1
|
Han YM, Ou D, Chai WM, Yang WL, Liu YL, Xiao JF, Zhang W, Qi WX, Chen JY. Exploration of anatomical distribution of brain metastasis from breast cancer at first diagnosis assisted by artificial intelligence. Heliyon 2024; 10:e29350. [PMID: 38694110 PMCID: PMC11061689 DOI: 10.1016/j.heliyon.2024.e29350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/03/2024] [Accepted: 04/05/2024] [Indexed: 05/03/2024] Open
Abstract
Objectives This study aimed to explore the spatial distribution of brain metastases (BMs) from breast cancer (BC) and to identify the high-risk sub-structures in BMs that are involved at first diagnosis. Methods Magnetic resonance imaging (MRI) scans were retrospectively reviewed at our centre. The brain was divided into eight regions according to its anatomy and function, and the volume of each region was calculated. The identification and volume calculation of metastatic brain lesions were accomplished using an automatically segmented 3D BUC-Net model. The observed and expected rates of BMs were compared using 2-tailed proportional hypothesis testing. Results A total of 250 patients with BC who presented with 1694 BMs were retrospectively identified. The overall observed incidences of the substructures were as follows: cerebellum, 42.1 %; frontal lobe, 20.1 %; occipital lobe, 9.7 %; temporal lobe, 8.0 %; parietal lobe, 13.1 %; thalamus, 4.7 %; brainstem, 0.9 %; and hippocampus, 1.3 %. Compared with the expected rate based on the volume of different brain regions, the cerebellum, occipital lobe, and thalamus were identified as higher risk regions for BMs (P value ≤ 5.6*10-3). Sub-group analysis according to the type of BC indicated that patients with triple-negative BC had a high risk of involvement of the hippocampus and brainstem. Conclusions Among patients with BC, the cerebellum, occipital lobe and thalamus were identified as higher-risk regions than expected for BMs. The brainstem and hippocampus were high-risk areas of the BMs in triple negative breast cancer. However, further validation of this conclusion requires a larger sample size.
Collapse
Affiliation(s)
- Yi-min Han
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Dan Ou
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wei-min Chai
- Department of Radiology, RuiJin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wen-lei Yang
- Department of Neurosurgery, RuiJin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Ying-long Liu
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - Ji-feng Xiao
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - Wei Zhang
- Shanghai United Imaging Healthcare Co., Ltd. Shanghai, China
| | - Wei-xiang Qi
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jia-yi Chen
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| |
Collapse
|
2
|
Kim M, Wang JY, Lu W, Jiang H, Stojadinovic S, Wardak Z, Dan T, Timmerman R, Wang L, Chuang C, Szalkowski G, Liu L, Pollom E, Rahimy E, Soltys S, Chen M, Gu X. Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today? Bioengineering (Basel) 2024; 11:454. [PMID: 38790322 PMCID: PMC11117895 DOI: 10.3390/bioengineering11050454] [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: 03/28/2024] [Revised: 04/26/2024] [Accepted: 04/30/2024] [Indexed: 05/26/2024] Open
Abstract
Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences.
Collapse
Affiliation(s)
- Matthew Kim
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Jen-Yeu Wang
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Weiguo Lu
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Hao Jiang
- NeuralRad LLC, Madison, WI 53717, USA
| | | | - Zabi Wardak
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Tu Dan
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Robert Timmerman
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Lei Wang
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Cynthia Chuang
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Gregory Szalkowski
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Lianli Liu
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Erqi Pollom
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Elham Rahimy
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Scott Soltys
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Mingli Chen
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Xuejun Gu
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| |
Collapse
|
3
|
Yu Z, Li X, Li J, Chen W, Tang Z, Geng D. HSA-net with a novel CAD pipeline boosts both clinical brain tumor MR image classification and segmentation. Comput Biol Med 2024; 170:108039. [PMID: 38308874 DOI: 10.1016/j.compbiomed.2024.108039] [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: 10/19/2023] [Revised: 01/07/2024] [Accepted: 01/26/2024] [Indexed: 02/05/2024]
Abstract
Brain tumors are among the most prevalent neoplasms in current medical studies. Accurately distinguishing and classifying brain tumor types accurately is crucial for patient treatment and survival in clinical practice. However, existing computer-aided diagnostic pipelines are inadequate for practical medical use due to tumor complexity. In this study, we curated a multi-centre brain tumor dataset that includes various clinical brain tumor data types, including segmentation and classification annotations, surpassing previous efforts. To enhance brain tumor segmentation accuracy, we propose a new segmentation method: HSA-Net. This method utilizes the Shared Weight Dilated Convolution module (SWDC) and Hybrid Dense Dilated Convolution module (HDense) to capture multi-scale information while minimizing parameter count. The Effective Multi-Dimensional Attention (EMA) and Important Feature Attention (IFA) modules effectively aggregate task-related information. We introduce a novel clinical brain tumor computer-aided diagnosis pipeline (CAD) that combines HSA-Net with pipeline modification. This approach not only improves segmentation accuracy but also utilizes the segmentation mask as an additional channel feature to enhance brain tumor classification results. Our experimental evaluation of 3327 real clinical data demonstrates the effectiveness of the proposed method, achieving an average Dice coefficient of 86.85 % for segmentation and a classification accuracy of 95.35 %. We also validated the effectiveness of our proposed method using the publicly available BraTS dataset.
Collapse
Affiliation(s)
- Zekuan Yu
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China.
| | - Xiang Li
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China; School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan, 232000, China
| | - Jiaxin Li
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Weiqiang Chen
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Zhiri Tang
- School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, China
| | - Daoying Geng
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China; Huashan Hospital, Fudan University, Shanghai, 200040, China.
| |
Collapse
|
4
|
Teng Y, Chen C, Shu X, Zhao F, Zhang L, Xu J. Automated, fast, robust brain extraction on contrast-enhanced T1-weighted MRI in presence of brain tumors: an optimized model based on multi-center datasets. Eur Radiol 2024; 34:1190-1199. [PMID: 37615767 PMCID: PMC10853304 DOI: 10.1007/s00330-023-10078-4] [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: 04/05/2023] [Revised: 07/12/2023] [Accepted: 07/14/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVES Existing brain extraction models should be further optimized to provide more information for oncological analysis. We aimed to develop an nnU-Net-based deep learning model for automated brain extraction on contrast-enhanced T1-weighted (T1CE) images in presence of brain tumors. METHODS This is a multi-center, retrospective study involving 920 patients. A total of 720 cases with four types of intracranial tumors from private institutions were collected and set as the training group and the internal test group. Mann-Whitney U test (U test) was used to investigate if the model performance was associated with pathological types and tumor characteristics. Then, the generalization of model was independently tested on public datasets consisting of 100 glioma and 100 vestibular schwannoma cases. RESULTS In the internal test, the model achieved promising performance with median Dice similarity coefficient (DSC) of 0.989 (interquartile range (IQR), 0.988-0.991), and Hausdorff distance (HD) of 6.403 mm (IQR, 5.099-8.426 mm). U test suggested a slightly descending performance in meningioma and vestibular schwannoma group. The results of U test also suggested that there was a significant difference in peritumoral edema group, with median DSC of 0.990 (IQR, 0.989-0.991, p = 0.002), and median HD of 5.916 mm (IQR, 5.000-8.000 mm, p = 0.049). In the external test, our model also showed to be robust performance, with median DSC of 0.991 (IQR, 0.983-0.998) and HD of 8.972 mm (IQR, 6.164-13.710 mm). CONCLUSIONS For automated processing of MRI neuroimaging data presence of brain tumors, the proposed model can perform brain extraction including important superficial structures for oncological analysis. CLINICAL RELEVANCE STATEMENT The proposed model serves as a radiological tool for image preprocessing in tumor cases, focusing on superficial brain structures, which could streamline the workflow and enhance the efficiency of subsequent radiological assessments. KEY POINTS • The nnU-Net-based model is capable of segmenting significant superficial structures in brain extraction. • The proposed model showed feasible performance, regardless of pathological types or tumor characteristics. • The model showed generalization in the public datasets.
Collapse
Affiliation(s)
- Yuen Teng
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyue Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
- West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China.
| | - Xin Shu
- College of Computer Science, Sichuan University, Chengdu, People's Republic of China
| | - Fumin Zhao
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Lei Zhang
- College of Computer Science, Sichuan University, Chengdu, People's Republic of China.
| | - Jianguo Xu
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
- West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China.
| |
Collapse
|
5
|
Hammer Y, Najjar W, Kahanov L, Joskowicz L, Shoshan Y. Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline. J Neurooncol 2024; 166:547-555. [PMID: 38300389 PMCID: PMC10876809 DOI: 10.1007/s11060-024-04580-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 01/18/2024] [Indexed: 02/02/2024]
Abstract
PURPOSE Close MRI surveillance of patients with brain metastases following Stereotactic Radiosurgery (SRS) treatment is essential for assessing treatment response and the current disease status in the brain. This follow-up necessitates the comparison of target lesion sizes in pre- (prior) and post-SRS treatment (current) T1W-Gad MRI scans. Our aim was to evaluate SimU-Net, a novel deep-learning model for the detection and volumetric analysis of brain metastases and their temporal changes in paired prior and current scans. METHODS SimU-Net is a simultaneous multi-channel 3D U-Net model trained on pairs of registered prior and current scans of a patient. We evaluated its performance on 271 pairs of T1W-Gad MRI scans from 226 patients who underwent SRS. An expert oncological neurosurgeon manually delineated 1,889 brain metastases in all the MRI scans (1,368 with diameters > 5 mm, 834 > 10 mm). The SimU-Net model was trained/validated on 205 pairs from 169 patients (1,360 metastases) and tested on 66 pairs from 57 patients (529 metastases). The results were then compared to the ground truth delineations. RESULTS SimU-Net yielded a mean (std) detection precision and recall of 1.00±0.00 and 0.99±0.06 for metastases > 10 mm, 0.90±0.22 and 0.97±0.12 for metastases > 5 mm of, and 0.76±0.27 and 0.94±0.16 for metastases of all sizes. It improves lesion detection precision by 8% for all metastases sizes and by 12.5% for metastases < 10 mm with respect to standalone 3D U-Net. The segmentation Dice scores were 0.90±0.10, 0.89±0.10 and 0.89±0.10 for the above metastases sizes, all above the observer variability of 0.80±0.13. CONCLUSION Automated detection and volumetric quantification of brain metastases following SRS have the potential to enhance the assessment of treatment response and alleviate the clinician workload.
Collapse
Affiliation(s)
- Yonny Hammer
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond. J. Safra Campus, Givat Ram, 9190401, Jerusalem, Israel
| | - Wenad Najjar
- Department of Neurosurgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Lea Kahanov
- Department of Neurosurgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond. J. Safra Campus, Givat Ram, 9190401, Jerusalem, Israel.
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Yigal Shoshan
- Department of Neurosurgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| |
Collapse
|
6
|
Wang TW, Hsu MS, Lee WK, Pan HC, Yang HC, Lee CC, Wu YT. Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis. Radiother Oncol 2024; 190:110007. [PMID: 37967585 DOI: 10.1016/j.radonc.2023.110007] [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: 07/20/2023] [Revised: 10/15/2023] [Accepted: 11/08/2023] [Indexed: 11/17/2023]
Abstract
BACKGROUND Manual detection of brain metastases is both laborious and inconsistent, driving the need for more efficient solutions. Accordingly, our systematic review and meta-analysis assessed the efficacy of deep learning algorithms in detecting and segmenting brain metastases from various primary origins in MRI images. METHODS We conducted a comprehensive search of PubMed, Embase, and Web of Science up to May 24, 2023, which yielded 42 relevant studies for our analysis. We assessed the quality of these studies using the QUADAS-2 and CLAIM tools. Using a random-effect model, we calculated the pooled lesion-wise dice score as well as patient-wise and lesion-wise sensitivity. We performed subgroup analyses to investigate the influence of factors such as publication year, study design, training center of the model, validation methods, slice thickness, model input dimensions, MRI sequences fed to the model, and the specific deep learning algorithms employed. Additionally, meta-regression analyses were carried out considering the number of patients in the studies, count of MRI manufacturers, count of MRI models, training sample size, and lesion number. RESULTS Our analysis highlighted that deep learning models, particularly the U-Net and its variants, demonstrated superior segmentation accuracy. Enhanced detection sensitivity was observed with an increased diversity in MRI hardware, both in terms of manufacturer and model variety. Furthermore, slice thickness was identified as a significant factor influencing lesion-wise detection sensitivity. Overall, the pooled results indicated a lesion-wise dice score of 79%, with patient-wise and lesion-wise sensitivities at 86% and 87%, respectively. CONCLUSIONS The study underscores the potential of deep learning in improving brain metastasis diagnostics and treatment planning. Still, more extensive cohorts and larger meta-analysis are needed for more practical and generalizable algorithms. Future research should prioritize these areas to advance the field. This study was funded by the Gen. & Mrs. M.C. Peng Fellowship and registered under PROSPERO (CRD42023427776).
Collapse
Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ming-Sheng Hsu
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei-Kai Lee
- Institute of Biophotonics, National Yang Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan
| | - Hung-Chuan Pan
- Department of Neurosurgery, Taichung Veterans General Hospital, Taichung, Taiwan; Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Huai-Che Yang
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Cheng-Chia Lee
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan; National Yang Ming Chiao Tung University, Brain Research Center, Taiwan; National Yang Ming Chiao Tung University, College Medical Device Innovation and Translation Center, Taiwan.
| |
Collapse
|
7
|
Prezelski K, Hsu DG, del Balzo L, Heller E, Ma J, Pike LRG, Ballangrud Å, Aristophanous M. Artificial-intelligence-driven measurements of brain metastases' response to SRS compare favorably with current manual standards of assessment. Neurooncol Adv 2024; 6:vdae015. [PMID: 38464949 PMCID: PMC10924534 DOI: 10.1093/noajnl/vdae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024] Open
Abstract
Background Evaluation of treatment response for brain metastases (BMs) following stereotactic radiosurgery (SRS) becomes complex as the number of treated BMs increases. This study uses artificial intelligence (AI) to track BMs after SRS and validates its output compared with manual measurements. Methods Patients with BMs who received at least one course of SRS and followed up with MRI scans were retrospectively identified. A tool for automated detection, segmentation, and tracking of intracranial metastases on longitudinal imaging, MEtastasis Tracking with Repeated Observations (METRO), was applied to the dataset. The longest three-dimensional (3D) diameter identified with METRO was compared with manual measurements of maximum axial BM diameter, and their correlation was analyzed. Change in size of the measured BM identified with METRO after SRS treatment was used to classify BMs as responding, or not responding, to treatment, and its accuracy was determined relative to manual measurements. Results From 71 patients, 176 BMs were identified and measured with METRO and manual methods. Based on a one-to-one correlation analysis, the correlation coefficient was R2 = 0.76 (P = .0001). Using modified BM response classifications of BM change in size, the longest 3D diameter data identified with METRO had a sensitivity of 0.72 and a specificity of 0.95 in identifying lesions that responded to SRS, when using manual axial diameter measurements as the ground truth. Conclusions Using AI to automatically measure and track BM volumes following SRS treatment, this study showed a strong correlation between AI-driven measurements and the current clinically used method: manual axial diameter measurements.
Collapse
Affiliation(s)
- Kayla Prezelski
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Saint Louis University School of Medicine, St. Louis, Missouri, USA
| | - Dylan G Hsu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Luke del Balzo
- Medical College of Georgia, Athens, Georgia, USA
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Erica Heller
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jennifer Ma
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Luke R G Pike
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Biomarker Development Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Åse Ballangrud
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Michalis Aristophanous
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| |
Collapse
|
8
|
Egashira M, Arimura H, Kobayashi K, Moriyama K, Kodama T, Tokuda T, Ninomiya K, Okamoto H, Igaki H. Magnetic resonance-based imaging biopsy with signatures including topological Betti number features for prediction of primary brain metastatic sites. Phys Eng Sci Med 2023; 46:1411-1426. [PMID: 37603131 DOI: 10.1007/s13246-023-01308-6] [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: 02/02/2023] [Accepted: 07/20/2023] [Indexed: 08/22/2023]
Abstract
This study incorporated topology Betti number (BN) features into the prediction of primary sites of brain metastases and the construction of magnetic resonance-based imaging biopsy (MRB) models. The significant features of the MRB model were selected from those obtained from gray-scale and three-dimensional wavelet-filtered images, BN and inverted BN (iBN) maps, and clinical variables (age and gender). The primary sites were predicted as either lung cancer or other cancers using MRB models, which were built using seven machine learning methods with significant features chosen by three feature selection methods followed by a combination strategy. Our study dealt with a dataset with relatively smaller brain metastases, which included effective diameters greater than 2 mm, with metastases ranging from 2 to 9 mm accounting for 17% of the dataset. The MRB models were trained by T1-weighted contrast-enhanced images of 494 metastases chosen from 247 patients and applied to 115 metastases from 62 test patients. The most feasible model attained an area under the receiver operating characteristic curve (AUC) of 0.763 for the test patients when using a signature including features of BN and iBN maps, gray-scale and wavelet-filtered images, and clinical variables. The AUCs of the model were 0.744 for non-small cell lung cancer and 0.861 for small cell lung cancer. The results suggest that the BN signature boosted the performance of MRB for the identification of primary sites of brain metastases including small tumors.
Collapse
Affiliation(s)
- Mai Egashira
- Division of Medical Quantum Science, Department of Health Science, Graduate School of Medical Science, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
| | - Kazuma Kobayashi
- Department of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Kazutoshi Moriyama
- Division of Medical Quantum Science, Department of Health Science, Graduate School of Medical Science, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Takumi Kodama
- Division of Medical Quantum Science, Department of Health Science, Graduate School of Medical Science, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Tomoki Tokuda
- Joint Graduate School of Mathematics for Innovation, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Kenta Ninomiya
- Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA, USA
| | - Hiroyuki Okamoto
- Radiation Safety and Quality Assurance Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Hiroshi Igaki
- Department of Radiation Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| |
Collapse
|
9
|
Yang C, Zhou Q, Li M, Xu L, Zeng Y, Liu J, Wei Y, Shi F, Chen J, Li P, Shu Y, Yang L, Shu J. MRI-based automatic identification and segmentation of extrahepatic cholangiocarcinoma using deep learning network. BMC Cancer 2023; 23:1089. [PMID: 37950207 PMCID: PMC10636947 DOI: 10.1186/s12885-023-11575-x] [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: 09/26/2023] [Accepted: 10/27/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Accurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challenging because of the small size and complex background structure. Therefore, considering the limitation of manual delineation, it's necessary to develop automated identification and segmentation methods for ECC. The aim of this study was to develop a deep learning approach for automatic identification and segmentation of ECC using MRI. METHODS We recruited 137 ECC patients from our hospital as the main dataset (C1) and an additional 40 patients from other hospitals as the external validation set (C2). All patients underwent axial T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Manual delineations were performed and served as the ground truth. Next, we used 3D VB-Net to establish single-mode automatic identification and segmentation models based on T1WI (model 1), T2WI (model 2), and DWI (model 3) in the training cohort (80% of C1), and compared them with the combined model (model 4). Subsequently, the generalization capability of the best models was evaluated using the testing set (20% of C1) and the external validation set (C2). Finally, the performance of the developed models was further evaluated. RESULTS Model 3 showed the best identification performance in the training, testing, and external validation cohorts with success rates of 0.980, 0.786, and 0.725, respectively. Furthermore, model 3 yielded an average Dice similarity coefficient (DSC) of 0.922, 0.495, and 0.466 to segment ECC automatically in the training, testing, and external validation cohorts, respectively. CONCLUSION The DWI-based model performed better in automatically identifying and segmenting ECC compared to T1WI and T2WI, which may guide clinical decisions and help determine prognosis.
Collapse
Affiliation(s)
- Chunmei Yang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Qin Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Mingdong Li
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Lulu Xu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Yanyan Zeng
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Jiong Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jing Chen
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Pinxiong Li
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Yue Shu
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Lu Yang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Jian Shu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China.
| |
Collapse
|
10
|
Zhou Z, Wang M, Zhao R, Shao Y, Xing L, Qiu Q, Yin Y. A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis. J Transl Med 2023; 21:788. [PMID: 37936137 PMCID: PMC10629110 DOI: 10.1186/s12967-023-04681-8] [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: 07/27/2023] [Accepted: 10/29/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND The precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial goals in computer-aided lung adenocarcinoma brain metastasis diagnosis. However, these two tasks present continuous difficulties due to the nonuniform intensity distributions, ambiguous boundaries, and variable shapes of brain metastasis (BM) in MR images.The existing approaches for tackling these challenges mainly rely on single-task algorithms, which overlook the interdependence between these two tasks. METHODS To comprehensively address these challenges, we propose a multi-task deep learning model that simultaneously enables GTV segmentation and EGFR subtype classification. Specifically, a multi-scale self-attention encoder that consists of a convolutional self-attention module is designed to extract the shared spatial and global information for a GTV segmentation decoder and an EGFR genotype classifier. Then, a hybrid CNN-Transformer classifier consisting of a convolutional block and a Transformer block is designed to combine the global and local information. Furthermore, the task correlation and heterogeneity issues are solved with a multi-task loss function, aiming to balance the above two tasks by incorporating segmentation and classification loss functions with learnable weights. RESULTS The experimental results demonstrate that our proposed model achieves excellent performance, surpassing that of single-task learning approaches. Our proposed model achieves a mean Dice score of 0.89 for GTV segmentation and an EGFR genotyping accuracy of 0.88 on an internal testing set, and attains an accuracy of 0.81 in the EGFR genotype prediction task and an average Dice score of 0.85 in the GTV segmentation task on the external testing set. This shows that our proposed method has outstanding performance and generalization. CONCLUSION With the introduction of an efficient feature extraction module, a hybrid CNN-Transformer classifier, and a multi-task loss function, the proposed multi-task deep learning network significantly enhances the performance achieved in both GTV segmentation and EGFR genotyping tasks. Thus, the model can serve as a noninvasive tool for facilitating clinical treatment.
Collapse
Affiliation(s)
- Zichun Zhou
- Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, 250117, Shandong, China
| | - Min Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, 250117, Shandong, China
| | - Rubin Zhao
- Department of Radiation Oncology and Technology, Linyi People's Hospital, 27 Jiefang Road, Linyi, 276003, Shandong, China
| | - Yan Shao
- Shanghai Chest Hospital, School of Medicine, Shanghai Jiaotong University, 241 Huaihai West Road, Shanghai, 200030, China
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, 250117, Shandong, China
| | - Qingtao Qiu
- Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, 250117, Shandong, China.
| | - Yong Yin
- Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, 250117, Shandong, China.
| |
Collapse
|
11
|
Heyn C, Moody AR, Tseng CL, Wong E, Kang T, Kapadia A, Howard P, Maralani P, Symons S, Goubran M, Martel A, Chen H, Myrehaug S, Detsky J, Sahgal A, Soliman H. Segmentation of Brain Metastases Using Background Layer Statistics (BLAST). AJNR Am J Neuroradiol 2023; 44:1135-1143. [PMID: 37735088 PMCID: PMC10549939 DOI: 10.3174/ajnr.a7998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/16/2023] [Indexed: 09/23/2023]
Abstract
BACKGROUND AND PURPOSE Accurate segmentation of brain metastases is important for treatment planning and evaluating response. The aim of this study was to assess the performance of a semiautomated algorithm for brain metastases segmentation using Background Layer Statistics (BLAST). MATERIALS AND METHODS Nineteen patients with 48 parenchymal and dural brain metastases were included. Segmentation was performed by 4 neuroradiologists and 1 radiation oncologist. K-means clustering was used to identify normal gray and white matter (background layer) in a 2D parameter space of signal intensities from postcontrast T2 FLAIR and T1 MPRAGE sequences. The background layer was subtracted and operator-defined thresholds were applied in parameter space to segment brain metastases. The remaining voxels were back-projected to visualize segmentations in image space and evaluated by the operators. Segmentation performance was measured by calculating the Dice-Sørensen coefficient and Hausdorff distance using ground truth segmentations made by the investigators. Contours derived from the segmentations were evaluated for clinical acceptance using a 5-point Likert scale. RESULTS The median Dice-Sørensen coefficient was 0.82 for all brain metastases and 0.9 for brain metastases of ≥10 mm. The median Hausdorff distance was 1.4 mm. Excellent interreader agreement for brain metastases volumes was found with an intraclass correlation coefficient = 0.9978. The median segmentation time was 2.8 minutes/metastasis. Forty-five contours (94%) had a Likert score of 4 or 5, indicating that the contours were acceptable for treatment, requiring no changes or minor edits. CONCLUSIONS We show accurate and reproducible segmentation of brain metastases using BLAST and demonstrate its potential as a tool for radiation planning and evaluating treatment response.
Collapse
Affiliation(s)
- Chris Heyn
- From the Department of Medical Imaging (C.H., A.R.M., E.W., T.K., A.K., P.H., P.M., S.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
- Sunnybrook Research Institute (C.H., A.R.M., M.G., A.M.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Alan R Moody
- From the Department of Medical Imaging (C.H., A.R.M., E.W., T.K., A.K., P.H., P.M., S.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
- Sunnybrook Research Institute (C.H., A.R.M., M.G., A.M.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology (C.-L.T., H.C., S.M., J.D., A.S., H.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Erin Wong
- From the Department of Medical Imaging (C.H., A.R.M., E.W., T.K., A.K., P.H., P.M., S.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Tony Kang
- From the Department of Medical Imaging (C.H., A.R.M., E.W., T.K., A.K., P.H., P.M., S.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Anish Kapadia
- From the Department of Medical Imaging (C.H., A.R.M., E.W., T.K., A.K., P.H., P.M., S.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Peter Howard
- From the Department of Medical Imaging (C.H., A.R.M., E.W., T.K., A.K., P.H., P.M., S.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Pejman Maralani
- From the Department of Medical Imaging (C.H., A.R.M., E.W., T.K., A.K., P.H., P.M., S.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Sean Symons
- From the Department of Medical Imaging (C.H., A.R.M., E.W., T.K., A.K., P.H., P.M., S.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Maged Goubran
- Sunnybrook Research Institute (C.H., A.R.M., M.G., A.M.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
- Department of Medical Biophysics (M.G., A.M.), University of Toronto, Toronto, Ontario, Canada
| | - Anne Martel
- Sunnybrook Research Institute (C.H., A.R.M., M.G., A.M.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
- Department of Medical Biophysics (M.G., A.M.), University of Toronto, Toronto, Ontario, Canada
| | - Hanbo Chen
- Department of Radiation Oncology (C.-L.T., H.C., S.M., J.D., A.S., H.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology (C.-L.T., H.C., S.M., J.D., A.S., H.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Jay Detsky
- Department of Radiation Oncology (C.-L.T., H.C., S.M., J.D., A.S., H.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology (C.-L.T., H.C., S.M., J.D., A.S., H.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Hany Soliman
- Department of Radiation Oncology (C.-L.T., H.C., S.M., J.D., A.S., H.S.), Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| |
Collapse
|
12
|
Yu L, Yu Z, Sun L, Zhu L, Geng D. A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy. Front Med (Lausanne) 2023; 10:1232496. [PMID: 37841015 PMCID: PMC10576559 DOI: 10.3389/fmed.2023.1232496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 09/08/2023] [Indexed: 10/17/2023] Open
Abstract
Objectives Gliomas and brain metastases (Mets) are the most common brain malignancies. The treatment strategy and clinical prognosis of patients are different, requiring accurate diagnosis of tumor types. However, the traditional radiomics diagnostic pipeline requires manual annotation and lacks integrated methods for segmentation and classification. To improve the diagnosis process, a gliomas and Mets computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy on multi-center datasets was proposed. Methods Overall, 1,022 high-grade gliomas and 775 Mets patients' preoperative MR images were adopted in the study, including contrast-enhanced T1-weighted (T1-CE) and T2-fluid attenuated inversion recovery (T2-flair) sequences from three hospitals. Two segmentation models trained on the gliomas and Mets datasets, respectively, were used to automatically segment tumors. Multiple radiomics features were extracted after automatic segmentation. Several machine learning classifiers were used to measure the impact of feature selection methods. A weight soft voting (RSV) model and ensemble decision strategy based on prior knowledge (EDPK) were introduced in the radiomics pipeline. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the classification performance. Results The proposed pipeline improved the diagnosis of gliomas and Mets with ACC reaching 0.8950 and AUC reaching 0.9585 after automatic lesion segmentation, which was higher than those of the traditional radiomics pipeline (ACC:0.8850, AUC:0.9450). Conclusion The proposed model accurately classified gliomas and Mets patients using MRI radiomics. The novel pipeline showed great potential in diagnosing gliomas and Mets with high generalizability and interpretability.
Collapse
Affiliation(s)
- Liheng Yu
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Huashan Hospital, Fudan University, Shanghai, China
- Greater BayArea Institute of Precision Medicine (Guangzhou), Fudan University, Nansha District, Guangzhou, Guangdong, China
| | - Zekuan Yu
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Huashan Hospital, Fudan University, Shanghai, China
- Greater BayArea Institute of Precision Medicine (Guangzhou), Fudan University, Nansha District, Guangzhou, Guangdong, China
| | - Linlin Sun
- Department of Radiology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Li Zhu
- Department of Radiology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Daoying Geng
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Huashan Hospital, Fudan University, Shanghai, China
- Greater BayArea Institute of Precision Medicine (Guangzhou), Fudan University, Nansha District, Guangzhou, Guangdong, China
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| |
Collapse
|
13
|
Hsu DG, Ballangrud Å, Prezelski K, Swinburne NC, Young R, Beal K, Deasy JO, Cerviño L, Aristophanous M. Automatically tracking brain metastases after stereotactic radiosurgery. Phys Imaging Radiat Oncol 2023; 27:100452. [PMID: 37720463 PMCID: PMC10500025 DOI: 10.1016/j.phro.2023.100452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/12/2023] [Accepted: 05/26/2023] [Indexed: 09/19/2023] Open
Abstract
Background and purpose Patients with brain metastases (BMs) are surviving longer and returning for multiple courses of stereotactic radiosurgery. BMs are monitored after radiation with follow-up magnetic resonance (MR) imaging every 2-3 months. This study investigated whether it is possible to automatically track BMs on longitudinal imaging and quantify the tumor response after radiotherapy. Methods The METRO process (MEtastasis Tracking with Repeated Observations was developed to automatically process patient data and track BMs. A longitudinal intrapatient registration method for T1 MR post-Gd was conceived and validated on 20 patients. Detections and volumetric measurements of BMs were obtained from a deep learning model. BM tracking was validated on 32 separate patients by comparing results with manual measurements of BM response and radiologists' assessments of new BMs. Linear regression and residual analysis were used to assess accuracy in determining tumor response and size change. Results A total of 123 irradiated BMs and 38 new BMs were successfully tracked. 66 irradiated BMs were visible on follow-up imaging 3-9 months after radiotherapy. Comparing their longest diameter changes measured manually vs. METRO, the Pearson correlation coefficient was 0.88 (p < 0.001); the mean residual error was -8 ± 17%. The mean registration error was 1.5 ± 0.2 mm. Conclusions Automatic, longitudinal tracking of BMs using deep learning methods is feasible. In particular, the software system METRO fulfills a need to automatically track and quantify volumetric changes of BMs prior to, and in response to, radiation therapy.
Collapse
Affiliation(s)
- Dylan G. Hsu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Åse Ballangrud
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Kayla Prezelski
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Nathaniel C. Swinburne
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Robert Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Kathryn Beal
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY 10065, United States
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Laura Cerviño
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Michalis Aristophanous
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| |
Collapse
|
14
|
Chen Y, Yue H, Kuang H, Wang J. RBS-Net: Hippocampus segmentation using multi-layer feature learning with the region, boundary and structure loss. Comput Biol Med 2023; 160:106953. [PMID: 37120987 DOI: 10.1016/j.compbiomed.2023.106953] [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: 10/19/2022] [Revised: 04/10/2023] [Accepted: 04/15/2023] [Indexed: 05/02/2023]
Abstract
Hippocampus has great influence over the Alzheimer's disease (AD) research because of its essential role as a biomarker in the human brain. Thus the performance of hippocampus segmentation influences the development of clinical research for brain disorders. Deep learning using U-net-like networks becomes prevalent in hippocampus segmentation on Magnetic Resonance Imaging (MRI) due to its efficiency and accuracy. However, current methods lose sufficient detailed information during pooling, which hinders the segmentation results. And weak supervision on the details like edges or positions results in fuzzy and coarse boundary segmentation, causing great differences between the segmentation and ground-truth. In view of these drawbacks, we propose a Region-Boundary and Structure Net (RBS-Net), which consists of a primary net and an auxiliary net. (1) Our primary net focuses on the region distribution of hippocampus and introduces a distance map for boundary supervision. Furthermore the primary net adds a multi-layer feature learning module to compensate the information loss during pooling and strengthen the differences between the foreground and background, improving the region and boundary segmentation. (2) The auxiliary net concentrates on the structure similarity and also utilizes the multi-layer feature learning module, and this parallel task can refine encoders by similarizing the structure of the segmentation and ground-truth. We train and test our network using 5-fold cross-validation on HarP, a public available hippocampus dataset. Experimental results demonstrate that our proposed RBS-Net achieves a Dice of 89.76% in average, outperforming several state-of-the-art hippocampus segmentation methods. Furthermore, in few shot circumstances, our proposed RBS-Net achieves better results in terms of a comprehensive evaluation compared to several state-of-the-art deep learning-based methods. Finally we can observe that visual segmentation results for the boundary and detailed regions are improved by our proposed RBS-Net.
Collapse
Affiliation(s)
- Yu Chen
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China
| | - Hailin Yue
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China
| | - Hulin Kuang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.
| |
Collapse
|
15
|
Stember JN, Young RJ, Shalu H. Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution. J Digit Imaging 2023; 36:536-546. [PMID: 36396839 PMCID: PMC10039135 DOI: 10.1007/s10278-022-00725-5] [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: 04/06/2022] [Revised: 09/29/2022] [Accepted: 10/25/2022] [Indexed: 11/18/2022] Open
Abstract
Cancer centers have an urgent and unmet clinical and research need for AI that can guide patient management. A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example, as per RECIST or RANO criteria, is tedious and time-consuming, and can miss important tumor response information. Most notably, the prevalent response criteria often exclude lesions, the non-target lesions, altogether. We wish to assess change in a holistic fashion that includes all lesions, obtaining simple, informative, and automated assessments of tumor progression or regression. Because genetic sub-types of cancer can be fairly specific and patient enrollment in therapy trials is often limited in number and accrual rate, we wish to make response assessments with small training sets. Deep neuroevolution (DNE) is a novel radiology artificial intelligence (AI) optimization approach that performs well on small training sets. Here, we use a DNE parameter search to optimize a convolutional neural network (CNN) that predicts progression versus regression of metastatic brain disease. We analyzed 50 pairs of MRI contrast-enhanced images as our training set. Half of these pairs, separated in time, qualified as disease progression, while the other 25 image pairs constituted regression. We trained the parameters of a CNN via "mutations" that consisted of random CNN weight adjustments and evaluated mutation "fitness" as summed training set accuracy. We then incorporated the best mutations into the next generation's CNN, repeating this process for approximately 50,000 generations. We applied the CNNs to our training set, as well as a separate testing set with the same class balance of 25 progression and 25 regression cases. DNE achieved monotonic convergence to 100% training set accuracy. DNE also converged monotonically to 100% testing set accuracy. We have thus shown that DNE can accurately classify brain metastatic disease progression versus regression. Future work will extend the input from 2D image slices to full 3D volumes, and include the category of "no change." We believe that an approach such as ours can ultimately provide a useful and informative complement to RANO/RECIST assessment and volumetric AI analysis.
Collapse
Affiliation(s)
- Joseph N Stember
- Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY, 10065, USA.
| | - Robert J Young
- Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY, 10065, USA
| | - Hrithwik Shalu
- Indian Institute of Technology Madras, Madras, Chennai, 600036, India
| |
Collapse
|
16
|
Eidex Z, Ding Y, Wang J, Abouei E, Qiu RL, Liu T, Wang T, Yang X. Deep Learning in MRI-guided Radiation Therapy: A Systematic Review. ARXIV 2023:arXiv:2303.11378v2. [PMID: 36994167 PMCID: PMC10055493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed with emphasis placed on underlying methods. Studies are further categorized into the areas of segmentation, synthesis, radiomics, and real time MRI. Finally, clinical implications, current challenges, and future directions are discussed.
Collapse
Affiliation(s)
- Zach Eidex
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
- School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Yifu Ding
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Jing Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Elham Abouei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Richard L.J. Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Tian Liu
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Tonghe Wang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
- School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
| |
Collapse
|
17
|
Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction. J Neurooncol 2023; 161:441-450. [PMID: 36635582 DOI: 10.1007/s11060-022-04234-x] [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: 12/09/2022] [Accepted: 12/30/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Rapid evolution of artificial intelligence (AI) prompted its wide application in healthcare systems. Stereotactic radiosurgery served as a good candidate for AI model development and achieved encouraging result in recent years. This article aimed at demonstrating current AI application in radiosurgery. METHODS Literatures published in PubMed during 2010-2022, discussing AI application in stereotactic radiosurgery were reviewed. RESULTS AI algorithms, especially machine learning/deep learning models, have been administered to different aspect of stereotactic radiosurgery. Spontaneous tumor detection and automated lesion delineation or segmentation were two of the promising application, which could be further extended to longitudinal treatment follow-up. Outcome prediction utilized machine learning algorithms with radiomic-based analysis was another well-established application. CONCLUSIONS Stereotactic radiosurgery has taken a lead role in AI development. Current achievement, limitation, and further investigation was summarized in this article.
Collapse
|
18
|
Mackay K, Bernstein D, Glocker B, Kamnitsas K, Taylor A. A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy. Clin Oncol (R Coll Radiol) 2023; 35:354-369. [PMID: 36803407 DOI: 10.1016/j.clon.2023.01.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 12/05/2022] [Accepted: 01/23/2023] [Indexed: 02/01/2023]
Abstract
Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of consensus on how to assess and validate auto-contouring systems currently limits clinical use. This review formally quantifies the assessment metrics used in studies published during one calendar year and assesses the need for standardised practice. A PubMed literature search was undertaken for papers evaluating radiotherapy auto-contouring published during 2021. Papers were assessed for types of metric and the methodology used to generate ground-truth comparators. Our PubMed search identified 212 studies, of which 117 met the criteria for clinical review. Geometric assessment metrics were used in 116 of 117 studies (99.1%). This includes the Dice Similarity Coefficient used in 113 (96.6%) studies. Clinically relevant metrics, such as qualitative, dosimetric and time-saving metrics, were less frequently used in 22 (18.8%), 27 (23.1%) and 18 (15.4%) of 117 studies, respectively. There was heterogeneity within each category of metric. Over 90 different names for geometric measures were used. Methods for qualitative assessment were different in all but two papers. Variation existed in the methods used to generate radiotherapy plans for dosimetric assessment. Consideration of editing time was only given in 11 (9.4%) papers. A single manual contour as a ground-truth comparator was used in 65 (55.6%) studies. Only 31 (26.5%) studies compared auto-contours to usual inter- and/or intra-observer variation. In conclusion, significant variation exists in how research papers currently assess the accuracy of automatically generated contours. Geometric measures are the most popular, however their clinical utility is unknown. There is heterogeneity in the methods used to perform clinical assessment. Considering the different stages of system implementation may provide a framework to decide the most appropriate metrics. This analysis supports the need for a consensus on the clinical implementation of auto-contouring.
Collapse
Affiliation(s)
- K Mackay
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK.
| | - D Bernstein
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
| | - B Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - K Kamnitsas
- Department of Computing, Imperial College London, South Kensington Campus, London, UK; Department of Engineering Science, University of Oxford, Oxford, UK
| | - A Taylor
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
| |
Collapse
|
19
|
Ottesen JA, Yi D, Tong E, Iv M, Latysheva A, Saxhaug C, Jacobsen KD, Helland Å, Emblem KE, Rubin DL, Bjørnerud A, Zaharchuk G, Grøvik E. 2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data. Front Neuroinform 2023; 16:1056068. [PMID: 36743439 PMCID: PMC9889663 DOI: 10.3389/fninf.2022.1056068] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 12/26/2022] [Indexed: 01/20/2023] Open
Abstract
Introduction Management of patients with brain metastases is often based on manual lesion detection and segmentation by an expert reader. This is a time- and labor-intensive process, and to that end, this work proposes an end-to-end deep learning segmentation network for a varying number of available MRI available sequences. Methods We adapt and evaluate a 2.5D and a 3D convolution neural network trained and tested on a retrospective multinational study from two independent centers, in addition, nnU-Net was adapted as a comparative benchmark. Segmentation and detection performance was evaluated by: (1) the dice similarity coefficient, (2) a per-metastases and the average detection sensitivity, and (3) the number of false positives. Results The 2.5D and 3D models achieved similar results, albeit the 2.5D model had better detection rate, whereas the 3D model had fewer false positive predictions, and nnU-Net had fewest false positives, but with the lowest detection rate. On MRI data from center 1, the 2.5D, 3D, and nnU-Net detected 79%, 71%, and 65% of all metastases; had an average per patient sensitivity of 0.88, 0.84, and 0.76; and had on average 6.2, 3.2, and 1.7 false positive predictions per patient, respectively. For center 2, the 2.5D, 3D, and nnU-Net detected 88%, 86%, and 78% of all metastases; had an average per patient sensitivity of 0.92, 0.91, and 0.85; and had on average 1.0, 0.4, and 0.1 false positive predictions per patient, respectively. Discussion/Conclusion Our results show that deep learning can yield highly accurate segmentations of brain metastases with few false positives in multinational data, but the accuracy degrades for metastases with an area smaller than 0.4 cm2.
Collapse
Affiliation(s)
- Jon André Ottesen
- CRAI, Division of Radiology and Nuclear Medicine, Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway,Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway,*Correspondence: Jon André Ottesen ✉
| | - Darvin Yi
- Department of Ophthalmology, University of Illinois, Chicago, IL, United States
| | - Elizabeth Tong
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Michael Iv
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Anna Latysheva
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Cathrine Saxhaug
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Åslaug Helland
- Department of Oncology, Oslo University Hospital, Oslo, Norway
| | - Kyrre Eeg Emblem
- Division of Radiology and Nuclear Medicine, Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Daniel L. Rubin
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Atle Bjørnerud
- CRAI, Division of Radiology and Nuclear Medicine, Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway,Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Endre Grøvik
- Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway,Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
20
|
Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023; 15:cancers15020334. [PMID: 36672286 PMCID: PMC9857123 DOI: 10.3390/cancers15020334] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 12/31/2022] [Accepted: 12/31/2022] [Indexed: 01/06/2023] Open
Abstract
Since manual detection of brain metastases (BMs) is time consuming, studies have been conducted to automate this process using deep learning. The purpose of this study was to conduct a systematic review and meta-analysis of the performance of deep learning models that use magnetic resonance imaging (MRI) to detect BMs in cancer patients. A systematic search of MEDLINE, EMBASE, and Web of Science was conducted until 30 September 2022. Inclusion criteria were: patients with BMs; deep learning using MRI images was applied to detect the BMs; sufficient data were present in terms of detective performance; original research articles. Exclusion criteria were: reviews, letters, guidelines, editorials, or errata; case reports or series with less than 20 patients; studies with overlapping cohorts; insufficient data in terms of detective performance; machine learning was used to detect BMs; articles not written in English. Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Finally, 24 eligible studies were identified for the quantitative analysis. The pooled proportion of patient-wise and lesion-wise detectability was 89%. Articles should adhere to the checklists more strictly. Deep learning algorithms effectively detect BMs. Pooled analysis of false positive rates could not be estimated due to reporting differences.
Collapse
|
21
|
Chartrand G, Emiliani RD, Pawlowski SA, Markel DA, Bahig H, Cengarle-Samak A, Rajakesari S, Lavoie J, Ducharme S, Roberge D. Automated Detection of Brain Metastases on T1-Weighted MRI Using a Convolutional Neural Network: Impact of Volume Aware Loss and Sampling Strategy. J Magn Reson Imaging 2022; 56:1885-1898. [PMID: 35624544 DOI: 10.1002/jmri.28274] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/13/2022] [Accepted: 05/13/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Detection of brain metastases (BM) and segmentation for treatment planning could be optimized with machine learning methods. Convolutional neural networks (CNNs) are promising, but their trade-offs between sensitivity and precision frequently lead to missing small lesions. HYPOTHESIS Combining volume aware (VA) loss function and sampling strategy could improve BM detection sensitivity. STUDY TYPE Retrospective. POPULATION A total of 530 radiation oncology patients (55% women) were split into a training/validation set (433 patients/1460 BM) and an independent test set (97 patients/296 BM). FIELD STRENGTH/SEQUENCE 1.5 T and 3 T, contrast-enhanced three-dimensional (3D) T1-weighted fast gradient echo sequences. ASSESSMENT Ground truth masks were based on radiotherapy treatment planning contours reviewed by experts. A U-Net inspired model was trained. Three loss functions (Dice, Dice + boundary, and VA) and two sampling methods (label and VA) were compared. Results were reported with Dice scores, volumetric error, lesion detection sensitivity, and precision. A detected voxel within the ground truth constituted a true positive. STATISTICAL TESTS McNemar's exact test to compare detected lesions between models. Pearson's correlation coefficient and Bland-Altman analysis to compare volume agreement between predicted and ground truth volumes. Statistical significance was set at P ≤ 0.05. RESULTS Combining VA loss and VA sampling performed best with an overall sensitivity of 91% and precision of 81%. For BM in the 2.5-6 mm estimated sphere diameter range, VA loss reduced false negatives by 58% and VA sampling reduced it further by 30%. In the same range, the boundary loss achieved the highest precision at 81%, but a low sensitivity (24%) and a 31% Dice loss. DATA CONCLUSION Considering BM size in the loss and sampling function of CNN may increase the detection sensitivity regarding small BM. Our pipeline relying on a single contrast-enhanced T1-weighted MRI sequence could reach a detection sensitivity of 91%, with an average of only 0.66 false positives per scan. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
| | | | | | - Daniel A Markel
- Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| | - Houda Bahig
- Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| | | | - Selvan Rajakesari
- Department of Radiation Oncology, Hopital Charles Lemoyne, Greenfield Park, Québec, Canada
| | | | - Simon Ducharme
- AFX Medical Inc., Montréal, Canada.,Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montréal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Canada
| | - David Roberge
- Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| |
Collapse
|