Abstract
RATIONALE AND OBJECTIVES Brain tumor segmentations are integral to the clinical management of patients with glioblastoma, the deadliest primary brain tumor in adults. The manual delineation of tumors is time-consuming and highly provider-dependent. These two problems must be addressed by introducing automated, deep-learning-based segmentation tools. This study aimed to identify criteria experts use to evaluate the quality of automatically generated segmentations and their thought processes as they correct them. MATERIALS AND METHODS Multiple methods were used to develop a detailed understanding of the complex factors that shape experts' perception of segmentation quality and their thought processes in correcting proposed segmentations. Data from a questionnaire and semistructured interview with neuro-oncologists and neuroradiologists were collected between August and December 2021 and analyzed using a combined deductive and inductive approach. RESULTS Brain tumors are highly complex and ambiguous segmentation targets. Therefore, physicians rely heavily on the given context related to the patient and clinical context in evaluating the quality and need to correct brain tumor segmentation. Most importantly, the intended clinical application determines the segmentation quality criteria and editing decisions. Physicians' personal beliefs and preferences about the capabilities of AI algorithms and whether questionable areas should not be included are additional criteria influencing the perception of segmentation quality and appearance of an edited segmentation. CONCLUSION Our findings on experts' perceptions of segmentation quality will allow the design of improved frameworks for expert-centered evaluation of brain tumor segmentation models. In particular, the knowledge presented here can inspire the development of brain tumor-specific metrics for segmentation model training and evaluation.
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Affiliation(s)
- Katharina V Hoebel
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Christopher P Bridge
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Albert Kim
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Elizabeth R Gerstner
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Ina K Ly
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Francis Deng
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Massachusetts
| | - Matthew N DeSalvo
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jorg Dietrich
- Department of Neurology, Division of Neuro-Oncology, Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, Massachusetts
| | - Raymond Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Susie Y Huang
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - Stuart R Pomerantz
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Saivenkat Vagvala
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Bruce R Rosen
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - Jayashree Kalpathy-Cramer
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Department of Ophthalmology, University of Colorado Anschutz Medical Campus, 1675 Aurora Court, Mail Stop F731, Aurora, CO.
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Panagiotidis E, Papachristou K, Makridou A, Zoglopitou LA, Paschali A, Kalathas T, Chatzimarkou M, Chatzipavlidou V. Review of artificial intelligence clinical applications in Nuclear Medicine. Nucl Med Commun 2024; 45:24-34. [PMID: 37901920 DOI: 10.1097/mnm.0000000000001786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
This paper provides an in-depth analysis of the clinical applications of artificial intelligence (AI) in Nuclear Medicine, focusing on three key areas: neurology, cardiology, and oncology. Beginning with neurology, specifically Alzheimer's disease and Parkinson's disease, the paper examines reviews on diagnosis and treatment planning. The same pattern is followed in cardiology studies. In the final section on oncology, the paper explores the various AI applications in multiple cancer types, including lung, head and neck, lymphoma, and pancreatic cancer.
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Affiliation(s)
| | | | - Anna Makridou
- Medical Physics Department, Cancer Hospital of Thessaloniki 'Theagenio', Thessaloniki, Greece
| | | | - Anna Paschali
- Nuclear Medicine Department, Cancer Hospital of Thessaloniki 'Theagenio' and
| | - Theodoros Kalathas
- Nuclear Medicine Department, Cancer Hospital of Thessaloniki 'Theagenio' and
| | - Michael Chatzimarkou
- Medical Physics Department, Cancer Hospital of Thessaloniki 'Theagenio', Thessaloniki, Greece
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Gay SS, Cardenas CE, Nguyen C, Netherton TJ, Yu C, Zhao Y, Skett S, Patel T, Adjogatse D, Guerrero Urbano T, Naidoo K, Beadle BM, Yang J, Aggarwal A, Court LE. Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy. Sci Rep 2023; 13:21797. [PMID: 38066074 PMCID: PMC10709623 DOI: 10.1038/s41598-023-48944-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
Planning for palliative radiotherapy is performed without the advantage of MR or PET imaging in many clinics. Here, we investigated CT-only GTV delineation for palliative treatment of head and neck cancer. Two multi-institutional datasets of palliative-intent treatment plans were retrospectively acquired: a set of 102 non-contrast-enhanced CTs and a set of 96 contrast-enhanced CTs. The nnU-Net auto-segmentation network was chosen for its strength in medical image segmentation, and five approaches separately trained: (1) heuristic-cropped, non-contrast images with a single GTV channel, (2) cropping around a manually-placed point in the tumor center for non-contrast images with a single GTV channel, (3) contrast-enhanced images with a single GTV channel, (4) contrast-enhanced images with separate primary and nodal GTV channels, and (5) contrast-enhanced images along with synthetic MR images with separate primary and nodal GTV channels. Median Dice similarity coefficient ranged from 0.6 to 0.7, surface Dice from 0.30 to 0.56, and 95th Hausdorff distance from 14.7 to 19.7 mm across the five approaches. Only surface Dice exhibited statistically-significant difference across these five approaches using a two-tailed Wilcoxon Rank-Sum test (p ≤ 0.05). Our CT-only results met or exceeded published values for head and neck GTV autocontouring using multi-modality images. However, significant edits would be necessary before clinical use in palliative radiotherapy.
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Affiliation(s)
- Skylar S Gay
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Callistus Nguyen
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Tucker J Netherton
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Cenji Yu
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Yao Zhao
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | | | | | | | | | | | | | - Jinzhong Yang
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | | | - Laurence E Court
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
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Bollen H, Gulyban A, Nuyts S. Impact of consensus guidelines on delineation of primary tumor clinical target volume (CTVp) for head and neck cancer: Results of a national review project. Radiother Oncol 2023; 189:109915. [PMID: 37739317 DOI: 10.1016/j.radonc.2023.109915] [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: 06/15/2023] [Revised: 08/31/2023] [Accepted: 09/12/2023] [Indexed: 09/24/2023]
Abstract
BACKGROUND AND PURPOSE A significant interobserver variability (IOV) for clinical target volume of the primary tumor (CTVp) delineation was shown in a previous national review project. Since then, international expert consensus guidelines (CG) for the delineation of CTVp were published. The aim of this follow-up study was to 1) objectify the extent of implementation of the CG, 2) assess its impact on delineation quality and consistency, 3) identify any remaining ambiguities. MATERIALS AND METHODS All Belgian RT departments were invited to complete an online survey and submit CTVp for 5 reference cases. Organs at risk and GTV of the primary tumor were predefined. Margins, volumes, IOV between all participating centers (IOVall) and IOV compared to a reference consensus delineation (IOVref) were calculated and compared to the previous analysis. A qualitative analysis was performed assessing the correct interpretation of the CG for each case. RESULTS 17 RT centers completed both survey and delineations, of which 88% had implemented CG. Median DSCref for CTVp_total was 0.80-0.92. IOVall and IOVref improved significantly for the centers following CG (p = 0.005). IOVref for CTVp_high was small with a DSC higher than 0.90 for all cases. A significant volume decrease for the CTVp receiving 70 Gy was observed. Interpretation of CG was more accurate for (supra)glottic carcinoma. 60% of the radiation oncologists thinks clarification of CG is indicated. CONCLUSION Implementation of consensus guidelines for CTVp delineation is already fairly advanced on a national level, resulting in significantly increased delineation uniformity. The accompanying substantial decrease of CTV receiving high dose RT calls for caution and correct interpretation of CG. Clarification of the existing guidelines seems appropriate especially for oropharyngeal and hypopharyngeal carcinoma.
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Affiliation(s)
- Heleen Bollen
- KU Leuven, Dept. Oncology, Laboratory of Experimental Radiotherapy, & UZ Leuven, Radiation Oncology, B-3000, Leuven, Belgium.
| | - Akos Gulyban
- Medical Physics department, Institut Jules Bordet, Brussels, Belgium; Radiophysics and MRI physics laboratory, Faculty of Medicine, Free University of Bruxelles (ULB), Brussels, Belgium
| | - Sandra Nuyts
- KU Leuven, Dept. Oncology, Laboratory of Experimental Radiotherapy, & UZ Leuven, Radiation Oncology, B-3000, Leuven, Belgium
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Li C, Yang Y, Hu F, Xu Y, Wu B, Huang J, Yang K, Lan X. Evaluation of 11 C-Choline PET/CT for T Staging and Tumor Volume Delineation in Nasopharyngeal Cancer Patients in Comparison to 18 F-FDG PET/CT. Clin Nucl Med 2023; 48:563-573. [PMID: 37115936 DOI: 10.1097/rlu.0000000000004645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
PURPOSE Accurate determination of the primary tumor extension of nasopharyngeal carcinoma (NPC) by 18 F-FDG PET/CT is limited by the high physiological 18 F-FDG uptake in the surrounding area, especially in the brain tissue. We aimed to assess whether 11 C-choline PET/CT could improve the accuracy of T staging and tumor volume delineation for NPC patients. METHODS Patients with pathologically confirmed diagnosis of NPC were enrolled. The primary tumor extension of each patient was evaluated by 11 C-choline PET/CT, 18 F-FDG PET/CT, and contrast-enhanced MRI. The PET/CT-based tumor volume ( VPET ) was measured by 3 threshold methods, including the threshold of SUV 2.5 (Th 2.5 ), 40% of maximal SUV (Th 40% ), and the relative background-dependent threshold (Th bgd ). Tumor volume and Dice similarity coefficient were compared among VPET with different segmentation methods and VMR . RESULTS Thirty-three patients with treatment-naive NPC and 6 patients with suspicious recurrent disease were enrolled. The NPC lesions were avid for both 11 C-choline and 18 F-FDG. Visual analysis showed that 11 C-choline PET/CT had better contrast and higher discernability than 18 F-FDG PET/CT for intracranial, skull base, and orbital involvement. 11 C-choline PET/CT also exhibited advantage over MRI for differentiation between local recurrence and radiation-induced alterations. For the tumor delineated, the VMR was larger than VPET in general, except for 18 F-FDG PET/CT with Th 2.5 threshold. For all 3 threshold methods applied, 11 C-choline PET/CT produced more consistent and comparable tumor volume to MRI than 18 F-FDG PET/CT. 11 C-choline PET/CT with Th bgd threshold showed the closest tumor volume and highest similarity to MRI. CONCLUSIONS 11 C-choline PET/CT provides a higher accuracy than 18 F-FDG PET/CT in mapping tumor extension in locally advanced NPC and may be a promising complement to MRI in delineating the primary tumor.
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Affiliation(s)
| | - Yuhui Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
| | | | | | - Bian Wu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
| | - Jing Huang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
| | - Kunyu Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
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Terzidis E, Friborg J, Vogelius IR, Lelkaitis G, von Buchwald C, Olin AB, Johannesen HH, Fischer BM, Wessel I, Rasmussen JH. Tumor volume definitions in head and neck squamous cell carcinoma - Comparing PET/MRI and histopathology. Radiother Oncol 2023; 180:109484. [PMID: 36690303 DOI: 10.1016/j.radonc.2023.109484] [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: 07/01/2022] [Revised: 01/16/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023]
Abstract
BACKGROUND AND PURPOSE In cancer treatment precise definition of the tumor volume is essential, but despite development in imaging modalities, this remains a challenge. Here, pathological tumor volumes from the surgical specimens were obtained and compared to tumor volumes defined from modern PET/MRI hybrid imaging. The purpose is to evaluate mismatch between the volumes defined from imaging and pathology was estimated and potential clinical impact. METHODS AND MATERIALS Twenty-five patients with head and neck squamous cell carcinoma were scanned on an integrated PET/MRI system prior to surgery. Three gross tumor volumes (GTVs) from the primary tumor site were delineated defined from MRI (GTVMRI), PET (GTVPET) and one by utilizing both anatomical images and clinical information (GTVONCO). Twenty-five primary tumor specimens were extracted en bloc, scanned with PET/MRI and co-registered to the patient images. Each specimen was sectioned in blocks, sliced and stained with haematoxylin and eosin. All slices were digitalized and tumor delineated by a head and neck pathologist. The pathological tumor areas in all slices were interpolated yielding a pathological 3D tumor volume (GTVPATO). GTVPATOwas compared with the imaging GTV's and potential mismatch was estimated. RESULTS Thirteen patients were included. The mean volume of GTVONCOwas larger than the GTV's defined from PET or MRI. The mean mismatch of the GTVPATOcompared to the GTVPET, GTVMRIand GTVONCOwas 31.9 %, 54.5 % and 27.9 % respectively, and the entire GTVPATO was only fully encompassed in GTVONCO in 1 of 13 patients. However, after the addition of a clinical 5 mm margin the GTVPATO was fully encompassed in GTVONCO in 11 out of 13 patients. CONCLUSIONS Despite modern hybrid imaging modalities, a mismatch between imaging and pathological defined tumor volumes was observed in all patients.A 5 mm clinical margin was sufficient to ensure inclusion of the entire pathological volume in 11 out of 13 patients.
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Affiliation(s)
- Emmanouil Terzidis
- Department of Oncology, Section of Radiotherapy, Rigshospitalet, Copenhagen, Denmark; Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Jeppe Friborg
- Department of Oncology, Section of Radiotherapy, Rigshospitalet, Copenhagen, Denmark
| | - Ivan R Vogelius
- Department of Oncology, Section of Radiotherapy, Rigshospitalet, Copenhagen, Denmark
| | | | - Christian von Buchwald
- Department of Otorhinolaryngology, Head & Neck Surgery and Audiology, Rigshospitalet, Copenhagen, Denmark
| | - Anders B Olin
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Denmark
| | - Helle H Johannesen
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Denmark
| | - Barbara M Fischer
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Irene Wessel
- Department of Otorhinolaryngology, Head & Neck Surgery and Audiology, Rigshospitalet, Copenhagen, Denmark
| | - Jacob H Rasmussen
- Department of Otorhinolaryngology, Head & Neck Surgery and Audiology, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark.
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Bollen H, Willems S, Wegge M, Maes F, Nuyts S. Benefits of automated gross tumor volume segmentation in head and neck cancer using multi-modality information. Radiother Oncol 2023; 182:109574. [PMID: 36822358 DOI: 10.1016/j.radonc.2023.109574] [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: 10/17/2022] [Revised: 02/08/2023] [Accepted: 02/12/2023] [Indexed: 02/23/2023]
Abstract
PURPOSE Gross tumor volume (GTV) delineation for head and neck cancer (HNC) radiation therapy planning is time consuming and prone to interobserver variability (IOV). The aim of this study was (1) to develop an automated GTV delineation approach of primary tumor (GTVp) and pathologic lymph nodes (GTVn) based on a 3D convolutional neural network (CNN) exploiting multi-modality imaging input as required in clinical practice, and (2) to validate its accuracy, efficiency and IOV compared to manual delineation in a clinical setting. METHODS Two datasets were retrospectively collected from 150 clinical cases. CNNs were trained for GTV delineation with consensus delineation as ground truth, with either single (CT) or co-registered multi-modal (CT + PET or CT + MRI) imaging data as input. For validation, GTVs were delineated on 20 new cases by two observers, once manually, once by correcting the delineations generated by the CNN. RESULTS Both multi-modality CNNs performed better than the single-modality CNN and were selected for clinical validation. Mean Dice Similarity Coefficient (DSC) for (GTVp, GTVn) respectively between automated and manual delineations was (69%, 79%) for CT + PET and (59%,71%) for CT + MRI. Mean DSC between automated and corrected delineations was (81%,89%) for CT + PET and (69%,77%) for CT + MRI. Mean DSC between observers was (76%,86%) for manual delineations and (95%,96%) for corrected delineations, indicating a significant decrease in IOV (p < 10-5), while efficiency increased significantly (48%, p < 10-5). CONCLUSION Multi-modality automated delineation of GTV of HNC was shown to be more efficient and consistent compared to manual delineation in a clinical setting and beneficial over a single-modality approach.
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Affiliation(s)
- Heleen Bollen
- KU Leuven, Dept. Oncology, Laboratory of Experimental Radiotherapy, & UZ Leuven, Radiation Oncology, B-3000 Leuven, Belgium.
| | - Siri Willems
- KU Leuven, Dept. ESAT, Processing Speech and Images (PSI), & UZ Leuven, Medical Imaging Research Center, B-3000 Leuven, Belgium
| | - Marilyn Wegge
- KU Leuven, Dept. Oncology, Laboratory of Experimental Radiotherapy, & UZ Leuven, Radiation Oncology, B-3000 Leuven, Belgium
| | - Frederik Maes
- KU Leuven, Dept. ESAT, Processing Speech and Images (PSI), & UZ Leuven, Medical Imaging Research Center, B-3000 Leuven, Belgium
| | - Sandra Nuyts
- KU Leuven, Dept. Oncology, Laboratory of Experimental Radiotherapy, & UZ Leuven, Radiation Oncology, B-3000 Leuven, Belgium
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Therapeutic optimization in head and neck radiotherapy planning: Advocacy for 18FDG PET-CT in treatment condition. Bull Cancer 2022; 109:1262-1268. [DOI: 10.1016/j.bulcan.2022.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 07/20/2022] [Accepted: 07/24/2022] [Indexed: 11/27/2022]
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Cardenas CE, Blinde SE, Mohamed ASR, Ng SP, Raaijmakers C, Philippens M, Kotte A, Al-Mamgani AA, Karam I, Thomson DJ, Robbins J, Newbold K, Fuller CD, Terhaard C, On Behalf Of The, Bahig H, Blanchard P, Dehnad H, Doornaert P, Elhalawani H, Frank SJ, Garden A, Gunn GB, Hamming-Vrieze O, Kamal M, Kasperts N, Lee LW, McDonald BA, McPartlin A, Meheissen MA, Morrison WH, Navran A, Nutting CM, Pameijer F, Phan J, Poon I, Rosenthal DI, Smid EJ, Sykes AJ. Comprehensive Quantitative Evaluation of Variability in MR-guided Delineation of Oropharyngeal Gross Tumor Volumes and High-risk Clinical Target Volumes: An R-IDEAL Stage 0 Prospective Study. Int J Radiat Oncol Biol Phys 2022; 113:426-436. [PMID: 35124134 DOI: 10.1016/j.ijrobp.2022.01.050] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 01/12/2022] [Accepted: 01/26/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE Tumor and target volume manual delineation remains a challenging task in head-and-neck cancer radiotherapy. The purpose of this study was to conduct a multi-institutional evaluation of manual delineations of gross tumor volume (GTV), high-risk clinical target volume (CTV), parotids, and submandibular glands on treatment simulation MR scans of oropharyngeal cancer (OPC) patients. METHODS Pre-treatment T1-weighted (T1w), T1-weighted with gadolinium contrast (T1w+C) and T2-weighted (T2w) MRI scans were retrospectively collected for 4 OPC patients under an IRB-approved protocol. The scans were provided to twenty-six radiation oncologists from seven international cancer centers who participated in this delineation study. In addition, patients' clinical history and physical examination findings, along with a medical photographic image and radiological results, were provided. The contours were compared using overlap/distance metrics using both STAPLE and pair-wise comparisons. Lastly, participants completed a brief questionnaire to assess participants' experience and CTV delineation institutional practices. RESULTS Large variability was measured between observers' delineations for GTVs and CTVs. The mean Dice Similarity Coefficient values across all physicians' delineations for GTVp, GTVn, CTVp, and CTVn were 0.77, 0.67, 0.77, and 0.69, respectively, for STAPLE comparison and 0.67, 0.60, 0.67, and 0.58, respectively, for pair-wise analysis. Normal tissue contours were defined more consistently when considering overlap/distance metrics. The median radiation oncology clinical experience was 7 years. The median experience delineating on MRI was 3.5 years. The GTV-to-CTV margin used was 10 mm for six of seven participant institutions. One institution used 8 mm and three participants (from three different institutions) used a margin of 5 mm. CONCLUSION The data from this study suggests that appropriate guidelines, contouring quality assurance sessions, and training are still needed for the adoption of MR-based treatment planning for head-and-neck cancers. Such efforts should play a critical role in reducing delineation variation and ensure standardization of target design across clinical practices.
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Affiliation(s)
- Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Sanne E Blinde
- Department of Radiation Oncology, Klinikum Kassel, Kassel, Germany
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Sweet Ping Ng
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA; Department of Radiation Oncology, Olivia Newton-John Cancer Centre, Austin Health, Melbourne, Australia
| | - Cornelis Raaijmakers
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marielle Philippens
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alexis Kotte
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Abrahim A Al-Mamgani
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Irene Karam
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Science Centre, University of Toronto, Toronto, ON, Canada
| | - David J Thomson
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - Jared Robbins
- Department of Radiation Oncology, University of Arizona, Tucson, Arizona, USA
| | - Kate Newbold
- Royal Marsden NHS Foundation Trust and Institute of Cancer Research, London, UK
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
| | - Chris Terhaard
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - On Behalf Of The
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Houda Bahig
- Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal, Montreal, Quebec, Canada
| | - Pierre Blanchard
- Department of Radiation Oncology, Institut Gustave Roussy, Villejuif, France
| | - Homan Dehnad
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Patricia Doornaert
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hesham Elhalawani
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Steven J Frank
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Adam Garden
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - G Brandon Gunn
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Olga Hamming-Vrieze
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Mona Kamal
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Nicolien Kasperts
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lip Wai Lee
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - Brigid A McDonald
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Andrew McPartlin
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - Mohamed Am Meheissen
- Alexandria Clinical Oncology Department, Alexandria University, Alexandria, Egypt
| | - William H Morrison
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Arash Navran
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Frank Pameijer
- Department of Radiology, Division of Imaging & Oncology, University Medical Center, Utrecht, The Netherlands
| | - Jack Phan
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ian Poon
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Science Centre, University of Toronto, Toronto, ON, Canada
| | - David I Rosenthal
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ernst J Smid
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Andrew J Sykes
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
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Dejanovic D, Specht L, Munk OL, Christensen CB, Berthelsen AK, Law I, Loft A. PET for radiotherapy planning. Nucl Med Mol Imaging 2022. [DOI: 10.1016/b978-0-12-822960-6.00128-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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12
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Sadeghi S, Siavashpour Z, Vafaei Sadr A, Farzin M, Sharp R, Gholami S. A rapid review of influential factors and appraised solutions on organ delineation uncertainties reduction in radiotherapy. Biomed Phys Eng Express 2021; 7. [PMID: 34265746 DOI: 10.1088/2057-1976/ac14d0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 07/15/2021] [Indexed: 11/11/2022]
Abstract
Background and purpose.Accurate volume delineation plays an essential role in radiotherapy. Contouring is a potential source of uncertainties in radiotherapy treatment planning that could affect treatment outcomes. Therefore, reducing the degree of contouring uncertainties is crucial. The role of utilized imaging modality in the organ delineation uncertainties has been investigated. This systematic review explores the influential factors on inter-and intra-observer uncertainties of target volume and organs at risk (OARs) delineation focusing on the used imaging modality for these uncertainties reduction and the reported subsequent histopathology and follow-up assessment.Methods and materials.An inclusive search strategy has been conducted to query the available online databases (Scopus, Google Scholar, PubMed, and Medline). 'Organ at risk', 'target', 'delineation', 'uncertainties', 'radiotherapy' and their relevant terms were utilized using every database searching syntax. Final article extraction was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Included studies were limited to the ones published in English between 1995 and 2020 and that just deal with computed tomography (CT) and magnetic resonance imaging (MRI) modalities.Results.A total of 923 studies were screened and 78 were included of which 31 related to the prostate 20 to the breast, 18 to the head and neck, and 9 to the brain tumor site. 98% of the extracted studies performed volumetric analysis. Only 24% of the publications reported the dose deviations resulted from variation in volume delineation Also, heterogeneity in studied populations and reported geometric and volumetric parameters were identified such that quantitative synthesis was not appropriate.Conclusion.This review highlightes the inter- and intra-observer variations that could lead to contouring uncertainties and impede tumor control in radiotherapy. For improving volume delineation and reducing inter-observer variability, the implementation of well structured training programs, homogeneity in following consensus and guidelines, reliable ground truth selection, and proper imaging modality utilization could be clinically beneficial.
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Affiliation(s)
- Sogand Sadeghi
- Department of Nuclear Physics, Faculty of Sciences, University of Mazandaran, Babolsar, Iran
| | - Zahra Siavashpour
- Department of Radiation Oncology, Shohada-e Tajrish Educational Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Vafaei Sadr
- Département de Physique Théorique and Center for Astroparticle Physics, Université de Genève, Geneva, Switzerland
| | - Mostafa Farzin
- Radiation Oncology Research Center (RORC), Tehran University of Medical Science, Tehran, Iran.,Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Ryan Sharp
- Department of Health Physics and Diagnostic Sciences, University of Nevada, Las Vegas, NV, United States of America
| | - Somayeh Gholami
- Radiotherapy Oncology Department, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
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van der Veen J, Gulyban A, Willems S, Maes F, Nuyts S. Interobserver variability in organ at risk delineation in head and neck cancer. Radiat Oncol 2021; 16:120. [PMID: 34183040 PMCID: PMC8240214 DOI: 10.1186/s13014-020-01677-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 09/24/2020] [Indexed: 11/25/2022] Open
Abstract
Background In radiotherapy inaccuracy in organ at risk (OAR) delineation can impact treatment plan optimisation and treatment plan evaluation. Brouwer et al. showed significant interobserver variability (IOV) in OAR delineation in head and neck cancer (HNC) and published international consensus guidelines (ICG) for OAR delineation in 2015. The aim of our study was to evaluate IOV in the presence of these guidelines. Methods HNC radiation oncologists (RO) from each Belgian radiotherapy centre were invited to complete a survey and submit contours for 5 HNC cases. Reference contours (OARref) were obtained by a clinically validated artificial intelligence-tool trained using ICG. Dice similarity coefficients (DSC), mean surface distance (MSD) and 95% Hausdorff distances (HD95) were used for comparison. Results Fourteen of twenty-two RO (64%) completed the survey and submitted delineations. Thirteen (93%) confirmed the use of delineation guidelines, of which six (43%) used the ICG. The OARs whose delineations agreed best with the OARref were mandible [median DSC 0.9, range (0.8–0.9); median MSD 1.1 mm, range (0.8–8.3), median HD95 3.4 mm, range (1.5–38.7)], brainstem [median DSC 0.9 (0.6–0.9); median MSD 1.5 mm (1.1–4.0), median HD95 4.0 mm (2.3–15.0)], submandibular glands [median DSC 0.8 (0.5–0.9); median MSD 1.2 mm (0.9–2.5), median HD95 3.1 mm (1.8–12.2)] and parotids [median DSC 0.9 (0.6–0.9); median MSD 1.9 mm (1.2–4.2), median HD95 5.1 mm (3.1–19.2)]. Oral cavity, cochleas, PCMs, supraglottic larynx and glottic area showed more variation. RO who used the consensus guidelines showed significantly less IOV (p = 0.008). Conclusions Although ICG for delineation of OARs in HNC exist, they are only implemented by about half of RO participating in this study, which partly explains the delineation variability. However, this study highlights that guidelines alone do not suffice to eliminate IOV and that more effort needs to be done to accomplish further treatment standardisation, for example with artificial intelligence.
Supplementary information Supplementary information accompanies this paper at 10.1186/s13014-020-01677-2.
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Affiliation(s)
- J van der Veen
- Department of Oncology, Radiation-Oncology, University of Leuven, University Hospitals Leuven, 3000, Leuven, KU, Belgium
| | - A Gulyban
- Department of Medical Physics, Jules Bordet Institute, Brussels, Belgium.
| | - S Willems
- Department ESAT, Processing Speech and Images (PSI), Medical Imaging Research Center, KU Leuven, University Hospitals Leuven, 3000, Leuven, Belgium
| | - F Maes
- Department ESAT, Processing Speech and Images (PSI), Medical Imaging Research Center, KU Leuven, University Hospitals Leuven, 3000, Leuven, Belgium
| | - S Nuyts
- Department of Oncology, Radiation-Oncology, University of Leuven, University Hospitals Leuven, 3000, Leuven, KU, Belgium.
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Abstract
Head–Neck Cancer (HNC) has a relevant impact on the oncology patient population and for this reason, the present review is dedicated to this type of neoplastic disease. In particular, a collection of methods aimed at tumor delineation is presented, because this is a fundamental task to perform efficient radiotherapy. Such a segmentation task is often performed on uni-modal data (usually Positron Emission Tomography (PET)) even though multi-modal images are preferred (PET-Computerized Tomography (CT)/PET-Magnetic Resonance (MR)). Datasets can be private or freely provided by online repositories on the web. The adopted techniques can belong to the well-known image processing/computer-vision algorithms or the newest deep learning/artificial intelligence approaches. All these aspects are analyzed in the present review and comparison among various approaches is performed. From the present review, the authors draw the conclusion that despite the encouraging results of computerized approaches, their performance is far from handmade tumor delineation result.
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Forde E, Leech M, Robert C, Herron E, Marignol L. Influence of inter-observer delineation variability on radiomic features of the parotid gland. Phys Med 2021; 82:240-248. [PMID: 33677385 DOI: 10.1016/j.ejmp.2021.01.084] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 01/06/2021] [Accepted: 01/29/2021] [Indexed: 12/14/2022] Open
Abstract
PURPOSE This study aimed to quantify the variability in the values of radiomic features extracted from a right parotid gland (RPG) delineated by a series of independent observers. METHODS This was a secondary analysis of anonymous data from a delineation workshop. Inter-observer variability of the RPG from 40 participants was quantified using DICE similarity coefficient (DSC) and Hausdorff distance (HD). An additional contour was generated using Varian SmartSegmentation. Radiomic features extracted include four shape features, six histogram features, and 32 texture features. The absolute mean paired percentage difference (PPD) in feature values from the expert and participants were ranked . Feature robustness was classified using pre- determined thresholds. RESULTS 63% of participants achieved a DSC > 0.7, the auto- segmentation DSC was 0.76. The average HD for the participants was 16.16 mm ± 0.66 mm, and 15.16 mm for the auto-segmentation. 48% (n = 20) and 33% (n = 14) of features were deemed to be robust with a mean absolute PPD < 5%, for the auto-segmentation and manual delineations respectively; the majority of which were from the grey-run length matrix family. 7% (n = 3) of features from the auto- segmentation and 10% (n = 4) from the manual contours were deemed to be unstable with a mean absolute PPD > 50%. The value of the most robust feature was not related to DSC and HD. CONCLUSION Inter-observer delineation variability affects the value of the radiomic features extracted from the RPG. This study identifies the radiomic features least sensitive to these uncertainties. Further investigation of the clinical relevance of these features in prediction of xerostomia is warranted.
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Affiliation(s)
- E Forde
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity St James' Cancer Institute, Trinity College Dublin, Dublin, Ireland.
| | - M Leech
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity St James' Cancer Institute, Trinity College Dublin, Dublin, Ireland
| | - C Robert
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Salcay, Villejuif, France
| | - E Herron
- Department of Psychiatry School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - L Marignol
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity St James' Cancer Institute, Trinity College Dublin, Dublin, Ireland
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Groendahl AR, Skjei Knudtsen I, Huynh BN, Mulstad M, Moe YM, Knuth F, Tomic O, Indahl UG, Torheim T, Dale E, Malinen E, Futsaether CM. A comparison of methods for fully automatic segmentation of tumors and involved nodes in PET/CT of head and neck cancers. Phys Med Biol 2021; 66:065012. [PMID: 33666176 DOI: 10.1088/1361-6560/abe553] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Target volume delineation is a vital but time-consuming and challenging part of radiotherapy, where the goal is to deliver sufficient dose to the target while reducing risks of side effects. For head and neck cancer (HNC) this is complicated by the complex anatomy of the head and neck region and the proximity of target volumes to organs at risk. The purpose of this study was to compare and evaluate conventional PET thresholding methods, six classical machine learning algorithms and a 2D U-Net convolutional neural network (CNN) for automatic gross tumor volume (GTV) segmentation of HNC in PET/CT images. For the latter two approaches the impact of single versus multimodality input on segmentation quality was also assessed. 197 patients were included in the study. The cohort was split into training and test sets (157 and 40 patients, respectively). Five-fold cross-validation was used on the training set for model comparison and selection. Manual GTV delineations represented the ground truth. Tresholding, classical machine learning and CNN segmentation models were ranked separately according to the cross-validation Sørensen-Dice similarity coefficient (Dice). PET thresholding gave a maximum mean Dice of 0.62, whereas classical machine learning resulted in maximum mean Dice scores of 0.24 (CT) and 0.66 (PET; PET/CT). CNN models obtained maximum mean Dice scores of 0.66 (CT), 0.68 (PET) and 0.74 (PET/CT). The difference in cross-validation Dice between multimodality PET/CT and single modality CNN models was significant (p ≤ 0.0001). The top-ranked PET/CT-based CNN model outperformed the best-performing thresholding and classical machine learning models, giving significantly better segmentations in terms of cross-validation and test set Dice, true positive rate, positive predictive value and surface distance-based metrics (p ≤ 0.0001). Thus, deep learning based on multimodality PET/CT input resulted in superior target coverage and less inclusion of surrounding normal tissue.
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Flaus A, Nevesny S, Guy JB, Sotton S, Magné N, Prévot N. Positron emission tomography for radiotherapy planning in head and neck cancer: What impact? Nucl Med Commun 2021; 42:234-243. [PMID: 33252513 DOI: 10.1097/mnm.0000000000001329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PET-computed tomography (CT) plays a growing role to guide target volume delineation for head and neck cancer in radiation oncology. Pretherapeutic [18F]FDG PET-CT adds information to morphological imaging. First, as a whole-body imaging modality, it reveals regional or distant metastases that induce major therapeutic changes in more than 10% of the cases. Moreover, it allows better pathological lymph node selection which improves overall regional control and overall survival. Second, locally, it allows us to define the metabolic tumoral volume, which is a reliable prognostic feature for survival outcome. [18F]FDG PET-CT-based gross tumor volume (GTV) is on average significantly smaller than GTV based on CT. Nevertheless, the overlap is incomplete and more evaluation of composite GTV based on PET and GTV based on CT are needed. However, in clinical practice, the study showed that using GTV PET alone for treatment planning was similar to using GTVCT for local control and dose distribution was better as a dose to organs at risk significantly decreased. In addition to FDG, pretherapeutic PET could give access to different biological tumoral volumes - thanks to different tracers - guiding heterogeneous dose delivery (dose painting concept) to resistant subvolumes. During radiotherapy treatment, follow-up [18F]FDG PET-CT revealed an earlier and more important diminution of GTV than other imaging modality. It may be a valuable support for adaptative radiotherapy as a new treatment plan with a significant impact on dose distribution became possible. Finally, additional studies are required to prospectively validate long-term outcomes and lower toxicity resulting from the use of PET-CT in treatment planning.
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Affiliation(s)
- Anthime Flaus
- Service de Médecine Nucléaire, Centre Hospitalier Universitaire de Saint-Etienne, St Etienne
| | - Stéphane Nevesny
- Département de Radiothérapie, Institut de Cancérologie de la Loire-Lucien Neuwirth, St Priest en Jarez
| | - Jean-Baptiste Guy
- Département de Radiothérapie, Institut de Cancérologie de la Loire-Lucien Neuwirth, St Priest en Jarez
- UMR CNRS 5822/IN2P3, IPNL, PRISME, Laboratoire de Radiobiologie Cellulaire et Moléculaire, Faculté de Médecine Lyon-Sud, Université Lyon 1, Oullins Cedex
| | - Sandrine Sotton
- Department of Research and Teaching, Lucien Neuwirth Cancer Institute, Saint-Priest-en-Jarez, University Departement of Research and Teaching
| | - Nicolas Magné
- Département de Radiothérapie, Institut de Cancérologie de la Loire-Lucien Neuwirth, St Priest en Jarez
- UMR CNRS 5822/IN2P3, IPNL, PRISME, Laboratoire de Radiobiologie Cellulaire et Moléculaire, Faculté de Médecine Lyon-Sud, Université Lyon 1, Oullins Cedex
| | - Nathalie Prévot
- Service de Médecine Nucléaire, Centre Hospitalier Universitaire de Saint-Etienne, St Etienne
- INSERM U 1059 Sainbiose, Université Jean Monnet, Saint-Etienne, France
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Naser MA, van Dijk LV, He R, Wahid KA, Fuller CD. Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images. HEAD AND NECK TUMOR SEGMENTATION : FIRST CHALLENGE, HECKTOR 2020, HELD IN CONJUNCTION WITH MICCAI 2020, LIMA, PERU, OCTOBER 4, 2020, PROCEEDINGS 2021; 12603:85-98. [PMID: 33724743 PMCID: PMC7929493 DOI: 10.1007/978-3-030-67194-5_10] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Segmentation of head and neck cancer (HNC) primary tumors onmedical images is an essential, yet labor-intensive, aspect of radiotherapy.PET/CT imaging offers a unique ability to capture metabolic and anatomicinformation, which is invaluable for tumor detection and border definition. Anautomatic segmentation tool that could leverage the dual streams of informationfrom PET and CT imaging simultaneously, could substantially propel HNCradiotherapy workflows forward. Herein, we leverage a multi-institutionalPET/CT dataset of 201 HNC patients, as part of the MICCAI segmentationchallenge, to develop novel deep learning architectures for primary tumor auto-segmentation for HNC patients. We preprocess PET/CT images by normalizingintensities and applying data augmentation to mitigate overfitting. Both 2D and3D convolutional neural networks based on the U-net architecture, which wereoptimized with a model loss function based on a combination of dice similaritycoefficient (DSC) and binary cross entropy, were implemented. The median andmean DSC values comparing the predicted tumor segmentation with the groundtruth achieved by the models through 5-fold cross validation are 0.79 and 0.69for the 3D model, respectively, and 0.79 and 0.67 for the 2D model, respec-tively. These promising results show potential to provide an automatic, accurate,and efficient approach for primary tumor auto-segmentation to improve theclinical practice of HNC treatment.
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Affiliation(s)
- Mohamed A Naser
- Department of Radiation Oncology, The University of Texas MD AndersonCancer, Houston, TX 77030, USA
| | - Lisanne V van Dijk
- Department of Radiation Oncology, The University of Texas MD AndersonCancer, Houston, TX 77030, USA
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD AndersonCancer, Houston, TX 77030, USA
| | - Kareem A Wahid
- Department of Radiation Oncology, The University of Texas MD AndersonCancer, Houston, TX 77030, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD AndersonCancer, Houston, TX 77030, USA
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Li B, Chang F, Xiao Y, Wei X, He W, Ming Y. Thermal Ablation Damage Analysis of CFRP Suffering from Lightning Based on Principles of Tomography. MATERIALS (BASEL, SWITZERLAND) 2020; 13:ma13225159. [PMID: 33207815 PMCID: PMC7697403 DOI: 10.3390/ma13225159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 11/04/2020] [Accepted: 11/05/2020] [Indexed: 06/11/2023]
Abstract
Coupled electrical-thermal finite element analysis (FEA) models are widely adopted to analyze the thermal ablation damage of carbon fiber reinforced polymer (CFRP) caused by lightning, but it is still difficult to analyze the ablation due to its complex space geometry. According to the principle of computerized tomography (CT), tomographic images of FEA models' temperature fields with different thicknesses were obtained to calculate the mass loss and compare the damage morphology. The four areas including Area 0, Area I, Area II, and Area III; were separated from the temperature fields in terms of different vaporization and pyrolysis temperature ranges of carbon fiber (CF) and resin matrix. Ablation mass losses were calculated by pixel statistics and tomographic intervals, which were consistent with the experimental results. The maximum ablation area of unprotected CFRP was found on the tomography images of 50 μm rather than the surface by comparing tomographic images with different thickness due to the influence of the thermal radiation, but this effect was not found in CFRP protected by copper mesh. Some other phenomena, including continuous evolutions of ablation areas and the influence of the intersection angle on the direction of the ablation extension, were also discovered.
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Affiliation(s)
- Bin Li
- Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, China; (B.L.); (F.C.); (W.H.)
| | - Fei Chang
- Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, China; (B.L.); (F.C.); (W.H.)
| | - Yao Xiao
- Aviation Maintenance NCO Academy, Air Force Engineering University, Xinyang, 464031, China
| | - Xiaolong Wei
- Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, China; (B.L.); (F.C.); (W.H.)
| | - Weifeng He
- Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, China; (B.L.); (F.C.); (W.H.)
| | - Yueke Ming
- State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi ’an 710054, China;
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Jensen K, Al-Farra G, Dejanovic D, Eriksen JG, Loft A, Hansen CR, Pameijer FA, Zukauskaite R, Grau C. Imaging for Target Delineation in Head and Neck Cancer Radiotherapy. Semin Nucl Med 2020; 51:59-67. [PMID: 33246540 DOI: 10.1053/j.semnuclmed.2020.07.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
The definition of tumor involved volumes in patients with head and neck cancer poses great challenges with the increasing use of highly conformal radiotherapy techniques eg, volumetric modulated arc therapy and intensity modulated proton therapy. The risk of underdosing the tumor might increase unless great care is taken in the process. The information gained from imaging is increasing with both PET and MRI becoming readily available for the definition of targets. The information gained from these techniques is indeed multidimensional as one often acquire data on eg, metabolism, diffusion, and hypoxia together with anatomical and structural information. Nevertheless, much work remains to fully exploit the available information on a patient-specific level. Multimodality target definition in radiotherapy is a chain of processes that must be individually scrutinized, optimized and quality assured. Any uncertainties or errors in image acquisition, reconstruction, interpretation, and delineation are systematic errors and hence will potentially have a detrimental effect on the entire radiotherapy treatment and hence; the chance of cure or the risk of unnecessary side effects. Common guidelines and procedures create a common minimum standard and ground for evaluation and development. In Denmark, the treatment of head and neck cancer is organized within the multidisciplinary Danish Head and Neck Cancer Group (DAHANCA). The radiotherapy quality assurance group of DAHANCA organized a workshop in January 2020 with participants from oncology, radiology, and nuclear medicine from all centers in Denmark, treating patients with head and neck cancer. The participants agreed on a national guideline on imaging for target delineation in head and neck cancer radiotherapy, which has been approved by the DAHANCA group. The guidelines are available in the Supplementary. The use of multimodality imaging is being recommended for the planning of all radical treatments with a macroscopic tumor. 2-[18F]FDG-PET/CT should be available, preferable in the treatment position. The recommended MRI sequences are T1, T2 with and without fat suppression, and T1 with contrast enhancement, preferable in the treatment position. The interpretation of clinical information, including thorough physical examination as well as imaging, should be done in a multidisciplinary setting with an oncologist, radiologist, and nuclear medicine specialist.
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Affiliation(s)
- Kenneth Jensen
- Danish Center for Particle Therapy. Aarhus University Hospital, Denmark.
| | - Gina Al-Farra
- Department of Radiology, Herlev and Gentofte Hospital, Denmark
| | - Danijela Dejanovic
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark
| | | | - Annika Loft
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark
| | - Christian R Hansen
- Laboratory of Radiation Physics, Odense University Hospital, Denmark; Institute of Clinical Research, University of Southern Denmark, Odense, Denmark; Danish Center for Particle Therapy. Aarhus University Hospital, Denmark
| | - Frank A Pameijer
- Department of Radiology, University Medical Center Utrecht, the Netherlands
| | - Ruta Zukauskaite
- Department of Oncology, Odense University Hospital, Denmark; Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Cai Grau
- Danish Center for Particle Therapy. Aarhus University Hospital, Denmark
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21
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Pavic M, Bogowicz M, Kraft J, Vuong D, Mayinger M, Kroeze SGC, Friess M, Frauenfelder T, Andratschke N, Huellner M, Weder W, Guckenberger M, Tanadini-Lang S, Opitz I. FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients. EJNMMI Res 2020; 10:81. [PMID: 32661672 PMCID: PMC7359199 DOI: 10.1186/s13550-020-00669-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 07/02/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Careful selection of malignant pleural mesothelioma (MPM) patients for curative treatment is of highest importance, as the multimodal treatment regimen is challenging for patients and harbors a high risk of substantial toxicity. Radiomics-a quantitative method for image analysis-has shown its prognostic ability in different tumor entities and could therefore play an important role in optimizing patient selection for radical cancer treatment. So far, radiomics as a prognostic tool in MPM was not investigated. MATERIALS AND METHODS This study is based on 72 MPM patients treated with surgery in a curative intent at our institution between 2009 and 2017. Pre-treatment Fluorine-18 fluorodeoxyglucose (FDG) PET and CT scans were used for radiomics outcome modeling. After extraction of 1404 CT and 1410 FDG PET features from each image, a preselection by principal component analysis was performed to include only robust, non-redundant features for the cox regression to predict the progression-free survival (PFS) and the overall survival (OS). Results were validated on a separate cohort. Additionally, SUVmax and SUVmean, and volume were tested for their prognostic ability for PFS and OS. RESULTS For the PFS a concordance index (c-index) of 0.67 (95% CI 0.52-0.82) and 0.66 (95% CI 0.57-0.78) for the training cohort (n = 36) and internal validation cohort (n = 36), respectively, were obtained for the PET radiomics model. The PFS advantage of the low-risk group translated also into an OS advantage. On CT images, no radiomics model could be trained. SUV max and SUV mean were also not prognostic in terms of PFS and OS. CONCLUSION We were able to build a successful FDG PET radiomics model for the prediction of PFS in MPM. Radiomics could serve as a tool to aid clinical decision support systems for treatment of MPM in future.
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Affiliation(s)
- M Pavic
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
| | - M Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - J Kraft
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - D Vuong
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - M Mayinger
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - S G C Kroeze
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - M Friess
- Department of Thoracic Surgery, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - T Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - N Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - M Huellner
- Department of Nuclear Medicine, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - W Weder
- Department of Thoracic Surgery, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - M Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - S Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - I Opitz
- Department of Thoracic Surgery, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
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22
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Shukla M, Forghani R, Agarwal M. Patient-Centric Head and Neck Cancer Radiation Therapy: Role of Advanced Imaging. Neuroimaging Clin N Am 2020; 30:341-357. [PMID: 32600635 DOI: 10.1016/j.nic.2020.04.005] [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] [Indexed: 12/24/2022]
Abstract
The traditional 'one-size-fits-all' approach to H&N cancer therapy is archaic. Advanced imaging can identify radioresistant areas by using biomarkers that detect tumor hypoxia, hypercellularity etc. Highly conformal radiotherapy can target resistant areas with precision. The critical information that can be gleaned about tumor biology from these advanced imaging modalities facilitates individualized radiotherapy. The tumor imaging world is pushing its boundaries. Molecular imaging can now detect protein expression and genotypic variations across tumors that can be exploited for tailoring treatment. The exploding field of radiomics and radiogenomics extracts quantitative, biologic and genetic information and further expands the scope of personalized therapy.
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Affiliation(s)
- Monica Shukla
- Department of Radiation Oncology, Froedtert and Medical College of Wisconsin, 9200 W. Wisconsin Avenue, Milwaukee, WI 53226, USA
| | - Reza Forghani
- Augmented Intelligence & Precision Health Laboratory, Department of Radiology, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada
| | - Mohit Agarwal
- Department of Radiology, Section of Neuroradiology, Froedtert and Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA.
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23
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Pike LC, Thomas CM, Guerrero-Urbano T, Michaelidou A, Greener T, Miles E, Eaton D, Barrington SF. Guidance on the use of PET for treatment planning in radiotherapy clinical trials. Br J Radiol 2019; 92:20190180. [PMID: 31437023 PMCID: PMC6849663 DOI: 10.1259/bjr.20190180] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 07/16/2019] [Accepted: 08/19/2019] [Indexed: 12/22/2022] Open
Abstract
The aim of this article is to propose meaningful guidance covering the practical and technical issues involved when planning or conducting clinical trials involving positron emission tomography (PET)-guided radiotherapy. The complexity of imaging requirements will depend on the study aims, design and PET methods used. Where PET is used to adapt radiotherapy, a high level of accuracy and reproducibility is required to ensure effective and safe treatment delivery. The guidance in this document is intended to assist researchers designing clinical trials involving PET-guided radiotherapy to provide sufficient information about the appropriate methods to complete PET-CT imaging to a consistent standard at participating centres. The guidance is divided into six categories: application of PET in radiotherapy, resource requirements, quality assurance, imaging protocol design, data management and image processing. Each section provides an overview of the recent literature to support the specific recommendations. This guidance builds on previous recommendations from the National Cancer Research Institute PET Research Network and has been produced in collaboration with the National Radiotherapy Trials Quality Assurance Group.
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Affiliation(s)
- Lucy C Pike
- King’s College London and Guy’s and St Thomas’ PET Centre, School of Biomedical Engineering and Imaging Sciences, King’s College London, King’s Health Partners, London, UK
| | | | | | | | - Tony Greener
- Radiotherapy Physics, Guy's & St Thomas’ NHS Foundation Trust, London, UK
| | - Elizabeth Miles
- National Radiotherapy Trials QA Group, Mount Vernon Hospital, Northwood, UK
| | | | - Sally F Barrington
- King’s College London and Guy’s and St Thomas’ PET Centre, School of Biomedical Engineering and Imaging Sciences, King’s College London, King’s Health Partners, London, UK
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24
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Guo Z, Guo N, Gong K, Zhong S, Li Q. Gross tumor volume segmentation for head and neck cancer radiotherapy using deep dense multi-modality network. Phys Med Biol 2019; 64:205015. [PMID: 31514173 PMCID: PMC7186044 DOI: 10.1088/1361-6560/ab440d] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In radiation therapy, the accurate delineation of gross tumor volume (GTV) is crucial for treatment planning. However, it is challenging for head and neck cancer (HNC) due to the morphology complexity of various organs in the head, low targets to background contrast and potential artifacts on conventional planning CT images. Thus, manual delineation of GTV on anatomical images is extremely time consuming and suffers from inter-observer variability that leads to planning uncertainty. With the wide use of PET/CT imaging in oncology, complementary functional and anatomical information can be utilized for tumor contouring and bring a significant advantage for radiation therapy planning. In this study, by taking advantage of multi-modality PET and CT images, we propose an automatic GTV segmentation framework based on deep learning for HNC. The backbone of this segmentation framework is based on 3D convolution with dense connections which enables a better information propagation and takes full advantage of the features extracted from multi-modality input images. We evaluate our proposed framework on a dataset including 250 HNC patients. Each patient receives both planning CT and PET/CT imaging before radiation therapy (RT). Manually delineated GTV contours by radiation oncologists are used as ground truth in this study. To further investigate the advantage of our proposed Dense-Net framework, we also compared with the framework using 3D U-Net which is the state-of-the-art in segmentation tasks. Meanwhile, for each frame, the performance comparison between single modality input (PET or CT image) and multi-modality input (both PET/CT) is conducted. Dice coefficient, mean surface distance (MSD), 95th-percentile Hausdorff distance (HD95) and displacement of mass centroid (DMC) are calculated for quantitative evaluation. The dataset is split into train (140 patients), validation (35 patients) and test (75 patients) groups to optimize the network. Based on the results on independent test group, our proposed multi-modality Dense-Net (Dice 0.73) shows better performance than the compared network (Dice 0.71). Furthermore, the proposed Dense-Net structure has less trainable parameters than the 3D U-Net, which reduces the prediction variability. In conclusion, our proposed multi-modality Dense-Net can enable satisfied GTV segmentation for HNC using multi-modality images and yield superior performance than conventional methods. Our proposed method provides an automatic, fast and consistent solution for GTV segmentation and shows potentials to be generally applied for radiation therapy planning of a variety of cancer (e.g. lung, sarcoma, liver and so on).
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Affiliation(s)
- Zhe Guo
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China 100081
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA 02114
| | - Ning Guo
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA 02114
| | - Kuang Gong
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA 02114
| | - Shun’an Zhong
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China 100081
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA 02114
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25
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Apolle R, Appold S, Bijl HP, Blanchard P, Bussink J, Faivre-Finn C, Khalifa J, Laprie A, Lievens Y, Madani I, Ruffier A, de Ruysscher D, van Elmpt W, Troost EGC. Inter-observer variability in target delineation increases during adaptive treatment of head-and-neck and lung cancer. Acta Oncol 2019; 58:1378-1385. [PMID: 31271079 DOI: 10.1080/0284186x.2019.1629017] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Introduction: Inter-observer variability (IOV) in target volume delineation is a well-documented source of geometric uncertainty in radiotherapy. Such variability has not yet been explored in the context of adaptive re-delineation based on imaging data acquired during treatment. We compared IOV in the pre- and mid-treatment setting using expert primary gross tumour volume (GTV) and clinical target volume (CTV) delineations in locoregionally advanced head-and-neck squamous cell carcinoma (HNSCC) and (non-)small cell lung cancer [(N)SCLC]. Material and methods: Five and six observers participated in the HNSCC and (N)SCLC arm, respectively, and provided delineations for five cases each. Imaging data consisted of CT studies partly complemented by FDG-PET and was provided in two separate phases for pre- and mid-treatment. Global delineation compatibility was assessed with a volume overlap metric (the Generalised Conformity Index), while local extremes of IOV were identified through the standard deviation of surface distances from observer delineations to a median consensus delineation. Details of delineation procedures, in particular, GTV to CTV expansion and adaptation strategies, were collected through a questionnaire. Results: Volume overlap analysis revealed a worsening of IOV in all but one case per disease site, which failed to reach significance in this small sample (p-value range .063-.125). Changes in agreement were propagated from GTV to CTV delineations, but correlation could not be formally demonstrated. Surface distance based analysis identified longitudinal target extent as a pervasive source of disagreement for HNSCC. High variability in (N)SCLC was often associated with tumours abutting consolidated lung tissue or potentially invading the mediastinum. Adaptation practices were variable between observers with fewer than half stating that they consistently adapted pre-treatment delineations during treatment. Conclusion: IOV in target volume delineation increases during treatment, where a disparity in institutional adaptation practices adds to the conventional causes of IOV. Consensus guidelines are urgently needed.
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Affiliation(s)
- Rudi Apolle
- OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology – OncoRay, Dresden, Germany
| | - Steffen Appold
- OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Henk P. Bijl
- Department of Radiation Oncology, University Medical Center Groningen, Groningen, The Netherlands
| | - Pierre Blanchard
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Corinne Faivre-Finn
- The Christie NHS Foundation Trust, Division of Cancer Science, The University of Manchester, Manchester, UK
| | - Jonathan Khalifa
- Department of Radiotherapy, Institut Claudius Regaud/Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| | - Anne Laprie
- Department of Radiotherapy, Institut Claudius Regaud/Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| | - Yolande Lievens
- Radiation Oncology Department, Ghent University Hospital and Ghent University, Ghent, Belgium
| | - Indira Madani
- Department of Radiation Oncology, University Hospital Zürich, Zürich, Switzerland
| | - Amandine Ruffier
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Dirk de Ruysscher
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Esther G. C. Troost
- OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology – OncoRay, Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center DKFZ, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany
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26
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Sarkar V, Paxton A, Kunz J, Szegedi M, Nelson G, Rassiah‐Szegedi P, Zhao H, Huang YJ, Su F, Salter BJ. A systematic evaluation of the error detection abilities of a new diode transmission detector. J Appl Clin Med Phys 2019; 20:122-132. [PMID: 31385436 PMCID: PMC6753730 DOI: 10.1002/acm2.12691] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 06/19/2019] [Accepted: 07/20/2019] [Indexed: 02/02/2023] Open
Abstract
Transmission detectors meant to measure every beam delivered on a linear accelerator are now becoming available for monitoring the quality of the dose distribution delivered to the patient daily. The purpose of this work is to present results from a systematic evaluation of the error detection capabilities of one such detector, the Delta4 Discover. Existing patient treatment plans were modified through in‐house‐developed software to mimic various delivery errors that have been observed in the past. Errors included shifts in multileaf collimator leaf positions, changing the beam energy from what was planned, and a simulation of what would happen if the secondary collimator jaws did not track with the leaves as they moved. The study was done for simple 3D plans, static gantry intensity modulated radiation therapy plans as well as dynamic arc and volumetric modulated arc therapy (VMAT) plans. Baseline plans were delivered with both the Discover device and the Delta4 Phantom+ to establish baseline gamma pass rates. Modified plans were then delivered using the Discover only and the predicted change in gamma pass rate, as well as the detected leaf positions were evaluated. Leaf deviations as small as 0.5 mm for a static three‐dimensional field were detected, with this detection limit growing to 1 mm with more complex delivery modalities such as VMAT. The gamma pass rates dropped noticeably once the intentional leaf error introduced was greater than the distance‐to‐agreement criterion. The unit also demonstrated the desired drop in gamma pass rates of at least 20% when jaw tracking was intentionally disabled and when an incorrect energy was used for the delivery. With its ability to find errors intentionally introduced into delivered plans, the Discover shows promise of being a valuable, independent error detection tool that should serve to detect delivery errors that can occur during radiotherapy treatment.
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Affiliation(s)
| | | | | | | | | | | | - Hui Zhao
- University of UtahSalt Lake CityUTUSA
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27
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Interobserver variability in delineation of target volumes in head and neck cancer. Radiother Oncol 2019; 137:9-15. [DOI: 10.1016/j.radonc.2019.04.006] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 03/25/2019] [Accepted: 04/04/2019] [Indexed: 12/19/2022]
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28
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Cozzi L, Franzese C, Fogliata A, Franceschini D, Navarria P, Tomatis S, Scorsetti M. Predicting survival and local control after radiochemotherapy in locally advanced head and neck cancer by means of computed tomography based radiomics. Strahlenther Onkol 2019; 195:805-818. [PMID: 31222468 DOI: 10.1007/s00066-019-01483-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 06/06/2019] [Indexed: 12/31/2022]
Abstract
PURPOSE To appraise the ability of a radiomics signature to predict clinical outcome after definitive radiochemotherapy (RCT) of stage III-IV head and neck cancer. METHODS A cohort of 110 patients was included in a retrospective analysis. Radiomics texture features were extracted from the gross tumor volumes contoured on planning computed tomography (CT) images. The cohort of patients was randomly divided into a training (70 patients) and a validation (40 patients) cohorts. Textural features were correlated to survival and control data to build predictive models. All the significant predictors of the univariate analysis were included in a multivariate model. The quality of the models was appraised by means of the concordance index (CI). RESULTS A signature with 3 features was identified as predictive of overall survival (OS) with CI = 0.88 and 0.90 for the training and validation cohorts, respectively. A signature with 2 features was identified for progression-free survival (PFS; CI = 0.72 and 0.80); 2 features also characterized the signature for local control (LC; CI = 0.72 and 0.82). In all cases, the stratification in high- and low-risk groups for the training and validation cohorts led to significant differences in the actuarial curves. In the validation cohort the mean OS times (in months) were 78.9 ± 2.1 vs 67.4 ± 6.0 in the low- and high-risk groups, respectively, the PFS was 73.1 ± 3.7 and 50.7 ± 7.2, while the LC was 78.7 ± 2.1 and 63.9 ± 6.5. CONCLUSION CT-based radiomic signatures that correlate with survival and control after RCT were identified and allow low- and high-risk groups of patients to be identified.
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Affiliation(s)
- Luca Cozzi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy. .,Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy.
| | - Ciro Franzese
- Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Antonella Fogliata
- Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Davide Franceschini
- Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Pierina Navarria
- Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Stefano Tomatis
- Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Marta Scorsetti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy.,Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy
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29
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Ma Z, Zhou S, Wu X, Zhang H, Yan W, Sun S, Zhou J. Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning. Phys Med Biol 2019; 64:025005. [PMID: 30524024 DOI: 10.1088/1361-6560/aaf5da] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Multi-modality examinations have been extensively applied in current clinical cancer management. Leveraging multi-modality medical images can be highly beneficial for automated tumor segmentation as they provide complementary information that could make the segmentation of tumors more accurate. This paper investigates CNN-based methods for automated nasopharyngeal carcinoma (NPC) segmentation using computed tomography (CT) and magnetic resonance (MR) images. Specially, a multi-modality convolutional neural network (M-CNN) is designed to jointly learn a multi-modal similarity metric and segmentation of paired CT-MR images. By jointly optimizing the similarity learning error and the segmentation error, the feature learning processes of both modalities are mutually guided. In doing so, the segmentation sub-networks are able to take advantage of the other modality's information. Considering that each modality possesses certain distinctive characteristics, we combine the higher-layer features extracted by a single-modality CNN (S-CNN) and M-CNN to form a combined CNN (C-CNN) for each modality, which is able to further utilize the complementary information of different modalities and improve the segmentation performance. The proposed M-CNN and C-CNN were evaluated on 90 CT-MR images of NPC patients. Experimental results demonstrate that our methods achieve improved segmentation performance compared to their counterparts without multi-modal information fusion and the existing CNN-based multi-modality segmentation methods.
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Affiliation(s)
- Zongqing Ma
- College of Computer Science, Sichuan University, Chengdu 610065, People's Republic of China
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30
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Role of pretreatment 18F-FDG PET/CT parameters in predicting outcome of non-endemic EBV DNA-related nasopharyngeal cancer (NPC) patients treated with IMRT and chemotherapy. Radiol Med 2018; 124:414-421. [DOI: 10.1007/s11547-018-0980-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Accepted: 12/06/2018] [Indexed: 12/15/2022]
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31
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Sandström H, Jokura H, Chung C, Toma-Dasu I. Multi-institutional study of the variability in target delineation for six targets commonly treated with radiosurgery. Acta Oncol 2018; 57:1515-1520. [PMID: 29786462 DOI: 10.1080/0284186x.2018.1473636] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
BACKGROUND Although accurate delineation of the target is a key factor of success in radiosurgery there are no consensus guidelines for target contouring. AIM The aim of the present study was therefore to quantify the variability in target delineation and discuss the potential clinical implications, for six targets regarded as common in stereotactic radiosurgery. MATERIAL AND METHODS Twelve Gamma Knife centers participated in the study by contouring the targets and organs at risks and performing the treatment plans. Analysis of target delineation variability was based on metrics defined based on agreement volumes derived from overlapping structures following a previously developed method. The 50% agreement volume (AV50), the common and the encompassing volumes as well as the Agreement Volume Index (AVI) were determined. RESULTS Results showed that the lowest AVI (0.16) was found for one of the analyzed metastases (range of delineated volumes 1.27-3.33 cm3). AVI for the other two metastases was 0.62 and 0.37, respectively. Corresponding AVIs for the cavernous sinus meningioma, pituitary adenoma and vestibular schwannoma were 0.22, 0.37 and 0.50. CONCLUSIONS This study showed that the variability in the contouring was much higher than expected and therefore further work in standardizing the contouring practice in radiosurgery is warranted.
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Affiliation(s)
- Helena Sandström
- Medical Radiation Physics, Department of Physics, Stockholm University, Stockholm, Sweden
- Medical Radiation Physics, Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden
| | - Hidefumi Jokura
- Furukawa Seiryo Hospital, Jiro Suzuki Memorial Gamma House, Osaki, Japan
| | - Caroline Chung
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Iuliana Toma-Dasu
- Medical Radiation Physics, Department of Physics, Stockholm University, Stockholm, Sweden
- Medical Radiation Physics, Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden
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Ng SP, Dyer BA, Kalpathy-Cramer J, Mohamed ASR, Awan MJ, Gunn GB, Phan J, Zafereo M, Debnam JM, Lewis CM, Colen RR, Kupferman ME, Guha-Thakurta N, Canahuate G, Marai GE, Vock D, Hamilton B, Holland J, Cardenas CE, Lai S, Rosenthal D, Fuller CD. A prospective in silico analysis of interdisciplinary and interobserver spatial variability in post-operative target delineation of high-risk oral cavity cancers: Does physician specialty matter? Clin Transl Radiat Oncol 2018; 12:40-46. [PMID: 30148217 PMCID: PMC6105928 DOI: 10.1016/j.ctro.2018.07.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 07/31/2018] [Indexed: 11/21/2022] Open
Abstract
Background The aim of this study was to determine the interdisciplinary agreement in identifying the post-operative tumor bed. Methods Three radiation oncologists (ROs), four surgeons, and three radiologists segmented post-operative tumor and nodal beds for three patients with oral cavity cancer. Specialty cohort composite contours were created by STAPLE algorithm implementation results for interspecialty comparison. Dice similarity coefficient and Hausdorff distance were utilized to compare spatial differentials between specialties. Results There were significant differences between disciplines in target delineation. There was unacceptable variation in Dice similarity coefficient for each observer and discipline when compared to the STAPLE contours. Within surgery and radiology disciplines, there was good consistency in volumes. ROs and radiologists have similar Dice similarity coefficient scores compared to surgeons. Conclusion There were significant interdisciplinary differences in perceptions of tissue-at-risk. Better communication and explicit description of at-risk areas between disciplines is required to ensure high-risk areas are adequately targeted.
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Affiliation(s)
- Sweet Ping Ng
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Brandon A Dyer
- Department of Radiation Oncology, UC Davis Comprehensive Cancer Center, Sacramento, California, USA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | | | - Musaddiq J Awan
- Department of Radiation Oncology, Case Western Reserve University, Cleveland, Ohio, USA
| | - G Brandon Gunn
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jack Phan
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mark Zafereo
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - J Matthew Debnam
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Carol M Lewis
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rivka R Colen
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Michael E Kupferman
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Nandita Guha-Thakurta
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Guadalupe Canahuate
- Department of Electrical & Computer Engineering, University of Iowa, Iowa City, Iowa, USA
| | - G Elisabeta Marai
- Department of Computer Science, University of Illinois at Chicago, Chicago, Illinois, USA
| | - David Vock
- Department of Biostatistics, University of Minnesota of Public Health, Minneapolis, Minnesota, USA
| | - Bronwyn Hamilton
- Department of Radiology, Oregon Health & Science University, Portland, Oregon, USA
| | - John Holland
- Department of Radiation Oncology, Oregon Health & Science University, Portland, Oregon, USA
| | - Carlos E Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Stephen Lai
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - David Rosenthal
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Pavic M, Bogowicz M, Würms X, Glatz S, Finazzi T, Riesterer O, Roesch J, Rudofsky L, Friess M, Veit-Haibach P, Huellner M, Opitz I, Weder W, Frauenfelder T, Guckenberger M, Tanadini-Lang S. Influence of inter-observer delineation variability on radiomics stability in different tumor sites. Acta Oncol 2018. [PMID: 29513054 DOI: 10.1080/0284186x.2018.1445283] [Citation(s) in RCA: 130] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Radiomics is a promising methodology for quantitative analysis and description of radiological images using advanced mathematics and statistics. Tumor delineation, which is still often done manually, is an essential step in radiomics, however, inter-observer variability is a well-known uncertainty in radiation oncology. This study investigated the impact of inter-observer variability (IOV) in manual tumor delineation on the reliability of radiomic features (RF). METHODS Three different tumor types (head and neck squamous cell carcinoma (HNSCC), malignant pleural mesothelioma (MPM) and non-small cell lung cancer (NSCLC)) were included. For each site, eleven individual tumors were contoured on CT scans by three experienced radiation oncologists. Dice coefficients (DC) were calculated for quantification of delineation variability. RF were calculated with an in-house developed software implementation, which comprises 1404 features: shape (n = 18), histogram (n = 17), texture (n = 137) and wavelet (n = 1232). The IOV of RF was studied using the intraclass correlation coefficient (ICC). An ICC >0.8 indicates a good reproducibility. For the stable RF, an average linkage hierarchical clustering was performed to identify classes of uncorrelated features. RESULTS Median DC was high for NSCLC (0.86, range 0.57-0.90) and HNSCC (0.72, 0.21-0.89), whereas it was low for MPM (0.26, 0-0.9) indicating substantial IOV. Stability rate of RF correlated with DC and depended on tumor site, showing a high stability in NSCLC (90% of total parameters), acceptable stability in HNSCC (59% of total parameters) and low stability in MPM (36% of total parameters). Shape features showed the weakest stability across all tumor types. Hierarchical clustering revealed 14 groups of correlated and stable features for NSCLC and 6 groups for both HNSCC and MPM. CONCLUSION Inter-observer delineation variability has a relevant influence on radiomics analysis and is strongly influenced by tumor type. This leads to a reduced number of suitable imaging features.
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Affiliation(s)
- Matea Pavic
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Xaver Würms
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Stefan Glatz
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Tobias Finazzi
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Oliver Riesterer
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Johannes Roesch
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Leonie Rudofsky
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Martina Friess
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Patrick Veit-Haibach
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martin Huellner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Isabelle Opitz
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Walter Weder
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Thomas Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
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Belli ML, Mori M, Broggi S, Cattaneo GM, Bettinardi V, Dell'Oca I, Fallanca F, Passoni P, Vanoli EG, Calandrino R, Di Muzio N, Picchio M, Fiorino C. Quantifying the robustness of [ 18 F]FDG-PET/CT radiomic features with respect to tumor delineation in head and neck and pancreatic cancer patients. Phys Med 2018; 49:105-111. [PMID: 29866335 DOI: 10.1016/j.ejmp.2018.05.013] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 05/08/2018] [Accepted: 05/10/2018] [Indexed: 11/18/2022] Open
Abstract
PURPOSE To investigate the robustness of PET radiomic features (RF) against tumour delineation uncertainty in two clinically relevant situations. METHODS Twenty-five head-and-neck (HN) and 25 pancreatic cancer patients previously treated with 18F-Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based planning optimization were considered. Seven FDG-based contours were delineated for tumour (T) and positive lymph nodes (N, for HN patients only) following manual (2 observers), semi-automatic (based on SUV maximum gradient: PET_Edge) and automatic (40%, 50%, 60%, 70% SUV_max thresholds) methods. Seventy-three RF (14 of first order and 59 of higher order) were extracted using the CGITA software (v.1.4). The impact of delineation on volume agreement and RF was assessed by DICE and Intra-class Correlation Coefficients (ICC). RESULTS A large disagreement between manual and SUV_max method was found for thresholds ≥50%. Inter-observer variability showed median DICE values between 0.81 (HN-T) and 0.73 (pancreas). Volumes defined by PET_Edge were better consistent with the manual ones compared to SUV40%. Regarding RF, 19%/19%/47% of the features showed ICC < 0.80 between observers for HN-N/HN-T/pancreas, mostly in the Voxel-alignment matrix and in the intensity-size zone matrix families. RFs with ICC < 0.80 against manual delineation (taking the worst value) increased to 44%/36%/61% for PET_Edge and to 69%/53%/75% for SUV40%. CONCLUSIONS About 80%/50% of 72 RF were consistent between observers for HN/pancreas patients. PET_edge was sufficiently robust against manual delineation while SUV40% showed a worse performance. This result suggests the possibility to replace manual with semi-automatic delineation of HN and pancreas tumours in studies including PET radiomic analyses.
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Affiliation(s)
| | - Martina Mori
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Sara Broggi
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | | | | | - Italo Dell'Oca
- Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | | | - Paolo Passoni
- Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | | | | | - Nadia Di Muzio
- Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Maria Picchio
- Nuclear Medicine, San Raffaele Scientific Institute, Milano, Italy
| | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy.
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Tseng M, Tan TH, Zheng H, Soon YY, Tey J. Quality of head and neck radiotherapy reporting in randomized controlled trials. Head Neck 2018; 40:1854-1860. [PMID: 29575476 DOI: 10.1002/hed.25133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2017] [Revised: 11/29/2017] [Accepted: 02/01/2018] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The purpose of this clinical review was to determine the quality reporting of radiotherapy (RT) in randomized controlled trials (RCTs) of head and neck cancer and its impact on reporting of the trial primary efficacy and toxicity outcomes. METHODS We searched MEDLINE for eligible RCTs published between 1994 and 2015. We assessed the quality of RT reporting and bias in the reporting of trial outcomes using published criteria. RESULTS We found 67 eligible trial reports. There was significant variability in the quality of RT treatment reporting among the included trials. Thirty-two trials had adequate quality RT reporting. Cooperative group trials were more likely to have adequate quality reporting (odds ratio [OR] 3.02; 95% confidence interval [CI] 1.04-9.85). The quality of RT reporting did not influence the bias in the reporting of trial outcomes. CONCLUSION The quality of head and neck RT reporting in RCTs is variable and did not impact on bias in reporting of trial outcomes.
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Affiliation(s)
- Michelle Tseng
- Department of Radiation Oncology, National University Cancer Institute Singapore (NCIS), National University Health Systems (NUHS), Singapore
| | - Teng Hwee Tan
- Department of Radiation Oncology, National University Cancer Institute Singapore (NCIS), National University Health Systems (NUHS), Singapore
| | | | - Yu Yang Soon
- Department of Radiation Oncology, National University Cancer Institute Singapore (NCIS), National University Health Systems (NUHS), Singapore
| | - Jeremy Tey
- Department of Radiation Oncology, National University Cancer Institute Singapore (NCIS), National University Health Systems (NUHS), Singapore
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Kovacs DG, Rechner LA, Appelt AL, Berthelsen AK, Costa JC, Friborg J, Persson GF, Bangsgaard JP, Specht L, Aznar MC. Metal artefact reduction for accurate tumour delineation in radiotherapy. Radiother Oncol 2018; 126:479-486. [PMID: 29050958 PMCID: PMC5864514 DOI: 10.1016/j.radonc.2017.09.029] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 09/16/2017] [Accepted: 09/20/2017] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND PURPOSE Two techniques for metal artefact reduction for computed tomography were studied in order to identify their impact on tumour delineation in radiotherapy. MATERIALS AND METHODS Using specially designed phantoms containing metal implants (dental, spine and hip) as well as patient images, we investigated the impact of two methods for metal artefact reduction on (A) the size and severity of metal artefacts and the accuracy of Hounsfield Unit (HU) representation, (B) the visual impact of metal artefacts on image quality and (C) delineation accuracy. A metal artefact reduction algorithm (MAR) and two types of dual energy virtual monochromatic (DECT VM) reconstructions were used separately and in combination to identify the optimal technique for each implant site. RESULTS The artefact area and severity was reduced (by 48-76% and 58-79%, MAR and DECT VM respectively) and accurate Hounsfield-value representation was increased by 22-82%. For each energy, the observers preferred MAR over non-MAR reconstructions (p < 0.01 for dental and hip cases, p < 0.05 for the spine case). In addition, DECT VM was preferred for spine implants (p < 0.01). In all cases, techniques that improved target delineation significantly (p < 0.05) were identified. CONCLUSIONS DECT VM and MAR techniques improve delineation accuracy and the optimal of reconstruction technique depends on the type of metal implant.
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Affiliation(s)
- David Gergely Kovacs
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark, Lyngby, Denmark.
| | - Laura A Rechner
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Denmark; Niels Bohr Institute, University of Copenhagen, Denmark
| | - Ane L Appelt
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Denmark; Leeds Institute of Cancer and Pathology, University of Leeds, and Leeds Cancer Centre, St. James's University Hospital, UK
| | - Anne K Berthelsen
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Denmark; Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet Copenhagen University Hospital, Denmark
| | - Junia C Costa
- Department of Radiology, Copenhagen University Hospital Herlev Gentofte, Denmark
| | - Jeppe Friborg
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Denmark
| | - Gitte F Persson
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Denmark
| | | | - Lena Specht
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Marianne C Aznar
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, UK
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Mazzola R, Fiorentino A, Ricchetti F, Gregucci F, Corradini S, Alongi F. An update on radiation therapy in head and neck cancers. Expert Rev Anticancer Ther 2018; 18:359-364. [PMID: 29485913 DOI: 10.1080/14737140.2018.1446832] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
INTRODUCTION Technological and technical improvements allowed for significant advances in the field of radiation therapy (RT) of head and neck cancer (HNC). Several organ-sparing strategies have been investigated with the objective to decrease acute and long-term adverse effects and, subsequently, to assure a better quality of life in patients affected by HNC. In this context, intensity modulated irradiation and the use of multimodality-imaging could help clinicians to obtain a rapid dose fall off towards surrounding healthy tissues and a better delineation of targets volumes and organs at risk. Areas covered: A literature review was performed with the aim to offer an update on radiation therapy in HNC. Expert commentary: During these last years, radiation oncologists have observed a continuous changing regarding radiation treatment for HNC. The adoption of intensity-modulated RT (IMRT) and the use of multimodality-imaging for tumor volume definition and organs at risk or delineation have improved the clinical outcomes of HNC patients. In the future, a better integration of functional imaging for target volume delineation as well as adaptive delivery strategies will allow to further personalize radiation oncology in HNC. Furthermore, the latest breakthrough technologies, such as magnetic resonance imaging (MRI)-linacs and heavy particles technologies have a great potential to improve treatment-related quality of life in HNC. Future studies are needed to demonstrate the clinical advantages of these new RT technologies in HNC.
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Affiliation(s)
- Rosario Mazzola
- a Radiation Oncology , Sacro Cuore Don Calabria Hospital , Negrar-Verona , Italy
| | - Alba Fiorentino
- a Radiation Oncology , Sacro Cuore Don Calabria Hospital , Negrar-Verona , Italy
| | - Francesco Ricchetti
- a Radiation Oncology , Sacro Cuore Don Calabria Hospital , Negrar-Verona , Italy
| | - Fabiana Gregucci
- a Radiation Oncology , Sacro Cuore Don Calabria Hospital , Negrar-Verona , Italy
| | - Stefanie Corradini
- b Radiation Oncology , University Hospital, LMU Munich , Munich , Germany
| | - Filippo Alongi
- a Radiation Oncology , Sacro Cuore Don Calabria Hospital , Negrar-Verona , Italy.,c Radiation Oncology , University of Brescia , Brescia , Italy
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Soufi M, Kamali-Asl A, Geramifar P, Rahmim A. A Novel Framework for Automated Segmentation and Labeling of Homogeneous Versus Heterogeneous Lung Tumors in [ 18F]FDG-PET Imaging. Mol Imaging Biol 2018; 19:456-468. [PMID: 27770402 DOI: 10.1007/s11307-016-1015-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE Determination of intra-tumor high-uptake area using 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET) imaging is an important consideration for dose painting in radiation treatment applications. The aim of our study was to develop a framework towards automated segmentation and labeling of homogeneous vs. heterogeneous tumors in clinical lung [18F]FDG-PET with the capability of intra-tumor high-uptake region delineation. PROCEDURES We utilized and extended a fuzzy random walk PET tumor segmentation algorithm to delineate intra-tumor high-uptake areas. Tumor textural feature (TF) analysis was used to find a relationship between tumor type and TF values. Segmentation accuracy was evaluated quantitatively utilizing 70 clinical [18F]FDG-PET lung images of patients with a total of 150 solid tumors. For volumetric analysis, the Dice similarity coefficient (DSC) and Hausdorff distance (HD) measures were extracted with respect to gold-standard manual segmentation. A multi-linear regression model was also proposed for automated tumor labeling based on TFs, including cross-validation analysis. RESULTS Two-tailed t test analysis of TFs between homogeneous and heterogeneous tumors revealed significant statistical difference for size-zone variability (SZV), intensity variability (IV), zone percentage (ZP), proposed parameters II and III, entropy and tumor volume (p < 0.001), dissimilarity, high intensity emphasis (HIE), and SUVmin (p < 0.01). Lower statistical differences were observed for proposed parameter I (p = 0.02), and no significant differences were observed for SUVmax and SUVmean. Furthermore, the Spearman rank analysis between visual tumor labeling and TF analysis depicted a significant correlation for SZV, IV, entropy, parameters II and III, and tumor volume (0.68 ≤ ρ ≤ 0.84) and moderate correlation for ZP, HIE, homogeneity, dissimilarity, parameter I, and SUVmin (0.22 ≤ ρ ≤ 0.52), while no correlations were observed for SUVmax and SUVmean (ρ < 0.08). The multi-linear regression model for automated tumor labeling process resulted in R 2 and RMSE values of 0.93 and 0.14, respectively (p < 0.001), and generated tumor labeling sensitivity and specificity of 0.93 and 0.89. With respect to baseline random walk segmentation, the results showed significant (p < 0.001) mean DSC, HD, and SUVmean error improvements of 21.4 ± 11.5 %, 1.4 ± 0.8 mm, and 16.8 ± 8.1 % in homogeneous tumors and 7.4 ± 4.4 %, 1.5 ± 0.6 mm, and 7.9 ± 2.7 % in heterogeneous lesions. In addition, significant (p < 0.001) mean DSC, HD, and SUVmean error improvements were observed for tumor sub-volume delineations, namely 5 ± 2 %, 1.5 ± 0.6 mm, and 7 ± 3 % for the proposed Fuzzy RW method compared to RW segmentation. CONCLUSION We proposed and demonstrated an automatic framework for significantly improved segmentation and labeling of homogeneous vs. heterogeneous tumors in lung [18F]FDG-PET images.
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Affiliation(s)
- Motahare Soufi
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran
| | - Alireza Kamali-Asl
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran.
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Arman Rahmim
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA.
- Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
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Hybrid PET/MRI-based delineation of gross tumor volume in head and neck cancer and tumor parameter analysis. Nucl Med Commun 2018; 38:642-649. [PMID: 28489688 DOI: 10.1097/mnm.0000000000000687] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Accurate target delineation allows an increase in radiation dose to the target tumor while reducing damage to the surrounding normal tissue. However, there is currently no standard for evaluating volumes measured by different imaging modalities. The aim of this study is to evaluate the feasibility of contouring gross tumor volume (GTV) by PET/MRI in head and neck cancer, and to define an adaptive threshold level (aTL) for delineating the biological target volume (BTV). PATIENTS AND METHODS Eighteen head and neck cancer patients underwent time of flight PET/MRI before chemoradiotherapy. Different GTVs of primary tumors and metastatic lymph nodes were manually contoured on MRI (GTVMRI), PET (GTVVIS), and fused PET/MRI (GTVFUS). An MRI-based GTV contour was substituted for the pathologic GTV. The percentile threshold boundary of the maximum standardized uptake value (SUVmax) for the BTV was determined when the volume of BTV approached that of GTVMRI. RESULTS All GTVs were highly correlated (all Pearson's r>0.85, all P<0.001). Tumor diameter strongly correlated with GTVs (r=0.7-0.8 for all lesions and primary tumor; r=0.8-0.9 for lymph node metastases). aTL and SUVmax were moderately correlated for all lesions (r=-0.692, P<0.001) and were strongly correlated for primary tumors (r=-0.866, P<0.001). CONCLUSION Delineating GTV on hybrid PET/MRIs is feasible, and aTL, the threshold boundary of BTV, was correlated inversely with the SUVmax.
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40
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Bogowicz M, Riesterer O, Ikenberg K, Stieb S, Moch H, Studer G, Guckenberger M, Tanadini-Lang S. Computed Tomography Radiomics Predicts HPV Status and Local Tumor Control After Definitive Radiochemotherapy in Head and Neck Squamous Cell Carcinoma. Int J Radiat Oncol Biol Phys 2017; 99:921-928. [DOI: 10.1016/j.ijrobp.2017.06.002] [Citation(s) in RCA: 108] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 05/15/2017] [Accepted: 06/05/2017] [Indexed: 01/05/2023]
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Chang ATY, Tan LT, Duke S, Ng WT. Challenges for Quality Assurance of Target Volume Delineation in Clinical Trials. Front Oncol 2017; 7:221. [PMID: 28993798 PMCID: PMC5622143 DOI: 10.3389/fonc.2017.00221] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2017] [Accepted: 09/01/2017] [Indexed: 12/30/2022] Open
Abstract
In recent years, new radiotherapy techniques have emerged that aim to improve treatment outcome and reduce toxicity. The standard method of evaluating such techniques is to conduct large scale multicenter clinical trials, often across continents. A major challenge for such trials is quality assurance to ensure consistency of treatment across all participating centers. Analyses from previous studies have shown that poor compliance and protocol violation have a significant adverse effect on treatment outcomes. The results of the clinical trials may, therefore, be confounded by poor quality radiotherapy. Target volume delineation (TVD) is one of the most critical steps in the radiotherapy process. Many studies have shown large inter-observer variations in contouring, both within and outside of clinical trials. High precision techniques, such as intensity-modulated radiotherapy, image-guided brachytherapy, and stereotactic radiotherapy have steep dose gradients, and errors in contouring may lead to inadequate dose to the tumor and consequently, reduce the chance of cure. Similarly, variation in organ at risk delineation will make it difficult to evaluate dose response for toxicity. This article reviews the literature on TVD variability and its impact on dosimetry and clinical outcomes. The implications for quality assurance in clinical trials are discussed.
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Affiliation(s)
- Amy Tien Yee Chang
- Department of Clinical Oncology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, Hong Kong.,Department of Clinical Oncology, University of Hong Kong, Hong Kong
| | - Li Tee Tan
- Department of Oncology, Cambridge University Hospitals NHS Trust, Cambridge, United Kingdom
| | - Simon Duke
- Department of Oncology, Cambridge University Hospitals NHS Trust, Cambridge, United Kingdom
| | - Wai-Tong Ng
- Department of Clinical Oncology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, Hong Kong
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18F-Fluorodeoxyglucose-PET/CT in locally advanced head and neck cancer can influence the stage migration and nodal radiation treatment volumes. Radiol Med 2017; 122:952-959. [DOI: 10.1007/s11547-017-0804-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 08/16/2017] [Indexed: 12/23/2022]
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Besemer AE, Titz B, Grudzinski JJ, Weichert JP, Kuo JS, Robins HI, Hall LT, Bednarz BP. Impact of PET and MRI threshold-based tumor volume segmentation on patient-specific targeted radionuclide therapy dosimetry using CLR1404. Phys Med Biol 2017; 62:6008-6025. [PMID: 28682793 DOI: 10.1088/1361-6560/aa716d] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Variations in tumor volume segmentation methods in targeted radionuclide therapy (TRT) may lead to dosimetric uncertainties. This work investigates the impact of PET and MRI threshold-based tumor segmentation on TRT dosimetry in patients with primary and metastatic brain tumors. In this study, PET/CT images of five brain cancer patients were acquired at 6, 24, and 48 h post-injection of 124I-CLR1404. The tumor volume was segmented using two standardized uptake value (SUV) threshold levels, two tumor-to-background ratio (TBR) threshold levels, and a T1 Gadolinium-enhanced MRI threshold. The dice similarity coefficient (DSC), jaccard similarity coefficient (JSC), and overlap volume (OV) metrics were calculated to compare differences in the MRI and PET contours. The therapeutic 131I-CLR1404 voxel-level dose distribution was calculated from the 124I-CLR1404 activity distribution using RAPID, a Geant4 Monte Carlo internal dosimetry platform. The TBR, SUV, and MRI tumor volumes ranged from 2.3-63.9 cc, 0.1-34.7 cc, and 0.4-11.8 cc, respectively. The average ± standard deviation (range) was 0.19 ± 0.13 (0.01-0.51), 0.30 ± 0.17 (0.03-0.67), and 0.75 ± 0.29 (0.05-1.00) for the JSC, DSC, and OV, respectively. The DSC and JSC values were small and the OV values were large for both the MRI-SUV and MRI-TBR combinations because the regions of PET uptake were generally larger than the MRI enhancement. Notable differences in the tumor dose volume histograms were observed for each patient. The mean (standard deviation) 131I-CLR1404 tumor doses ranged from 0.28-1.75 Gy GBq-1 (0.07-0.37 Gy GBq-1). The ratio of maximum-to-minimum mean doses for each patient ranged from 1.4-2.0. The tumor volume and the interpretation of the tumor dose is highly sensitive to the imaging modality, PET enhancement metric, and threshold level used for tumor volume segmentation. The large variations in tumor doses clearly demonstrate the need for standard protocols for multimodality tumor segmentation in TRT dosimetry.
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Affiliation(s)
- Abigail E Besemer
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53705, United States of America. Department of Human Oncology, University of Wisconsin-Madison, Madison, WI 53705, United States of America
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44
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Gudi S, Ghosh-Laskar S, Agarwal JP, Chaudhari S, Rangarajan V, Nojin Paul S, Upreti R, Murthy V, Budrukkar A, Gupta T. Interobserver Variability in the Delineation of Gross Tumour Volume and Specified Organs-at-risk During IMRT for Head and Neck Cancers and the Impact of FDG-PET/CT on Such Variability at the Primary Site. J Med Imaging Radiat Sci 2017; 48:184-192. [DOI: 10.1016/j.jmir.2016.11.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Revised: 10/29/2016] [Accepted: 11/07/2016] [Indexed: 11/25/2022]
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Noid G, Tai A, Chen GP, Robbins J, Li XA. Reducing radiation dose and enhancing imaging quality of 4DCT for radiation therapy using iterative reconstruction algorithms. Adv Radiat Oncol 2017; 2:515-521. [PMID: 29114620 PMCID: PMC5605285 DOI: 10.1016/j.adro.2017.04.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 02/20/2017] [Accepted: 04/11/2017] [Indexed: 11/16/2022] Open
Abstract
Purpose Four-dimensional computed tomography (CT) images are typically used to quantify the necessary internal target volumes for thoracic and abdominal tumors. However, 4-dimensional CT is typically associated with excessive imaging dose to patients and the situation is exacerbated when using repeat 4-dimensional CT imaging on a weekly or daily basis throughout fractionated therapy. The aim of this work is to evaluate an iterative reconstruction (IR) algorithm that helps reduce the imaging dose to the patient while maintaining imaging quality as quantified by point spread function and contrast-to-noise ratios (CNRs). Methods and materials An IR algorithm, SAFIRE, was applied to CT data of a phantom and patients with varying CT doses and reconstruction kernels. Phantom data enable measurements of spatial resolution, contrast, and noise. The impact of SAFIRE on 4-dimensional CT was assessed with patient data acquired at 2 different dose levels during image guided radiation therapy with an in-room CT. Results Phantom data demonstrate that IR reduces noise approximately in proportion to the number of iterations indicated by the strength (SAFIRE 1 to SAFIRE 5). Spatial resolution and contrast are conserved independent of dose and reconstruction parameters. The CNR increases with an increase of imaging dose or an increase in the number of iterations. The use of IR on CT sets confirms the results that were derived from phantom scans. The IR significantly enhances single breathing phase CTs in 4-dimensional CT sets as assessed by CT number discrimination. Furthermore, the IR of the low dose 4-dimensional CT features a 45% increase in the CNR in comparison with the standard dose 4-dimensional CT. Conclusions The use of IR algorithms reduces noise while preserving spatial resolution and contrast, as evaluated from both phantom and patient CT data sets. For 4-dimensional CT, the IR can significantly improve image quality and reduce imaging dose without compromising image quality.
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Affiliation(s)
- George Noid
- Medical College of Wisconsin, Department of Radiation Oncology, Milwaukee, Wisconsin
| | - An Tai
- Medical College of Wisconsin, Department of Radiation Oncology, Milwaukee, Wisconsin
| | - Guang-Pei Chen
- Medical College of Wisconsin, Department of Radiation Oncology, Milwaukee, Wisconsin
| | - Jared Robbins
- Medical College of Wisconsin, Department of Radiation Oncology, Milwaukee, Wisconsin
| | - X Allen Li
- Medical College of Wisconsin, Department of Radiation Oncology, Milwaukee, Wisconsin
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Lin CS, de Oliveira Santos AB, Silva ELE, de Matos LL, Moyses RA, Kulcsar MAV, Pinto FR, Brandão LG, Cernea CR. Tumor volume as an independent predictive factor of worse survival in patients with oral cavity squamous cell carcinoma. Head Neck 2017; 39:960-964. [DOI: 10.1002/hed.24714] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Revised: 10/31/2016] [Accepted: 12/12/2016] [Indexed: 02/04/2023] Open
Affiliation(s)
- Chin Shien Lin
- Department of Head and Neck Surgery of Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo; São Paulo Brazil
| | - André Bandiera de Oliveira Santos
- Department of Head and Neck Surgery of Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo; São Paulo Brazil
| | - Evandro Lima e Silva
- Department of Head and Neck Surgery of Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo; São Paulo Brazil
| | - Leandro Luongo de Matos
- Department of Head and Neck Surgery of Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo; São Paulo Brazil
| | - Raquel Ajub Moyses
- Department of Head and Neck Surgery of Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo; São Paulo Brazil
| | - Marco Aurélio Vamondes Kulcsar
- Department of Head and Neck Surgery of Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo; São Paulo Brazil
| | - Fábio Roberto Pinto
- Department of Head and Neck Surgery of Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo; São Paulo Brazil
| | - Lenine Garcia Brandão
- Department of Head and Neck Surgery of Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo; São Paulo Brazil
| | - Claudio Roberto Cernea
- Department of Head and Neck Surgery of Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo; São Paulo Brazil
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Vinod SK, Jameson MG, Min M, Holloway LC. Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies. Radiother Oncol 2016; 121:169-179. [PMID: 27729166 DOI: 10.1016/j.radonc.2016.09.009] [Citation(s) in RCA: 214] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Revised: 08/27/2016] [Accepted: 09/25/2016] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE Volume delineation is a well-recognised potential source of error in radiotherapy. Whilst it is important to quantify the degree of interobserver variability (IOV) in volume delineation, the resulting impact on dosimetry and clinical outcomes is a more relevant endpoint. We performed a literature review of studies evaluating IOV in target volume and organ-at-risk (OAR) delineation in order to analyse these with respect to the metrics used, reporting of dosimetric consequences, and use of statistical tests. METHODS AND MATERIALS Medline and Pubmed databases were queried for relevant articles using keywords. We included studies published in English between 2000 and 2014 with more than two observers. RESULTS 119 studies were identified covering all major tumour sites. CTV (n=47) and GTV (n=38) were most commonly contoured. Median number of participants and data sets were 7 (3-50) and 9 (1-132) respectively. There was considerable heterogeneity in the use of metrics and methods of analysis. Statistical analysis of results was reported in 68% (n=81) and dosimetric consequences in 21% (n=25) of studies. CONCLUSION There is a lack of consistency in conducting and reporting analyses from IOV studies. We suggest a framework to use for future studies evaluating IOV.
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Affiliation(s)
- Shalini K Vinod
- Cancer Therapy Centre, Liverpool Hospital, Australia; South Western Sydney Clinical School, University of New South Wales, Australia; Western Sydney University, Australia.
| | - Michael G Jameson
- Cancer Therapy Centre, Liverpool Hospital, Australia; Ingham Institute of Applied Medical Research, Liverpool Hospital, Australia; Centre for Medical Radiation Physics, University of Wollongong, Australia
| | - Myo Min
- Cancer Therapy Centre, Liverpool Hospital, Australia; South Western Sydney Clinical School, University of New South Wales, Australia; Ingham Institute of Applied Medical Research, Liverpool Hospital, Australia
| | - Lois C Holloway
- Cancer Therapy Centre, Liverpool Hospital, Australia; South Western Sydney Clinical School, University of New South Wales, Australia; Ingham Institute of Applied Medical Research, Liverpool Hospital, Australia; Centre for Medical Radiation Physics, University of Wollongong, Australia
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Cacicedo J, Navarro A, Del Hoyo O, Gomez-Iturriaga A, Alongi F, Medina JA, Elicin O, Skanjeti A, Giammarile F, Bilbao P, Casquero F, de Bari B, Dal Pra A. Role of fluorine-18 fluorodeoxyglucose PET/CT in head and neck oncology: the point of view of the radiation oncologist. Br J Radiol 2016; 89:20160217. [PMID: 27416996 DOI: 10.1259/bjr.20160217] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Squamous cell carcinoma is the most common malignant tumour of the head and neck. The initial TNM staging, the evaluation of the tumour response during treatment, and the long-term surveillance are crucial moments in the approach to head and neck squamous cell carcinoma (HNSCC). Thus, at each of these moments, the choice of the best diagnostic tool providing the more precise and larger information is crucial. Positron emission tomography with fluorine-18 fludeoxyglucose integrated with CT (18F-FDG-PET/CT) rapidly gained clinical acceptance, and it has become an important imaging tool in routine clinical oncology. However, controversial data are currently available, for example, on the role of 18F-FDG-PET/CT imaging during radiotherapy planning, the prognostic value or its real clinical impact on treatment decisions. In this article, the role of 18F-FDG-PET/CT imaging in HNSCC during pre-treatment staging, radiotherapy planning, treatment response assessment, prognosis and follow-up is reviewed focusing on current evidence and controversial issues. A proposal on how to integrate 18F-FDG-PET/CT in daily clinical practice is also described.
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Affiliation(s)
- Jon Cacicedo
- 1 Radiation Oncology Department, Cruces University Hospital/Biocruces Health Research Institute, Barakaldo, Spain.,2 Grupo Español de Oncología Radioterápica en Cabeza y Cuello (GEORCC)
| | - Arturo Navarro
- 3 Radiation Oncology Department, Hospital Duran i Reynals (ICO) Avda, Gran Via de L´Hospitalet, Hospitalet de Llobregat, Barcelona, Spain
| | - Olga Del Hoyo
- 1 Radiation Oncology Department, Cruces University Hospital/Biocruces Health Research Institute, Barakaldo, Spain
| | - Alfonso Gomez-Iturriaga
- 1 Radiation Oncology Department, Cruces University Hospital/Biocruces Health Research Institute, Barakaldo, Spain
| | - Filippo Alongi
- 4 Radiation Oncology Department, Sacro Cuore-Don Calabria Hospital, Verona, Italy
| | - Jose A Medina
- 2 Grupo Español de Oncología Radioterápica en Cabeza y Cuello (GEORCC).,5 Radiation Oncology Department, Hospital Universitario Virgen de la Victoria, Malaga, Spain
| | - Olgun Elicin
- 6 Radiation Oncology Department, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Andrea Skanjeti
- 7 Nuclear Medicine Department, Hospices Civils de Lyon, Université Claude Bernard Lyon 1, Lyon, France
| | - Francesco Giammarile
- 7 Nuclear Medicine Department, Hospices Civils de Lyon, Université Claude Bernard Lyon 1, Lyon, France
| | - Pedro Bilbao
- 1 Radiation Oncology Department, Cruces University Hospital/Biocruces Health Research Institute, Barakaldo, Spain
| | - Francisco Casquero
- 1 Radiation Oncology Department, Cruces University Hospital/Biocruces Health Research Institute, Barakaldo, Spain
| | - Berardino de Bari
- 8 fESTRO Radiation Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Alan Dal Pra
- 6 Radiation Oncology Department, Inselspital, Bern University Hospital, Bern, Switzerland
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Patel DA, Chang ST, Goodman KA, Quon A, Thorndyke B, Gambhir SS, McMillan A, Loo BW, Koong AC. Impact of Integrated PET/CT on Variability of Target Volume Delineation in Rectal Cancer. Technol Cancer Res Treat 2016; 6:31-6. [PMID: 17241098 DOI: 10.1177/153303460700600105] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Several studies have demonstrated substantial variability among individual radiation oncologists in defining target volumes using computed tomography (CT). The objective of this study was to determine the impact of combined positron emission tomography and computed tomography (PET/CT) on inter-observer variability of target volume delineation in rectal cancer. We also compared the relative concordance of two PET imaging tracers, 18F-fluorodeoxyglucose (FDG) and 18F-fluorodeoxythymidine (FLT), against conventional computed tomography (CT). Six consecutive patients with locally advanced rectal cancer were enrolled onto an institutional protocol involving preoperative chemoradiotherapy and correlative studies including FDG- and FLT-PET scans acquired in the treatment position. Using these image data sets, four radiation oncologists independently delineated primary and nodal gross tumor volumes (GTVp and GTVn) for a hypothetical boost treatment. Contours were first defined based on CT alone with observers blinded to the PET images, then based on combined PET/CT. An inter-observer similarity index (SI), ranging from a value of 0 for complete disagreement to 1 for complete agreement of contoured voxels, was calculated for each set of volumes. For primary gross tumor volume (GTVp), the difference in estimated SI between CT and FDG was modest (CT SI = 0.77 vs. FDG SI = 0.81), but statistically significant (p = 0.013). The SI difference between CT and FLT for GTVp was also slight (FLT SI = 0.80) and marginally non-significant (p < 0.082). For nodal gross tumor volume, (GTVn), SI was significantly lower for CT based volumes with an estimated SI of 0.22 compared to an estimated SI of 0.70 for FDG-PET/CT (p < 0.0001) and an estimated SI of 0.70 for FLT-PET/CT (p < 0.0001). Boost target volumes in rectal cancer based on combined PET/CT results in lower inter-observer variability compared with CT alone, particularly for nodal disease. The use of FDG and FLT did not appear to be different from this perspective.
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Affiliation(s)
- Deep A Patel
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wibur Drive, Stanford, CA 94305-5847, USA
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Yang J, Beadle BM, Garden AS, Schwartz DL, Aristophanous M. A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy. Med Phys 2016; 42:5310-20. [PMID: 26328980 DOI: 10.1118/1.4928485] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. METHODS Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation-maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the "ground truth" for quantitative evaluation. RESULTS The median multichannel segmented GTV of the primary tumor was 15.7 cm(3) (range, 6.6-44.3 cm(3)), while the PET segmented GTV was 10.2 cm(3) (range, 2.8-45.1 cm(3)). The median physician-defined GTV was 22.1 cm(3) (range, 4.2-38.4 cm(3)). The median difference between the multichannel segmented and physician-defined GTVs was -10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was -19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55-0.84), and the median sensitivity and positive predictive value between them were 0.76 and 0.81, respectively. CONCLUSIONS The authors developed an automated multimodality segmentation algorithm for tumor volume delineation and validated this algorithm for head and neck cancer radiotherapy. The multichannel segmented GTV agreed well with the physician-defined GTV. The authors expect that their algorithm will improve the accuracy and consistency in target definition for radiotherapy.
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Affiliation(s)
- Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Beth M Beadle
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Adam S Garden
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - David L Schwartz
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390
| | - Michalis Aristophanous
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
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