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Liu Y, Zhao L, Bao J, Hou J, Jing Z, Liu S, Li X, Cao Z, Yang B, Shen J, Zhang J, Ji L, Kang Z, Hu C, Wang L, Liu J. Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics. Vis Comput Ind Biomed Art 2024; 7:16. [PMID: 38967824 PMCID: PMC11226574 DOI: 10.1186/s42492-024-00167-6] [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: 02/06/2024] [Accepted: 05/20/2024] [Indexed: 07/06/2024] Open
Abstract
Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.
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Affiliation(s)
- Yuwei Liu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Litao Zhao
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Jie Bao
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, China
| | - Jian Hou
- Department of CT-MR Center, the People's Hospital of Jimo, Qingdao, 266200, Shandong Province, China
| | - Zhaozhao Jing
- Department of Radiology, Sinopharm Tongmei General Hospital, Datong, 037003, Shanxi Province, China
| | - Songlu Liu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Xuanhao Li
- Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Zibing Cao
- Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Boyu Yang
- Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Junkang Shen
- Department of Radiology, the Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu Province, China
| | - Ji Zhang
- Department of Radiology, the People's Hospital of Taizhou, Taizhou, 225399, Jiangsu Province, China
| | - Libiao Ji
- Department of Radiology, Changshu No. 1 People's Hospital, Changshu, 215501, Jiangsu Province, China
| | - Zhen Kang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei Province, China
| | - Chunhong Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, China.
| | - Liang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
| | - Jiangang Liu
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, Beijing, 100191, China.
- Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing, 100191, China.
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Mendes B, Domingues I, Santos J. Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions. J Clin Med 2024; 13:3907. [PMID: 38999473 PMCID: PMC11242211 DOI: 10.3390/jcm13133907] [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: 05/26/2024] [Revised: 06/27/2024] [Accepted: 07/01/2024] [Indexed: 07/14/2024] Open
Abstract
Background: Prostate Cancer (PCa) is asymptomatic at an early stage and often painless, requiring only active surveillance. External Beam Radiotherapy (EBRT) is currently a curative option for localised and locally advanced diseases and a palliative option for metastatic low-volume disease. Although highly effective, especially in a hypofractionation scheme, 17.4% to 39.4% of all patients suffer from cancer recurrence after EBRT. But, radiographic findings also correlate with significant differences in protein expression patterns. In the PCa EBRT workflow, several imaging modalities are available for grading, staging and contouring. Using image data characterisation algorithms (radiomics), one can provide a quantitative analysis of prognostic and predictive treatment outcomes. Methods: This literature review searched for original studies in radiomics for PCa in the context of EBRT. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review includes 73 new studies and analyses datasets, imaging modality, segmentation technique, feature extraction, selection and model building methods. Results: Magnetic Resonance Imaging (MRI) is the preferred imaging modality for radiomic studies in PCa but Computed Tomography (CT), Positron Emission Tomography (PET) and Ultrasound (US) may offer valuable insights on tumour characterisation and treatment response prediction. Conclusions: Most radiomic studies used small, homogeneous and private datasets lacking external validation and variability. Future research should focus on collaborative efforts to create large, multicentric datasets and develop standardised methodologies, ensuring the full potential of radiomics in clinical practice.
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Affiliation(s)
- Bruno Mendes
- Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), Medical Physics, Radiobiology and Radiological Protection Group, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Faculty of Engineering of the University of Porto (FEUP), R. Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Inês Domingues
- Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), Medical Physics, Radiobiology and Radiological Protection Group, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal
| | - João Santos
- Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), Medical Physics, Radiobiology and Radiological Protection Group, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- School of Medicine and Biomedical Sciences (ICBAS), R. Jorge de Viterbo Ferreira 228, 4050-313 Porto, Portugal
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Tan R, Sui C, Wang C, Zhu T. MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study. Front Oncol 2024; 14:1401977. [PMID: 38803534 PMCID: PMC11128562 DOI: 10.3389/fonc.2024.1401977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
Background Accurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade. Methods A total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice. Results The intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort. Conclusion This study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.
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Affiliation(s)
- Rui Tan
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Chunxiao Sui
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Chao Wang
- Department of Neurosurgery, Qilu Hospital of Shandong University Dezhou Hospital (Dezhou People’s Hospital), Shandong, China
| | - Tao Zhu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
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Hiremath A, Corredor G, Li L, Leo P, Magi-Galluzzi C, Elliott R, Purysko A, Shiradkar R, Madabhushi A. An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings. Heliyon 2024; 10:e29602. [PMID: 38665576 PMCID: PMC11044050 DOI: 10.1016/j.heliyon.2024.e29602] [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: 11/07/2023] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Objectives To evaluate the added benefit of integrating features from pre-treatment MRI (radiomics) and digitized post-surgical pathology slides (pathomics) in prostate cancer (PCa) patients for prognosticating outcomes post radical-prostatectomy (RP) including a) rising prostate specific antigen (PSA), and b) extraprostatic-extension (EPE). Methods Multi-institutional data (N = 58) of PCa patients who underwent pre-treatment 3-T MRI prior to RP were included in this retrospective study. Radiomic and pathomic features were extracted from PCa regions on MRI and RP specimens delineated by expert clinicians. On training set (D1, N = 44), Cox Proportional-Hazards models MR, MP and MRaP were trained using radiomics, pathomics, and their combination, respectively, to prognosticate rising PSA (PSA > 0.03 ng/mL). Top features from MRaP were used to train a model to predict EPE on D1 and test on external dataset (D2, N = 14). C-index, Kalplan-Meier curves were used for survival analysis, and area under ROC (AUC) was used for EPE. MRaP was compared with the existing post-treatment risk-calculator, CAPRA (MC). Results Patients had median follow-up of 34 months. MRaP (c-index = 0.685 ± 0.05) significantly outperformed MR (c-index = 0.646 ± 0.05), MP (c-index = 0.631 ± 0.06) and MC (c-index = 0.601 ± 0.071) (p < 0.0001). Cross-validated Kaplan-Meier curves showed significant separation among risk groups for rising PSA for MRaP (p < 0.005, Hazard Ratio (HR) = 11.36) as compared to MR (p = 0.64, HR = 1.33), MP (p = 0.19, HR = 2.82) and MC (p = 0.10, HR = 3.05). Integrated radio-pathomic model MRaP (AUC = 0.80) outperformed MR (AUC = 0.57) and MP (AUC = 0.76) in predicting EPE on external-data (D2). Conclusions Results from this preliminary study suggest that a combination of radiomic and pathomic features can better predict post-surgical outcomes (rising PSA and EPE) compared to either of them individually as well as extant prognostic nomogram (CAPRA).
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Affiliation(s)
| | - Germán Corredor
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Lin Li
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Patrick Leo
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | | | - Robin Elliott
- Department of Pathology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Andrei Purysko
- Department of Radiology and Nuclear Medicine, Cleveland Clinic, Cleveland, OH, USA
| | - Rakesh Shiradkar
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Atlanta Veterans Administration Medical Center, Atlanta, GA, USA
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Huang D, Lin C, Jiang Y, Xin E, Xu F, Gan Y, Xu R, Wang F, Zhang H, Lou K, Shi L, Hu H. Radiomics model based on intratumoral and peritumoral features for predicting major pathological response in non-small cell lung cancer receiving neoadjuvant immunochemotherapy. Front Oncol 2024; 14:1348678. [PMID: 38585004 PMCID: PMC10996281 DOI: 10.3389/fonc.2024.1348678] [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: 12/03/2023] [Accepted: 03/06/2024] [Indexed: 04/09/2024] Open
Abstract
Objective To establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant immunochemotherapy. Methods A total of 148 NSCLC patients who underwent neoadjuvant immunochemotherapy from two centers (SRRSH and ZCH) were retrospectively included. The SRRSH dataset (n=105) was used as the training and internal validation cohort. Radiomics features of intratumoral (T) and peritumoral regions (P1 = 0-5mm, P2 = 5-10mm, and P3 = 10-15mm) were extracted from pre-treatment CT. Intra- and inter- class correlation coefficients and least absolute shrinkage and selection operator were used to feature selection. Four single ROI models mentioned above and a combined radiomics (CR: T+P1+P2+P3) model were established by using machine learning algorithms. Clinical factors were selected to construct the combined radiomics-clinical (CRC) model, which was validated in the external center ZCH (n=43). The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis. Results Histopathological type was the only independent clinical risk factor. The model CR with eight selected radiomics features demonstrated a good predictive performance in the internal validation (AUC=0.810) and significantly improved than the model T (AUC=0.810 vs 0.619, p<0.05). The model CRC yielded the best predictive capability (AUC=0.814) and obtained satisfactory performance in the independent external test set (AUC=0.768, 95% CI: 0.62-0.91). Conclusion We established a CRC model that incorporates intratumoral and peritumoral features and histopathological type, providing an effective approach for selecting NSCLC patients suitable for neoadjuvant immunochemotherapy.
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Affiliation(s)
- Dingpin Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chen Lin
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Yangyang Jiang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Enhui Xin
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yi Gan
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Rui Xu
- DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China
- DUT-RU Co-Research Center of Advanced Information Computing Technology (ICT) for Active Life, Dalian University of Technology, Dalian, Liaoning, China
| | - Fang Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haiping Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Kaihua Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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Zeevi T, Leapman MS, Sprenkle PC, Venkataraman R, Staib LH, Onofrey JA. Reliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy Histopathology. IEEE Trans Biomed Eng 2024; 71:1084-1091. [PMID: 37874731 PMCID: PMC10901528 DOI: 10.1109/tbme.2023.3326799] [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] [Indexed: 10/26/2023]
Abstract
OBJECTIVE To compute a dense prostate cancer risk map for the individual patient post-biopsy from magnetic resonance imaging (MRI) and to provide a more reliable evaluation of its fitness in prostate regions that were not identified as suspicious for cancer by a human-reader in pre- and intra-biopsy imaging analysis. METHODS Low-level pre-biopsy MRI biomarkers from targeted and non-targeted biopsy locations were extracted and statistically tested for representativeness against biomarkers from non-biopsied prostate regions. A probabilistic machine learning classifier was optimized to map biomarkers to their core-level pathology, followed by extrapolation of pathology scores to non-biopsied prostate regions. Goodness-of-fit was assessed at targeted and non-targeted biopsy locations for the post-biopsy individual patient. RESULTS Our experiments showed high predictability of imaging biomarkers in differentiating histopathology scores in thousands of non-targeted core-biopsy locations (ROC-AUCs: 0.85-0.88), but also high variability between patients (Median ROC-AUC [IQR]: 0.81-0.89 [0.29-0.40]). CONCLUSION The sparseness of prostate biopsy data makes the validation of a whole gland risk mapping a non-trivial task. Previous studies i) focused on targeted-biopsy locations although biopsy-specimens drawn from systematically scattered locations across the prostate constitute a more representative sample to non-biopsied regions, and ii) estimated prediction-power across predicted instances (e.g., biopsy specimens) with no patient distinction, which may lead to unreliable estimation of model fitness to the individual patient due to variation between patients in instance count, imaging characteristics, and pathologies. SIGNIFICANCE This study proposes a personalized whole-gland prostate cancer risk mapping post-biopsy to allow clinicians to better stage and personalize focal therapy treatment plans.
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Baydoun A, Jia AY, Zaorsky NG, Kashani R, Rao S, Shoag JE, Vince RA, Bittencourt LK, Zuhour R, Price AT, Arsenault TH, Spratt DE. Artificial intelligence applications in prostate cancer. Prostate Cancer Prostatic Dis 2024; 27:37-45. [PMID: 37296271 DOI: 10.1038/s41391-023-00684-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 05/05/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023]
Abstract
Artificial intelligence (AI) applications have enabled remarkable advancements in healthcare delivery. These AI tools are often aimed to improve accuracy and efficiency of histopathology assessment and diagnostic imaging interpretation, risk stratification (i.e., prognostication), and prediction of therapeutic benefit for personalized treatment recommendations. To date, multiple AI algorithms have been explored for prostate cancer to address automation of clinical workflow, integration of data from multiple domains in the decision-making process, and the generation of diagnostic, prognostic, and predictive biomarkers. While many studies remain within the pre-clinical space or lack validation, the last few years have witnessed the emergence of robust AI-based biomarkers validated on thousands of patients, and the prospective deployment of clinically-integrated workflows for automated radiation therapy design. To advance the field forward, multi-institutional and multi-disciplinary collaborations are needed in order to prospectively implement interoperable and accountable AI technology routinely in clinic.
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Affiliation(s)
- Atallah Baydoun
- Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Angela Y Jia
- Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Nicholas G Zaorsky
- Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Rojano Kashani
- Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Santosh Rao
- Department of Medicine, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Jonathan E Shoag
- Department of Urology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Randy A Vince
- Department of Urology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Leonardo Kayat Bittencourt
- Department of Radiology, University Hospitals Cleveland Medical Center Center, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Raed Zuhour
- Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Alex T Price
- Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Theodore H Arsenault
- Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Daniel E Spratt
- Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA.
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Zhao Y, Wang X, Zhang Y, Liu T, Zuo S, Sun L, Zhang J, Wang K, Liu J. Combination of clinical information and radiomics models for the differentiation of acute simple appendicitis and non simple appendicitis on CT images. Sci Rep 2024; 14:1854. [PMID: 38253872 PMCID: PMC10803326 DOI: 10.1038/s41598-024-52390-z] [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: 06/04/2023] [Accepted: 01/18/2024] [Indexed: 01/24/2024] Open
Abstract
To investigate the radiomics models for the differentiation of simple and non-simple acute appendicitis. This study retrospectively included 334 appendectomy cases (76 simple and 258 non-simple cases) for acute appendicitis. These cases were divided into training (n = 106) and test cohorts (n = 228). A radiomics model was developed using the radiomic features of the appendix area on CT images as the input variables. A CT model was developed using the clinical and CT features as the input variables. A combined model was developed by combining the radiomics model and clinical information. These models were tested, and their performance was evaluated by receiver operating characteristic curves and decision curve analysis (DCA). The variables independently associated with non-simple appendicitis in the combined model were body temperature, age, percentage of neutrophils and Rad-score. The AUC of the combined model was significantly higher than that of the CT model (P = 0.041). The AUC of the radiomics model was also higher than that of the CT model but did not reach a level of statistical significance (P = 0.053). DCA showed that all three models had a higher net benefit (NB) than the default strategies, and the combined model presented the highest NB. A nomogram of the combined model was developed as the graphical representation of the final model. It is feasible to use the combined information of clinical and CT radiomics models for the differentiation of simple and non-simple acute appendicitis.
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Affiliation(s)
- Yinming Zhao
- Department of Gastrointestinal Surgery, Peking University First Hospital, Beijing, China
| | - Xin Wang
- Department of Gastrointestinal Surgery, Peking University First Hospital, Beijing, China
| | - Yaofeng Zhang
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Tao Liu
- Department of Gastrointestinal Surgery, Peking University First Hospital, Beijing, China
| | - Shuai Zuo
- Department of Gastrointestinal Surgery, Peking University First Hospital, Beijing, China
| | - Lie Sun
- Department of Gastrointestinal Surgery, Peking University First Hospital, Beijing, China
| | - Junling Zhang
- Department of Gastrointestinal Surgery, Peking University First Hospital, Beijing, China.
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University Beijing, Beijing, China.
| | - Jing Liu
- Department of Radiology, Peking University First Hospital, Beijing, China.
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Ma Y, Li Q. An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning. Cancer Radiother 2023; 27:705-711. [PMID: 37932182 DOI: 10.1016/j.canrad.2023.05.005] [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] [Received: 05/13/2023] [Revised: 05/22/2023] [Accepted: 05/24/2023] [Indexed: 11/08/2023]
Abstract
PURPOSE The purpose of this study was to develop a model for predicting chemoradiation response in non-small cell lung cancer (NSCLC) patients by integrating radiomics and deep-learning features and combined intra- and peritumoral regions with pre-treated CT images. MATERIALS AND METHODS This study enrolled 462 patients with NSCLC who received chemoradiation. On the basis of pretreated CT images, we developed three models to compare the prediction of chemoradiation: intratumoral, peritumoral and combined regions. To further illustrate each model, we established different feature integration methods: a) radiomics model with 1500 features; b) deep learning model with a multiple instance learning algorithm; c) integrated model by integrating radiomic and deep learning features. For radiomics and integrated models, support vector machine and the least absolute shrinkage and selection operator were used to extract and select features. Transfer learning and max pooling algorithms were used to identify high informative features in deep learning models. We applied ten-fold cross validation in model training and testing. RESULTS The best area under the curve (AUC) of intratumoral, peritumoral and combined models were 0.89 (95% CI, 0.74-0.93), 0.86 (95% CI, 0.75-0.92) and 0.92 (95% CI, 0.81-0.95), respectively. It indicated the importance of the peritumoral region for treatment response prediction and should be used in combination with the intratumoral region. Integrated models gave better results than models with radiomics and deep learning features alone in all regions of interest and radiomics models outperformed deep learning models in any comparative models. CONCLUSIONS The model that integrate radiomic and deep learning features and combined intra- and peritumoral regions provide valuable information in predicting treatment response of chemoradiation. It can help oncologists customize personalized clinical treatment plans for NSCLC patients.
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Affiliation(s)
- Y Ma
- The First Affiliated Hospital of China Medical University, Department of Pathology, 110001 Shenyang, China.
| | - Q Li
- The First Affiliated Hospital of China Medical University, Department of Pathology, 110001 Shenyang, China.
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10
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Cai M, Zhao L, Zhang Y, Wu W, Jia L, Zhao J, Yang Q, Qiang Y. A progressive phased attention model fused histopathology image features and gene features for lung cancer staging prediction. Int J Comput Assist Radiol Surg 2023; 18:1857-1865. [PMID: 36943546 DOI: 10.1007/s11548-023-02844-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: 08/05/2022] [Accepted: 01/19/2023] [Indexed: 03/23/2023]
Abstract
PURPOSE Identifying the stage of lung cancer accurately from histopathology images and gene is very important for the diagnosis and treatment of lung cancer. Despite the substantial progress achieved by existing methods, it remains challenging due to large intra-class variances, and a high degree of inter-class similarities. METHODS In this paper, we propose a phased Multimodal Multi-scale Attention Model (MMAM) that predicts lung cancer stages using histopathology image data and gene data. The model consists of two phases. In Phase1, we propose a Staining Difference Elimination Network (SDEN) to eliminate staining differences between different histopathology images, In Phase2, it utilizes the image feature extractor provided by Phase1 to extract image features, and sends the multi-scale image features together with gene features into our Adaptive Enhanced Attention Fusion (AEAF) module for multimodal multi-scale features fusion to enable prediction of lung cancer staging. RESULTS We evaluated the proposed MMAM on the TCGA lung cancer dataset, and achieved 88.51% AUC and 88.17% accuracy on classification prediction of lung cancer stages I, II, III, and IV. CONCLUSION The method can help doctors diagnose the stage of lung cancer patients and can benefit from multimodal data.
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Affiliation(s)
- Meiling Cai
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Lin Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yanan Zhang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Wei Wu
- Department of Clinical Laboratory, Shanxi Provincial People's Hospital, Xi'an, China
| | - Liye Jia
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Qianqian Yang
- College of Information, Jinzhong College of Information, Jinzhong, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
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11
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Duenweg SR, Bobholz SA, Barrett MJ, Lowman AK, Winiarz A, Nath B, Stebbins M, Bukowy J, Iczkowski KA, Jacobsohn KM, Vincent-Sheldon S, LaViolette PS. T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence. Cancers (Basel) 2023; 15:4437. [PMID: 37760407 PMCID: PMC10526331 DOI: 10.3390/cancers15184437] [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: 06/30/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.
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Affiliation(s)
- Savannah R. Duenweg
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Samuel A. Bobholz
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Michael J. Barrett
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Allison K. Lowman
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Aleksandra Winiarz
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Biprojit Nath
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Margaret Stebbins
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - John Bukowy
- Department of Electrical Engineering and Computer Science, Milwaukee School of Engineering, 1025 N Broadway, Milwaukee, WI 53202, USA
| | - Kenneth A. Iczkowski
- Department of Pathology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA;
| | - Kenneth M. Jacobsohn
- Department of Urology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Stephanie Vincent-Sheldon
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Peter S. LaViolette
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
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12
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Midya A, Hiremath A, Huber J, Sankar Viswanathan V, Omil-Lima D, Mahran A, Bittencourt LK, Harsha Tirumani S, Ponsky L, Shiradkar R, Madabhushi A. Delta radiomic patterns on serial bi-parametric MRI are associated with pathologic upgrading in prostate cancer patients on active surveillance: preliminary findings. Front Oncol 2023; 13:1166047. [PMID: 37731630 PMCID: PMC10508842 DOI: 10.3389/fonc.2023.1166047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 07/24/2023] [Indexed: 09/22/2023] Open
Abstract
Objective The aim of this study was to quantify radiomic changes in prostate cancer (PCa) progression on serial MRI among patients on active surveillance (AS) and evaluate their association with pathologic progression on biopsy. Methods This retrospective study comprised N = 121 biopsy-proven PCa patients on AS at a single institution, of whom N = 50 at baseline conformed to the inclusion criteria. ISUP Gleason Grade Groups (GGG) were obtained from 12-core TRUS-guided systematic biopsies at baseline and follow-up. A biopsy upgrade (AS+) was defined as an increase in GGG (or in number of positive cores) and no upgrade (AS-) was defined when GGG remained the same during a median period of 18 months. Of N = 50 patients at baseline, N = 30 had MRI scans available at follow-up (median interval = 18 months) and were included for delta radiomic analysis. A total of 252 radiomic features were extracted from the PCa region of interest identified by board-certified radiologists on 3T bi-parametric MRI [T2-weighted (T2W) and apparent diffusion coefficient (ADC)]. Delta radiomic features were computed as the difference of radiomic feature between baseline and follow-up scans. The association of AS+ with age, prostate-specific antigen (PSA), Prostate Imaging Reporting and Data System (PIRADS v2.1) score, and tumor size was evaluated at baseline and follow-up. Various prediction models were built using random forest (RF) classifier within a threefold cross-validation framework leveraging baseline radiomics (Cbr), baseline radiomics + baseline clinical (Cbrbcl), delta radiomics (CΔr), delta radiomics + baseline clinical (CΔrbcl), and delta radiomics + delta clinical (CΔrΔcl). Results An AUC of 0.64 ± 0.09 was obtained for Cbr, which increased to 0.70 ± 0.18 with the integration of clinical variables (Cbrbcl). CΔr yielded an AUC of 0.74 ± 0.15. Integrating delta radiomics with baseline clinical variables yielded an AUC of 0.77 ± 0.23. CΔrΔclresulted in the best AUC of 0.84 ± 0.20 (p < 0.05) among all combinations. Conclusion Our preliminary findings suggest that delta radiomics were more strongly associated with upgrade events compared to PIRADS and other clinical variables. Delta radiomics on serial MRI in combination with changes in clinical variables (PSA and tumor volume) between baseline and follow-up showed the strongest association with biopsy upgrade in PCa patients on AS. Further independent multi-site validation of these preliminary findings is warranted.
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Affiliation(s)
- Abhishek Midya
- Department of Biomedical Engineering, Emory University, Atlanta, GA, United States
| | | | - Jacob Huber
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | | | | | - Amr Mahran
- Department of Urology, Assiut University, Asyut, Egypt
| | - Leonardo K. Bittencourt
- Department of Radiology, University Hospitals, Cleveland Medical Center, Cleveland, OH, United States
| | - Sree Harsha Tirumani
- Department of Radiology, University Hospitals, Cleveland Medical Center, Cleveland, OH, United States
| | - Lee Ponsky
- Department of Urology, University Hospitals, Cleveland Medical Center, Cleveland, OH, United States
| | - Rakesh Shiradkar
- Department of Biomedical Engineering, Emory University, Atlanta, GA, United States
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University, Atlanta, GA, United States
- Atlanta Veterans Administration Medical Center, Atlanta, GA, United States
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Qiu Y, Liu YF, Shu X, Qiao XF, Ai GY, He XJ. Peritumoral Radiomics Strategy Based on Ensemble Learning for the Prediction of Gleason Grade Group of Prostate Cancer. Acad Radiol 2023; 30 Suppl 1:S1-S13. [PMID: 37393175 DOI: 10.1016/j.acra.2023.06.011] [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: 04/03/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 07/03/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and evaluate a peritumoral radiomic-based machine learning model to differentiate low-Gleason grade group (L-GGG) and high-GGG (H-GGG) prostate lesions. MATERIALS AND METHODS In this retrospective study, a total of 175 patients with prostate cancer (PCa) confirmed by puncture biopsy were recruited and included 59 patients with L-GGG and 116 patients with H-GGG. The original PCa regions of interest (ROIs) were delineated on T2-weighted (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps, and then centra-tumoral and peritumoral ROIs were defined. Features were meticulously extracted from each ROI to establish radiomics models, employing distinct sequence datasets. Peritumoral radiomics models were specifically developed for both the peripheral zone (PZ) and transitional zone (TZ), utilizing dedicated PZ and TZ datasets, respectively. The performances of the models were evaluated by using the receiver operating characteristic (ROC) curve and precision-recall curve. RESULTS The classification model with combined peritumoral features based on T2 + DWI + ADC sequence dataset demonstrated superior performance compared to the original tumor and centra-tumoral classification models. It achieved an area under the ROC curve (AUC) of 0.850 [95% confidence interval, 0.849, 0.860] and an average accuracy of 0.950. The combined peritumoral model outperformed the regional peritumoral models with AUC of 0.85 versus 0.75 for PZ lesions and 0.88 versus 0.69 for TZ lesions, respectively. The peritumoral classification models exhibit greater efficacy in predicting PZ lesions as opposed to TZ lesions. CONCLUSION The peritumoral radiomics features showed excellent performance in predicting GGG in PCa patients and might be a valuable addition to the non-invasive assessment of PCa aggressiveness.
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Affiliation(s)
- Yang Qiu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Yun-Fan Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Xin Shu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Xiao-Feng Qiao
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Guang-Yong Ai
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Xiao-Jing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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14
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Zhao LT, Liu ZY, Xie WF, Shao LZ, Lu J, Tian J, Liu JG. What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments? Mil Med Res 2023; 10:29. [PMID: 37357263 DOI: 10.1186/s40779-023-00464-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 06/07/2023] [Indexed: 06/27/2023] Open
Abstract
The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.
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Affiliation(s)
- Li-Tao Zhao
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhen-Yu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Wan-Fang Xie
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Li-Zhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Jian Lu
- Department of Urology, Peking University Third Hospital, Peking University, 100191, Beijing, China.
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, 100191, Beijing, China.
| | - Jian-Gang Liu
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, 100191, Beijing, China.
- Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing, 100029, China.
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15
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Li L, Shiradkar R, Tirumani SH, Bittencourt LK, Fu P, Mahran A, Buzzy C, Stricker PD, Rastinehad AR, Magi-Galluzzi C, Ponsky L, Klein E, Purysko AS, Madabhushi A. Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer. Eur J Radiol Open 2023; 10:100496. [PMID: 37396490 PMCID: PMC10311200 DOI: 10.1016/j.ejro.2023.100496] [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: 03/22/2023] [Revised: 06/01/2023] [Accepted: 06/04/2023] [Indexed: 07/04/2023] Open
Abstract
Background around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─). Objective To quantify the differences between MR visible (MRI+) and MRI─ CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI). Methods This retrospective and multi-institutional study comprised 164 patients with pre-biopsy 3T prostate multi-parametric MRI from 2014 to 2017. The MRI─ CsPCa referred to lesions with PI-RADS v2 score < 3 but ISUP grade group > 1. Three experienced radiologists were involved in annotating lesions and PI-RADS assignment. The validation set (Dv) comprised 52 patients from a single institution, the remaining 112 patients were used for training (Dt). 200 radiomic features were extracted from intra-lesional and peri-lesional regions on bpMRI.Logistic regression with least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation was applied on Dt to identify radiomic features associated with MRI─ and MRI+ CsPCa to generate corresponding risk scores RMRI─ and RMRI+. RbpMRI was further generated by integrating RMRI─ and RMRI+. Statistical significance was determined using the Wilcoxon signed-rank test. Results Both intra-lesional and peri-lesional bpMRI Haralick and CoLlAGe radiomic features were significantly associated with MRI─ CsPCa (p < 0.05). Intra-lesional ADC Haralick and CoLlAGe radiomic features were significantly different among MRI─ and MRI+ CsPCa (p < 0.05). RbpMRI yielded the highest AUC of 0.82 (95 % CI 0.72-0.91) compared to AUCs of RMRI+ 0.76 (95 % CI 0.63-0.89), and PI-RADS 0.58 (95 % CI 0.50-0.72) on Dv. RbpMRI correctly reclassified 10 out of 14 MRI─ CsPCa on Dv. Conclusion Our preliminary results demonstrated that both intra-lesional and peri-lesional bpMRI radiomic features were significantly associated with MRI─ CsPCa. These features could assist in CsPCa identification on bpMRI.
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Affiliation(s)
- Lin Li
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Rakesh Shiradkar
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology
| | | | | | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Amr Mahran
- Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Christina Buzzy
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | | | | | | | - Lee Ponsky
- Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Eric Klein
- Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Andrei S. Purysko
- Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology
- Atlanta Veterans Affairs Medical Center, Atlanta, GA, United States
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16
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Chang R, Qi S, Wu Y, Yue Y, Zhang X, Guan Y, Qian W. Deep radiomic model based on the sphere-shell partition for predicting treatment response to chemotherapy in lung cancer. Transl Oncol 2023; 35:101719. [PMID: 37320871 DOI: 10.1016/j.tranon.2023.101719] [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: 11/27/2022] [Revised: 05/16/2023] [Accepted: 06/08/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND The prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients. OBJECTIVES To develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images. MATERIALS AND METHODS This retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment. Two integrated models were developed using radiomic and deep-learning-based features. First, we partitioned pre-chemotherapy CT images into spheres and shells with different radii around the tumor (0-3, 3-6, 6-9, 9-12, 12-15 mm) containing intratumoral and peritumoral regions. Second, we extracted radiomic and deep-learning-based features from each partition. Third, using radiomic features, five sphere-shell models, one feature fusion model, and one image fusion model were developed. Finally, the model with the best performance was validated in two cohorts. RESULTS Among the five partitions, the model of 9-12 mm achieved the highest area under the curve (AUC) of 0.87 (95% confidence interval: 0.77-0.94). The AUC was 0.94 (0.85-0.98) for the feature fusion model and 0.91 (0.82-0.97) for the image fusion model. For the model integrating radiomic and deep-learning-based features, the AUC was 0.96 (0.88-0.99) for the feature fusion method and 0.94 (0.85-0.98) for the image fusion method. The best-performing model had an AUC of 0.91 (0.81-0.97) and 0.89 (0.79-0.93) in two validation sets, respectively. CONCLUSIONS This integrated model can predict the response to chemotherapy in NSCLC patients and assist physicians in clinical decision-making.
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Affiliation(s)
- Runsheng Chang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Yanan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yong Yue
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaoye Zhang
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yubao Guan
- Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wei Qian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
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17
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Zhou Y, Yuan J, Xue C, Poon DMC, Yang B, Yu SK, Cheung KY. A pilot study of MRI radiomics for high-risk prostate cancer stratification in 1.5 T MR-guided radiotherapy. Magn Reson Med 2023; 89:2088-2099. [PMID: 36572990 DOI: 10.1002/mrm.29564] [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: 08/30/2022] [Revised: 11/09/2022] [Accepted: 12/09/2022] [Indexed: 12/28/2022]
Abstract
PURPOSE To investigate the potential value of MRI radiomics obtained from a 1.5 T MRI-guided linear accelerator (MR-LINAC) for D'Amico high-risk prostate cancer (PC) classification in MR-guided radiotherapy (MRgRT). METHODS One hundred seventy-six consecutive PC patients underwent 1.5 T MRgRT treatment were retrospectively enrolled. Each patient received one or two pretreatment T2 -weighted MRI scans on a 1.5 T MR-LINAC. The endpoint was to differentiate high-risk from low/intermediate-risk PC based on D'Amico criteria using MRI-radiomics. Totally 1023 features were extracted from clinical target volume (CTV) and planning target volume (PTV). Intraclass correlation coefficient of scan-rescan repeatability, feature correlation, and recursive feature elimination were used for feature dimension reduction. Least absolute shrinkage and selection operator regression was employed for model construction. Receiver operating characteristic area under the curve (AUC) analysis was used for model performance assessment in both training and testing data. RESULTS One hundred and eleven patients fulfilled all criteria were finally included: 76 for training and 35 for testing. The constructed MRI-radiomics models extracted from CTV and PTV achieved the AUC of 0.812 and 0.867 in the training data, without significant difference (P = 0.083). The model performances remained in the testing. The sensitivity, specificity, and accuracy were 85.71%, 64.29%, and 77.14% for the PTV-based model; and 71.43%, 71.43%, and 71.43% for the CTV-based model. The corresponding AUCs were 0.718 and 0.750 (P = 0.091) for CTV- and PTV-based models. CONCLUSION MRI-radiomics obtained from a 1.5 T MR-LINAC showed promising results in D'Amico high-risk PC stratification, potentially helpful for the future PC MRgRT. Prospective studies with larger sample sizes and external validation are warranted for further verification.
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Affiliation(s)
- Yihang Zhou
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Jing Yuan
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Cindy Xue
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Darren M C Poon
- Comprehensive Oncology Center, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Bin Yang
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Siu Ki Yu
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Kin Yin Cheung
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
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18
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Shu X, Liu Y, Qiao X, Ai G, Liu L, Liao J, Deng Z, He X. Radiomic-based machine learning model for the accurate prediction of prostate cancer risk stratification. Br J Radiol 2023; 96:20220238. [PMID: 36475858 PMCID: PMC9975368 DOI: 10.1259/bjr.20220238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 10/20/2022] [Accepted: 11/20/2022] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES To precisely predict prostate cancer (PCa) risk stratification, we constructed a machine learning (ML) model based on magnetic resonance imaging (MRI) radiomic features. METHODS Between August 2016 and May 2021, patients with histologically proven PCa who underwent pre-operative MRI and prostate-specific antigen screening were included. The patients were grouped into different risk categories as defined by the European Association of Urology-European Association of Nuclear Medicine-European Society for Radiotherapy and Oncology-European Society of Urogenital Radiology-International Society of Geriatric Oncology guidelines. Using Artificial Intelligence Kit software, PCa regions of interest were delineated and radiomic features were extracted. Subsequently, predictable models were built by utilising five traditional ML approaches: support vector machine, logistic regression, gradient boosting decision tree, k-nearest neighbour and random forest (RF) classifiers. The classification capacity of the developed models was assessed by area under the receiver operating characteristic curve (AUC) analysis. RESULTS A total of 213 patients were enrolled, including 16 low-risk, 65 intermediate-risk, and 132 high-risk PCa patients. The risk stratification of PCa could be revealed by MRI radiomic features, and second-order features accounted for most of the selected features. Among the five established ML models, the RF model showed the best overall predictive performance (AUC = 0.87). After further analysis of the subgroups based on the RF model, the prediction of the high-risk group was the best (AUC = 0.89). CONCLUSION This study demonstrated that the MR radiomics-based ML method could be a promising tool for predicting PCa risk stratification precisely. ADVANCES IN KNOWLEDGE The ML models have valuable prospect for accurate PCa risk assessment, which might contribute to customize treatment and surveillance strategies.
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Affiliation(s)
- Xin Shu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yunfan Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaofeng Qiao
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Guangyong Ai
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Liu
- College of Big Data & Software Engineering, Chongqing University, Chongqing, China
| | - Jun Liao
- College of Big Data & Software Engineering, Chongqing University, Chongqing, China
| | - Zhengqiao Deng
- College of Big Data & Software Engineering, Chongqing University, Chongqing, China
| | - Xiaojing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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19
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Belue MJ, Harmon SA, Lay NS, Daryanani A, Phelps TE, Choyke PL, Turkbey B. The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms. J Am Coll Radiol 2023; 20:134-145. [PMID: 35922018 PMCID: PMC9887098 DOI: 10.1016/j.jacr.2022.05.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 05/13/2022] [Accepted: 05/18/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research. MATERIALS AND METHODS This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist. The additional outcomes for which data were sought included measures of AI model performance (eg, area under the curve [AUC], sensitivity, specificity, free-response operating characteristic curves), training and validation and testing group sample size, AI approach, detection versus classification AI, public data set utilization, MRI sequences used, and definition of gold standard for ground truth. The percentage of CLAIM checklist fulfillment was used to stratify studies into quartiles. Wilcoxon's rank-sum test was used for pair-wise comparisons. RESULTS In all, 75 studies were identified, and 53 studies qualified for analysis. The original CLAIM items that most studies did not fulfill includes item 12 (77% no): de-identification methods; item 13 (68% no): handling missing data; item 15 (47% no): rationale for choosing ground truth reference standard; item 18 (55% no): measurements of inter- and intrareader variability; item 31 (60% no): inclusion of validated interpretability maps; item 37 (92% no): inclusion of failure analysis to elucidate AI model weaknesses. An AUC score versus percentage CLAIM fulfillment quartile revealed a significant difference of the mean AUC scores between quartile 1 versus quartile 2 (0.78 versus 0.86, P = .034) and quartile 1 versus quartile 4 (0.78 versus 0.89, P = .003) scores. Based on additional information and outcome metrics gathered in this study, additional measures of best practice are defined. These new items include disclosure of public dataset usage, ground truth definition in comparison to other referenced works in the defined task, and sample size power calculation. CONCLUSION A large proportion of AI studies do not fulfill key items in CLAIM guidelines within their methods and results sections. The percentage of CLAIM checklist fulfillment is weakly associated with improved AI model performance. Additions or supplementations to CLAIM are recommended to improve publishing standards and aid reviewers in determining study rigor.
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Affiliation(s)
- Mason J Belue
- Medical Research Scholars Program Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephanie A Harmon
- Staff Scientist, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Nathan S Lay
- Staff Scientist, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Asha Daryanani
- Intramural Research Training Program Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Tim E Phelps
- Postdoctoral Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Artificial Intelligence Resource, Chief of Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Baris Turkbey
- Senior Clinician/Director, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
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20
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Evaluating the Downstream Revenues of a Self-Pay Bi-Parametric Prostate MRI Program. Urology 2023; 171:109-114. [PMID: 36195163 DOI: 10.1016/j.urology.2022.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/24/2022] [Accepted: 09/25/2022] [Indexed: 01/12/2023]
Abstract
OBJECTIVE To quantify downstream healthcare utilization and revenue associated with a self-pay bi-parametric prostate MRI (bpMRI) program. METHODS Medical records of 592 patients who underwent bpMRI between August 2017 and March 2020 were examined for follow-up clinical activities. These include prostate biopsy, radical prostatectomy, external beam radiation therapy, brachytherapy, androgen deprivation therapy, CT Chest, Abdomen and Pelvis, PET/CT, MRI Pelvis, and Nuclear Medicine Bone Scans. The charges for each clinical activity were derived from the Medicare Physician Fee Schedule to conservatively estimate revenues. This patient population was further divided into four groups: Group A, patients who demonstrated an MRI lesion and underwent prostatectomy; Group B, patients who did not demonstrate lesion but underwent prostatectomy; Group C, patients who demonstrated lesion but did not undergo prostatectomy; and Group D, patients who neither demonstrated lesion nor underwent prostatectomy. Revenues for each group were categorized by Urology, Radiation Oncology and Radiology subspecialties. RESULTS Conservative estimates yielded $520 of downstream revenue per patient who underwent bpMRI. Group A patients yielded 47% of total revenue ($1974 per patient). Group B patients, the smallest group, yielded $1828 per patient. Group C patients made up the largest group and grossed $398 per patient. Group D demonstrated the lowest per patient revenue of $179. Groups A and B yielded most relative revenue for Urology. Group C yielded most relative revenue for Radiation Oncology, and Group D yielded most relative revenue for Radiology. CONCLUSION A self-pay bpMRI program has the potential to improve patient access to prostate cancer screening while remaining financial sustainable.
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21
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Chang R, Qi S, Wu Y, Song Q, Yue Y, Zhang X, Guan Y, Qian W. Deep multiple instance learning for predicting chemotherapy response in non-small cell lung cancer using pretreatment CT images. Sci Rep 2022; 12:19829. [PMID: 36400881 PMCID: PMC9672640 DOI: 10.1038/s41598-022-24278-3] [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: 05/29/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022] Open
Abstract
The individual prognosis of chemotherapy is quite different in non-small cell lung cancer (NSCLC). There is an urgent need to precisely predict and assess the treatment response. To develop a deep multiple-instance learning (DMIL) based model for predicting chemotherapy response in NSCLC in pretreatment CT images. Two datasets of NSCLC patients treated with chemotherapy as the first-line treatment were collected from two hospitals. Dataset 1 (163 response and 138 nonresponse) was used to train, validate, and test the DMIL model and dataset 2 (22 response and 20 nonresponse) was used as the external validation cohort. Five backbone networks in the feature extraction module and three pooling methods were compared. The DMIL with a pre-trained VGG16 backbone and an attention mechanism pooling performed the best, with an accuracy of 0.883 and area under the curve (AUC) of 0.982 on Dataset 1. While using max pooling and convolutional pooling, the AUC was 0.958 and 0.931, respectively. In Dataset 2, the best DMIL model produced an accuracy of 0.833 and AUC of 0.940. Deep learning models based on the MIL can predict chemotherapy response in NSCLC using pretreatment CT images and the pre-trained VGG16 with attention mechanism pooling yielded better predictions.
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Affiliation(s)
- Runsheng Chang
- grid.412252.20000 0004 0368 6968College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- grid.412252.20000 0004 0368 6968College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China ,grid.412252.20000 0004 0368 6968Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Yanan Wu
- grid.412252.20000 0004 0368 6968College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Qiyuan Song
- grid.412252.20000 0004 0368 6968College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yong Yue
- grid.412467.20000 0004 1806 3501Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaoye Zhang
- grid.412467.20000 0004 1806 3501Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yubao Guan
- grid.410737.60000 0000 8653 1072Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wei Qian
- grid.412252.20000 0004 0368 6968College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
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22
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Yin F, Zhang H, Qi A, Zhu Z, Yang L, Wen G, Xie W. An exploratory study of CT radiomics using differential network feature selection for WHO/ISUP grading and progression-free survival prediction of clear cell renal cell carcinoma. Front Oncol 2022; 12:979613. [DOI: 10.3389/fonc.2022.979613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 10/11/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesTo explore the feasibility of predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade and progression-free survival (PFS) of clear cell renal cell cancer (ccRCC) using the radiomics features (RFs) based on the differential network feature selection (FS) method using the maximum-entropy probability model (MEPM).Methods175 ccRCC patients were divided into a training set (125) and a test set (50). The non-contrast phase (NCP), cortico-medullary phase, nephrographic phase, excretory phase phases, and all-phase WHO/ISUP grade prediction models were constructed based on a new differential network FS method using the MEPM. The diagnostic performance of the best phase model was compared with the other state-of-the-art machine learning models and the clinical models. The RFs of the best phase model were used for survival analysis and visualized using risk scores and nomograms. The performance of the above models was tested in both cross-validated and independent validation and checked by the Hosmer-Lemeshow test.ResultsThe NCP RFs model was the best phase model, with an AUC of 0.89 in the test set, and performed superior to other machine learning models and the clinical models (all p <0.05). Kaplan-Meier survival analysis, univariate and multivariate cox regression results, and risk score analyses showed the NCP RFs could predict PFS well (almost all p < 0.05). The nomogram model incorporated the best two RFs and showed good discrimination, a C-index of 0.71 and 0.69 in the training and test set, and good calibration.ConclusionThe NCP CT-based RFs selected by differential network FS could predict the WHO/ISUP grade and PFS of RCC.
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23
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Algohary A, Alhusseini M, Breto AL, Kwon D, Xu IR, Gaston SM, Castillo P, Punnen S, Spieler B, Abramowitz MC, Dal Pra A, Kryvenko ON, Pollack A, Stoyanova R. Longitudinal Changes and Predictive Value of Multiparametric MRI Features for Prostate Cancer Patients Treated with MRI-Guided Lattice Extreme Ablative Dose (LEAD) Boost Radiotherapy. Cancers (Basel) 2022; 14:cancers14184475. [PMID: 36139635 PMCID: PMC9496901 DOI: 10.3390/cancers14184475] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/01/2022] [Accepted: 09/10/2022] [Indexed: 11/16/2022] Open
Abstract
We investigated the longitudinal changes in multiparametric MRI (mpMRI) (T2-weighted, Apparent Diffusion Coefficient (ADC), and Dynamic Contrast Enhanced (DCE-)MRI) of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) radiotherapy (RT) and the capability of their imaging features to predict RT outcome based on endpoint biopsies. Ninety-five mpMRI exams from 25 patients, acquired pre-RT and at 3-, 9-, and 24-months post-RT were analyzed. MRI/Ultrasound-fused biopsies were acquired pre- and at two-years post-RT (endpoint). Five regions of interest (ROIs) were analyzed: Gross tumor volume (GTV), normally-appearing tissue (NAT) and peritumoral volume in both peripheral (PZ) and transition (TZ) zones. Diffusion and perfusion radiomics features were extracted from mpMRI and compared before and after RT using two-tailed Student t-tests. Selected features at the four scan points and their differences (Δ radiomics) were used in multivariate logistic regression models to predict the endpoint biopsy positivity. Baseline ADC values were significantly different between GTV, NAT-PZ, and NAT-TZ (p-values < 0.005). Pharmaco-kinetic features changed significantly in the GTV at 3-month post-RT compared to baseline. Several radiomics features at baseline and three-months post-RT were significantly associated with endpoint biopsy positivity and were used to build models with high predictive power of this endpoint (AUC = 0.98 and 0.89, respectively). Our study characterized the RT-induced changes in perfusion and diffusion. Quantitative imaging features from mpMRI show promise as being predictive of endpoint biopsy positivity.
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Affiliation(s)
- Ahmad Algohary
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Mohammad Alhusseini
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Adrian L. Breto
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Deukwoo Kwon
- Biostatistics and Bioinformatics Shared Resource, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Isaac R. Xu
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sandra M. Gaston
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Patricia Castillo
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sanoj Punnen
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Benjamin Spieler
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Matthew C. Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Oleksandr N. Kryvenko
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Pathology and Laboratory Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Correspondence: ; Tel.: +1-305-243-5856
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24
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Chang R, Qi S, Zuo Y, Yue Y, Zhang X, Guan Y, Qian W. Predicting chemotherapy response in non-small-cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined? Front Oncol 2022; 12:915835. [PMID: 36003781 PMCID: PMC9393703 DOI: 10.3389/fonc.2022.915835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/18/2022] [Indexed: 11/15/2022] Open
Abstract
Purpose This study aims to evaluate the ability of peritumoral, intratumoral, or combined computed tomography (CT) radiomic features to predict chemotherapy response in non-small cell lung cancer (NSCLC). Methods After excluding subjects with incomplete data or other types of treatments, 272 (Dataset 1) and 43 (Dataset 2, external validation) NSCLC patients who were only treated with chemotherapy as the first-line treatment were enrolled between 2015 and 2019. All patients were divided into response and nonresponse based on the response evaluation criteria in solid tumors, version 1.1. By using 3D slicer and morphological operations in python, the intra- and peritumoral regions of lung tumors were segmented from pre-treatment CT images (unenhanced) and confirmed by two experienced radiologists. Then radiomic features (the first order, texture, shape, et al.) were extracted from the above regions of interest. The models were trained and tested in Dataset 1 and further validated in Dataset 2. The performance of models was compared using the area under curve (AUC), confusion matrix, accuracy, precision, recall, and F1-score. Results The radiomic model using features from the peritumoral region of 0–3 mm outperformed that using features from 3–6, 6–9, 9–12 mm peritumoral region, and intratumoral region (AUC: 0.95 versus 0.87, 0.86, 0.85, and 0.88). By the fusion of features from 0–3 and 3–6 mm peritumoral regions, the logistic regression model achieved the best performance, with an AUC of 0.97. This model achieved an AUC of 0.85 in the external cohort. Moreover, among the 20 selected features, seven features differed significantly between the two groups (p < 0.05). Conclusions CT radiomic features from both the peri- and intratumoral regions can predict chemotherapy response in NSCLC using machine learning models. Combined features from two peritumoral regions yielded better predictions.
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Affiliation(s)
- Runsheng Chang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
- *Correspondence: Shouliang Qi,
| | - Yifan Zuo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yong Yue
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaoye Zhang
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yubao Guan
- Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wei Qian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
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Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine. Cancers (Basel) 2022; 14:cancers14122860. [PMID: 35740526 PMCID: PMC9220825 DOI: 10.3390/cancers14122860] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/03/2022] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices. Abstract Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.
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26
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Sushentsev N, Moreira Da Silva N, Yeung M, Barrett T, Sala E, Roberts M, Rundo L. Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review. Insights Imaging 2022; 13:59. [PMID: 35347462 PMCID: PMC8960511 DOI: 10.1186/s13244-022-01199-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 02/24/2022] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES We systematically reviewed the current literature evaluating the ability of fully-automated deep learning (DL) and semi-automated traditional machine learning (TML) MRI-based artificial intelligence (AI) methods to differentiate clinically significant prostate cancer (csPCa) from indolent PCa (iPCa) and benign conditions. METHODS We performed a computerised bibliographic search of studies indexed in MEDLINE/PubMed, arXiv, medRxiv, and bioRxiv between 1 January 2016 and 31 July 2021. Two reviewers performed the title/abstract and full-text screening. The remaining papers were screened by four reviewers using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) for DL studies and Radiomics Quality Score (RQS) for TML studies. Papers that fulfilled the pre-defined screening requirements underwent full CLAIM/RQS evaluation alongside the risk of bias assessment using QUADAS-2, both conducted by the same four reviewers. Standard measures of discrimination were extracted for the developed predictive models. RESULTS 17/28 papers (five DL and twelve TML) passed the quality screening and were subject to a full CLAIM/RQS/QUADAS-2 assessment, which revealed a substantial study heterogeneity that precluded us from performing quantitative analysis as part of this review. The mean RQS of TML papers was 11/36, and a total of five papers had a high risk of bias. AUCs of DL and TML papers with low risk of bias ranged between 0.80-0.89 and 0.75-0.88, respectively. CONCLUSION We observed comparable performance of the two classes of AI methods and identified a number of common methodological limitations and biases that future studies will need to address to ensure the generalisability of the developed models.
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Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK.
| | | | - Michael Yeung
- Department of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | - Tristan Barrett
- Department of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Lucida Medical Ltd, Biomedical Innovation Hub, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, The Cambridge Mathematics of Information in Healthcare Hub, University of Cambridge, Cambridge, UK
- Oncology R&D, AstraZeneca, Cambridge, UK
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Lucida Medical Ltd, Biomedical Innovation Hub, University of Cambridge, Cambridge, UK
- Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Fisciano, SA, Italy
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27
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Cho HH, Kim CK, Park H. Overview of radiomics in prostate imaging and future directions. Br J Radiol 2022; 95:20210539. [PMID: 34797688 PMCID: PMC8978251 DOI: 10.1259/bjr.20210539] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Recent advancements in imaging technology and analysis methods have led to an analytic framework known as radiomics. This framework extracts comprehensive high-dimensional features from imaging data and performs data mining to build analytical models for improved decision-support. Its features include many categories spanning texture and shape; thus, it can provide abundant information for precision medicine. Many studies of prostate radiomics have shown promising results in the assessment of pathological features, prediction of treatment response, and stratification of risk groups. Herein, we aimed to provide a general overview of radiomics procedures, discuss technical issues, explain various clinical applications, and suggest future research directions, especially for prostate imaging.
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Affiliation(s)
- Hwan-Ho Cho
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea.,School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea
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28
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Ferro M, de Cobelli O, Musi G, del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022; 14:17562872221109020. [PMID: 35814914 PMCID: PMC9260602 DOI: 10.1177/17562872221109020] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy
| | - Ottavio de Cobelli
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco del Giudice
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Carrieri
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | | | - Alessandro Sciarra
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Martina Maggi
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Vincenzo Francesco Caputo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, Chieti, Italy; Urology Unit, ‘SS. Annunziata’ Hospital, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti, Italy
| | - Giuseppe Lucarelli
- Department of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Stefano Luzzago
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Luigi Cormio
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
- Urology Unit, Bonomo Teaching Hospital, Foggia, Italy
| | | | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral Studies, I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
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Bhattacharya I, Khandwala YS, Vesal S, Shao W, Yang Q, Soerensen SJ, Fan RE, Ghanouni P, Kunder CA, Brooks JD, Hu Y, Rusu M, Sonn GA. A review of artificial intelligence in prostate cancer detection on imaging. Ther Adv Urol 2022; 14:17562872221128791. [PMID: 36249889 PMCID: PMC9554123 DOI: 10.1177/17562872221128791] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 08/30/2022] [Indexed: 11/07/2022] Open
Abstract
A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.
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Affiliation(s)
- Indrani Bhattacharya
- Department of Radiology, Stanford University School of Medicine, 1201 Welch Road, Stanford, CA 94305, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yash S. Khandwala
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Sulaiman Vesal
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Wei Shao
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Qianye Yang
- Centre for Medical Image Computing, University College London, London, UK
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Simon J.C. Soerensen
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Richard E. Fan
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Pejman Ghanouni
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Christian A. Kunder
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - James D. Brooks
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yipeng Hu
- Centre for Medical Image Computing, University College London, London, UK
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Mirabela Rusu
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Geoffrey A. Sonn
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
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30
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Zhan C, Hu Y, Wang X, Liu H, Xia L, Ai T. Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Intra-peritumoral Textural Transition Analysis based on Dynamic Contrast-enhanced Magnetic Resonance Imaging. Acad Radiol 2022; 29 Suppl 1:S107-S115. [PMID: 33712393 DOI: 10.1016/j.acra.2021.02.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/25/2021] [Accepted: 02/08/2021] [Indexed: 12/29/2022]
Abstract
RATIONALE AND OBJECTIVES Intra-peritumoural textural transition (Ipris) is a new radiomics method, which includes a series of quantitative measurements of the image features that represent the differences between the inside and outside of the tumour. This study aimed to explore the feasibility of Ipris analysis for the preoperative prediction of axillary lymph node (ALN) status in patients with breast cancer based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS This study was approved by the Institutional Review Board (IRB) of our hospital. One hundred sixty-six patients with clinicopathologically confirmed invasive breast cancer and ALN status were enrolled. All patients underwent preoperative breast DCE-MRI examinations. The primary breast lesion was manually segmented using the ITK-SNAP software for each patient. Two sets of image features were extracted, including Ipris features and conventional intratumoural features. Feature selection was conducted using Spearman correlation analysis and support vector machine with recursive feature elimination (SVM-RFE). Next, three models were established in training dataset: Model 1 was established by Ipris features; Model 2 was established by intratumoural features; Model 3 was established by combining Ipris features and intratumoural features. The performances of the three models were evaluated for the prediction of ALN status in testing datasets. RESULTS Model 1 with four Ipris features achieved an AUC of 0.816 (95% CI, 0.733-0.883) and 0.829 (95% CI, 0.695-0.922) in the training and testing datasets, respectively. Model 2 with six intratumoural features achieved an AUC of 0.801 (95% CI, 0.716-0.870) and 0.824 (95% CI, 0.689-0.918) in the training and testing datasets, respectively. By incorporating the Ipris and intratumoural features, the AUC of Model 3 increased to 0.968 (95% CI, 0.916-0.992) and 0.855 (95% CI, 0.724-0.939) in the training and testing datasets, respectively. CONCLUSION Ipris features based on DCE-MRI can be used to predict ALN status in patients with breast cancer. The model combining intratumoural and Ipris features achieved higher prediction performance.
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Ghezzo S, Bezzi C, Presotto L, Mapelli P, Bettinardi V, Savi A, Neri I, Preza E, Samanes Gajate AM, De Cobelli F, Scifo P, Picchio M. State of the art of radiomic analysis in the clinical management of prostate cancer: A systematic review. Crit Rev Oncol Hematol 2021; 169:103544. [PMID: 34801699 DOI: 10.1016/j.critrevonc.2021.103544] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/18/2021] [Accepted: 10/18/2021] [Indexed: 02/04/2023] Open
Abstract
We present the current clinical applications of radiomics in the context of prostate cancer (PCa) management. Several online databases for original articles using a combination of the following keywords: "(radiomic or radiomics) AND (prostate cancer or prostate tumour or prostate tumor or prostate neoplasia)" have been searched. The selected papers have been pooled as focus on (i) PCa detection, (ii) assessing the clinical significance of PCa, (iii) biochemical recurrence prediction, (iv) radiation-therapy outcome prediction and treatment efficacy monitoring, (v) metastases detection, (vi) metastases prediction, (vii) prediction of extra-prostatic extension. Seventy-six studies were included for qualitative analyses. Classifiers powered with radiomic features were able to discriminate between healthy tissue and PCa and between low- and high-risk PCa. However, before radiomics can be proposed for clinical use its methods have to be standardized, and these first encouraging results need to be robustly replicated in large and independent cohorts.
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Affiliation(s)
| | | | - Luca Presotto
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Mapelli
- Vita-Salute San Raffaele University, Milan, Italy; Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Valentino Bettinardi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Annarita Savi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Ilaria Neri
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Erik Preza
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Francesco De Cobelli
- Vita-Salute San Raffaele University, Milan, Italy; Radiology Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Scifo
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria Picchio
- Vita-Salute San Raffaele University, Milan, Italy; Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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32
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Mendes B, Domingues I, Silva A, Santos J. Prostate Cancer Aggressiveness Prediction Using CT Images. Life (Basel) 2021; 11:life11111164. [PMID: 34833040 PMCID: PMC8618689 DOI: 10.3390/life11111164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 11/16/2022] Open
Abstract
Prostate Cancer (PCa) is mostly asymptomatic at an early stage and often painless requiring active surveillance screening. Transrectal Ultrasound Guided Biopsy (TRUS) is the principal method to diagnose PCa following a histological examination by observing cell pattern irregularities and assigning the Gleason Score (GS) according to the recommended guidelines. This procedure presents sampling errors and, being invasive may cause complications to the patients. External Beam Radiotherapy Treatment (EBRT) is presented as curative option for localised and locally advanced disease, as a palliative option for metastatic low-volume disease or after prostatectomy for prostate bed and pelvic nodes salvage. In the EBRT worflow a Computed Tomography (CT) scan is performed as the basis for dose calculations and volume delineations. In this work, we evaluated the use of data-characterization algorithms (radiomics) from CT images for PCa aggressiveness assessment. The fundamental motivation relies on the wide availability of CT images and the need to provide tools to assess EBRT effectiveness. We used Pyradiomics and Local Image Features Extraction (LIFEx) to extract features and search for a radiomic signature within CT images. Finnaly, when applying Principal Component Analysis (PCA) to the features, we were able to show promising results.
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Affiliation(s)
- Bruno Mendes
- Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP), Grupo de Física Médica, Radiobiologia e Protecção Radiológica, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Faculdade de Engenharia da Universidade do Porto (FEUP), 4200-465 Porto, Portugal
- Correspondence:
| | - Inês Domingues
- Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP), Grupo de Física Médica, Radiobiologia e Protecção Radiológica, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Instituto Superior de Engenharia de Coimbra (ISEC), 3030-199 Coimbra, Portugal
| | - Augusto Silva
- IEETA, Universidade de Aveiro (UA), 3810-193 Aveiro, Portugal;
| | - João Santos
- Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP), Grupo de Física Médica, Radiobiologia e Protecção Radiológica, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Instituto de Ciências Biomédicas Abel Salazar (ICBAS), 4050-313 Porto, Portugal
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Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 2021; 19:132-146. [PMID: 34663898 DOI: 10.1038/s41571-021-00560-7] [Citation(s) in RCA: 239] [Impact Index Per Article: 79.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/03/2021] [Indexed: 12/14/2022]
Abstract
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.
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Affiliation(s)
- Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Nathaniel Braman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Tempus Labs, Chicago, IL, USA
| | - Amit Gupta
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. .,Louis Stokes Cleveland Veterans Medical Center, Cleveland, OH, USA.
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Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization. Int J Mol Sci 2021; 22:ijms22189971. [PMID: 34576134 PMCID: PMC8465891 DOI: 10.3390/ijms22189971] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/06/2021] [Accepted: 09/10/2021] [Indexed: 12/24/2022] Open
Abstract
Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.
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Hiremath A, Shiradkar R, Fu P, Mahran A, Rastinehad AR, Tewari A, Tirumani SH, Purysko A, Ponsky L, Madabhushi A. An integrated nomogram combining deep learning, Prostate Imaging-Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study. LANCET DIGITAL HEALTH 2021; 3:e445-e454. [PMID: 34167765 PMCID: PMC8261599 DOI: 10.1016/s2589-7500(21)00082-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 04/20/2021] [Accepted: 04/27/2021] [Indexed: 12/23/2022]
Abstract
Background Biparametric MRI (comprising T2-weighted MRI and apparent diffusion coefficient maps) is increasingly being used to characterise prostate cancer. Although previous studies have combined Prostate Imaging–Reporting & Data System (PI-RADS)-based MRI findings with routinely available clinical variables and with deep learning-based imaging predictors, respectively, for prostate cancer risk stratification, none have combined all three. We aimed to construct an integrated nomogram (referred to as ClaD) combining deep learning-based imaging predictions, PI-RADS scoring, and clinical variables to identify clinically significant prostate cancer on biparametric MRI. Methods In this retrospective multicentre study, we included patients with prostate cancer, with histopathology or biopsy reports and a screening or diagnostic MRI scan in the axial view, from four cohorts in the USA (from University Hospitals Cleveland Medical Center, Icahn School of Medicine at Mount Sinai, Cleveland Clinic, and Long Island Jewish Medical Center) and from the PROSTATEx Challenge dataset in the Netherlands. We constructed an integrated nomogram combining deep learning, PI-RADS score, and clinical variables (prostate-specific antigen, prostate volume, and lesion volume) using multivariable logistic regression to identify clinically significant prostate cancer on biparametric MRI. We used data from the first three cohorts to train the nomogram and data from the remaining two cohorts for independent validation. We compared the performance of our ClaD integrated nomogram with that of integrated nomograms combining clinical variables with either the deep learning-based imaging predictor (referred to as DIN) or PI-RADS score (referred to as PIN) using area under the receiver operating characteristic curves (AUCs). We also compared the ability of the nomograms to predict biochemical recurrence on a subset of patients who had undergone radical prostatectomy. We report cross-validation AUCs as means for the training set and used AUCs with 95% CIs to assess the performance on the test set. The difference in AUCs between the models were tested for statistical significance using DeLong’s test. We used log-rank tests and Kaplan-Meier curves to analyse survival. Findings We investigated 592 patients (823 lesions) with prostate cancer who underwent 3T multiparametric MRI at five hospitals in the USA between Jan 8, 2009, and June 3, 2017. The training data set consisted of 368 patients from three sites (the PROSTATEx Challenge cohort [n=204], University Hospitals Cleveland Medical Center [n=126], and Icahn School of Medicine at Mount Sinai [n=38]), and the independent validation data set consisted of 224 patients from two sites (Cleveland Clinic [n=151] and Long Island Jewish Medical Center [n=73]). The ClaD clinical nomogram yielded an AUC of 0·81 (95% CI 0·76–0·85) for identification of clinically significant prostate cancer in the validation data set, significantly improving performance over the DIN (0·74 [95% CI 0·69–0·80], p=0·0005) and PIN (0·76 [0·71–0·81], p<0·0001) nomograms. In the subset of patients who had undergone radical prostatectomy (n=81), the ClaD clinical nomogram resulted in a significant separation in Kaplan-Meier survival curves between patients with and without biochemical recurrence (HR 5·92 [2·34–15·00], p=0·044), whereas the DIN (1·22 [0·54–2·79], p=0·65) and PIN nomograms did not (1·30 [0·62–2·71], p=0·51). Interpretation Risk stratification of patients with prostate cancer using the integrated ClaD nomogram could help to identify patients with prostate cancer who are at low risk, very low risk, and favourable intermediate risk, who might be candidates for active surveillance, and could also help to identify patients with lethal prostate cancer who might benefit from adjuvant therapy. Funding National Cancer Institute of the US National Institutes of Health, National Institute for Biomedical Imaging and Bioengineering, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, US Department of Defense, US National Institute of Diabetes and Digestive and Kidney Diseases, The Ohio Third Frontier Technology Validation Fund, Case Western Reserve University, Dana Foundation, and Clinical and Translational Science Collaborative.
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Affiliation(s)
- Amogh Hiremath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Rakesh Shiradkar
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Amr Mahran
- Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | - Ashutosh Tewari
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sree Harsha Tirumani
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Andrei Purysko
- Department of Radiology and Nuclear Medicine, Cleveland Clinic, Cleveland, OH, USA
| | - Lee Ponsky
- Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA.
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36
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Chang R, Qi S, Yue Y, Zhang X, Song J, Qian W. Predictive Radiomic Models for the Chemotherapy Response in Non-Small-Cell Lung Cancer based on Computerized-Tomography Images. Front Oncol 2021; 11:646190. [PMID: 34307127 PMCID: PMC8293296 DOI: 10.3389/fonc.2021.646190] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 06/16/2021] [Indexed: 01/10/2023] Open
Abstract
The heterogeneity and complexity of non-small cell lung cancer (NSCLC) tumors mean that NSCLC patients at the same stage can have different chemotherapy prognoses. Accurate predictive models could recognize NSCLC patients likely to respond to chemotherapy so that they can be given personalized and effective treatment. We propose to identify predictive imaging biomarkers from pre-treatment CT images and construct a radiomic model that can predict the chemotherapy response in NSCLC. This single-center cohort study included 280 NSCLC patients who received first-line chemotherapy treatment. Non-contrast CT images were taken before and after the chemotherapy, and clinical information were collected. Based on the Response Evaluation Criteria in Solid Tumors and clinical criteria, the responses were classified into two categories: response (n = 145) and progression (n = 135), then all data were divided into two cohorts: training cohort (224 patients) and independent test cohort (56 patients). In total, 1629 features characterizing the tumor phenotype were extracted from a cube containing the tumor lesion cropped from the pre-chemotherapy CT images. After dimensionality reduction, predictive models of the chemotherapy response of NSCLC with different feature selection methods and different machine-learning classifiers (support vector machine, random forest, and logistic regression) were constructed. For the independent test cohort, the predictive model based on a random-forest classifier with 20 radiomic features achieved the best performance, with an accuracy of 85.7% and an area under the receiver operating characteristic curve of 0.941 (95% confidence interval, 0.898–0.982). Of the 20 selected features, four were first-order statistics of image intensity and the others were texture features. For nine features, there were significant differences between the response and progression groups (p < 0.001). In the response group, three features, indicating heterogeneity, were overrepresented and one feature indicating homogeneity was underrepresented. The proposed radiomic model with pre-chemotherapy CT features can predict the chemotherapy response of patients with non-small cell lung cancer. This radiomic model can help to stratify patients with NSCLC, thereby offering the prospect of better treatment.
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Affiliation(s)
- Runsheng Chang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.,Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Yong Yue
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaoye Zhang
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jiangdian Song
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Wei Qian
- Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX, United States
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Spohn SK, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021; 11:8027-8042. [PMID: 34335978 PMCID: PMC8315055 DOI: 10.7150/thno.61207] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/17/2021] [Indexed: 12/14/2022] Open
Abstract
Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics - a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.
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Affiliation(s)
- Simon K.B. Spohn
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
| | - Alisa S. Bettermann
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Michael Mix
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Nils H. Nicolay
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Tobias Fechter
- Department of Radiation Oncology - Division of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Tobias Hölscher
- Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- 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
| | - Radu Grosu
- Institute of Computer Engineering, Vienne University of Technology, Vienna, Austria
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele - Milan, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano - Milan, Italy
| | - Anca L. Grosu
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
- German Oncology Center, European University of Cyprus, Limassol, Cyprus
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Syer T, Mehta P, Antonelli M, Mallett S, Atkinson D, Ourselin S, Punwani S. Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies. Cancers (Basel) 2021; 13:3318. [PMID: 34282762 PMCID: PMC8268820 DOI: 10.3390/cancers13133318] [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: 05/28/2021] [Revised: 06/24/2021] [Accepted: 06/30/2021] [Indexed: 11/16/2022] Open
Abstract
Computer-aided diagnosis (CAD) of prostate cancer on multiparametric magnetic resonance imaging (mpMRI), using artificial intelligence (AI), may reduce missed cancers and unnecessary biopsies, increase inter-observer agreement between radiologists, and alleviate pressures caused by rising case incidence and a shortage of specialist radiologists to read prostate mpMRI. However, well-designed evaluation studies are required to prove efficacy above current clinical practice. A systematic search of the MEDLINE, EMBASE, and arXiv electronic databases was conducted for studies that compared CAD for prostate cancer detection or classification on MRI against radiologist interpretation and a histopathological reference standard, in treatment-naïve men with a clinical suspicion of prostate cancer. Twenty-seven studies were included in the final analysis. Due to substantial heterogeneities in the included studies, a narrative synthesis is presented. Several studies reported superior diagnostic accuracy for CAD over radiologist interpretation on small, internal patient datasets, though this was not observed in the few studies that performed evaluation using external patient data. Our review found insufficient evidence to suggest the clinical deployment of artificial intelligence algorithms at present. Further work is needed to develop and enforce methodological standards, promote access to large diverse datasets, and conduct prospective evaluations before clinical adoption can be considered.
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Affiliation(s)
- Tom Syer
- Centre for Medical Imaging, Division of Medicine, Bloomsbury Campus, University College London, London WC1E 6DH, UK; (T.S.); (S.M.); (D.A.)
| | - Pritesh Mehta
- Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, Bloomsbury Campus, University College London, London WC1E 6DH, UK;
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences and Medicine, St Thomas’ Campus, King’s College London, London SE1 7EH, UK; (M.A.); (S.O.)
| | - Sue Mallett
- Centre for Medical Imaging, Division of Medicine, Bloomsbury Campus, University College London, London WC1E 6DH, UK; (T.S.); (S.M.); (D.A.)
| | - David Atkinson
- Centre for Medical Imaging, Division of Medicine, Bloomsbury Campus, University College London, London WC1E 6DH, UK; (T.S.); (S.M.); (D.A.)
| | - Sébastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences and Medicine, St Thomas’ Campus, King’s College London, London SE1 7EH, UK; (M.A.); (S.O.)
| | - Shonit Punwani
- Centre for Medical Imaging, Division of Medicine, Bloomsbury Campus, University College London, London WC1E 6DH, UK; (T.S.); (S.M.); (D.A.)
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Singh A, Chitalia R, Kontos D. Radiogenomics in brain, breast, and lung cancer: opportunities and challenges. J Med Imaging (Bellingham) 2021; 8:031907. [PMID: 34164563 PMCID: PMC8212946 DOI: 10.1117/1.jmi.8.3.031907] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 06/04/2021] [Indexed: 01/06/2023] Open
Abstract
The field of radiogenomics largely focuses on developing imaging surrogates for genomic signatures and integrating imaging, genomic, and molecular data to develop combined personalized biomarkers for characterizing various diseases. Our study aims to highlight the current state-of-the-art and the role of radiogenomics in cancer research, focusing mainly on solid tumors, and is broadly divided into four sections. The first section reviews representative studies that establish the biologic basis of radiomic signatures using gene expression and molecular profiling information. The second section includes studies that aim to non-invasively predict molecular subtypes of tumors using radiomic signatures. The third section reviews studies that evaluate the potential to augment the performance of established prognostic signatures by combining complementary information encoded by radiomic and genomic signatures derived from cancer tumors. The fourth section includes studies that focus on ascertaining the biological significance of radiomic phenotypes. We conclude by discussing current challenges and opportunities in the field, such as the importance of coordination between imaging device manufacturers, regulatory organizations, health care providers, pharmaceutical companies, academic institutions, and physicians for the effective standardization of the results from radiogenomic signatures and for the potential use of these findings to improve precision care for cancer patients.
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Affiliation(s)
- Apurva Singh
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Rhea Chitalia
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Despina Kontos
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
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40
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Shiradkar R. Editorial for "Multiparametric MRI-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer". J Magn Reson Imaging 2021; 54:1231-1232. [PMID: 34050577 DOI: 10.1002/jmri.27747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 05/14/2021] [Indexed: 11/05/2022] Open
Affiliation(s)
- Rakesh Shiradkar
- Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
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41
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Twilt JJ, van Leeuwen KG, Huisman HJ, Fütterer JJ, de Rooij M. Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11060959. [PMID: 34073627 PMCID: PMC8229869 DOI: 10.3390/diagnostics11060959] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 12/14/2022] Open
Abstract
Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.
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Pecoraro M, Messina E, Bicchetti M, Carnicelli G, Del Monte M, Iorio B, La Torre G, Catalano C, Panebianco V. The future direction of imaging in prostate cancer: MRI with or without contrast injection. Andrology 2021; 9:1429-1443. [PMID: 33998173 DOI: 10.1111/andr.13041] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/23/2021] [Accepted: 05/05/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Multiparametric MRI (mpMRI) is the "state of the art" management tool for patients with suspicion of prostate cancer (PCa). The role of non-contrast MRI is investigated to move toward a more personalized, less invasive, and highly cost-effective PCa diagnostic workup. OBJECTIVE To perform a non-systematic review of the existing literature to highlight strength and flaws of performing non-contrast MRI, and to provide a critical overview of the international scientific production on the topic. MATERIALS AND METHODS Online databases (Medline, PubMed, and Web of Science) were searched for original articles, systematic review and meta-analysis, and expert opinion papers. RESULTS Several investigations have shown comparable diagnostic accuracy of biparametric (bpMRI) and mpMRI for the detection of PCa. The advantage of abandoning contrast-enhanced sequences improves operational logistics, lowering costs, acquisition time, and side effects. The main limitations of bpMRI are that most studies comparing non-contrast with contrast MRI come from centers with high expertise that might not be reproducible in the general community setting; besides, reduced protocols might be insufficient for estimation of the intra- and extra-prostatic extension and regional disease. The mentioned observations suggest that low-quality mpMRI for the general population might represent the main shortage to overcome. DISCUSSION Non-contrast MRI future trends are likely represented by PCa screening and the application of artificial intelligence (AI) tools. PCa screening is still a controversial topic; bpMRI has become one of the most promising diagnostic applications, as it is a more sensitive test for PCa early detection, compared to serum PSA level test. Also, AI applications and radiomic have been the object of several studies investigating PCa detection using bpMRI, showing encouraging results. CONCLUSION Today, the accessibility to MRI for early detection of PCa is a priority. Results from prospective, multicenter, multireader, and paired validation studies are needed to provide evidence supporting its role in the clinical practice.
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Affiliation(s)
- Martina Pecoraro
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Emanuele Messina
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Marco Bicchetti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Giorgia Carnicelli
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Maurizio Del Monte
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Beniamino Iorio
- Department of Surgical Sciences, "Tor Vergata" University of Rome, Rome, Italy
| | - Giuseppe La Torre
- Department of Public Health and Infectious Disease, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Carlo Catalano
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Valeria Panebianco
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
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Bai H, Xia W, Ji X, He D, Zhao X, Bao J, Zhou J, Wei X, Huang Y, Li Q, Gao X. Multiparametric Magnetic Resonance Imaging-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer. J Magn Reson Imaging 2021; 54:1222-1230. [PMID: 33970517 DOI: 10.1002/jmri.27678] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 04/17/2021] [Accepted: 04/19/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Preoperative prediction of extracapsular extension (ECE) of prostate cancer (PCa) is important to guide clinical decision-making and improve patient prognosis. PURPOSE To investigate the value of multiparametric magnetic resonance imaging (mpMRI)-based peritumoral radiomics for preoperative prediction of the presence of ECE. STUDY TYPE Retrospective. POPULATION Two hundred eighty-four patients with PCa from two centers (center 1: 226 patients; center 2: 58 patients). Cases from center 1 were randomly divided into training (158 patients) and internal validation (68 patients) sets. Cases from center 2 were assigned to the external validation set. FIELD STRENGTH/SEQUENCE A 3.0 T MRI scanners (three vendors). Sequence: Pelvic T2-weighted turbo/fast spin echo sequence and diffusion weighted echo planar imaging sequence. ASSESSMENT The peritumoral region (PTR) was obtained by 3-12 mm (half of the tumor length) 3D dilatation of the intratumoral region (ITR). Single-MRI radiomics signatures, mpMRI radiomics signatures, and integrated models, which combined clinical characteristics with the radiomics signatures were built. The discrimination ability was assessed by area under the receiver operating characteristic curve (AUC) in the internal and external validation sets. STATISTICAL TESTS Fisher's exact test, Mann-Whitney U-test, DeLong test. RESULTS The PTR radiomics signatures demonstrated significantly better performance than the corresponding ITR radiomics signatures (AUC: 0.674 vs. 0.554, P < 0.05 on T2-weighted, 0.652 vs. 0.546, P < 0.05 on apparent diffusion coefficient, 0.682 vs. 0.556 on mpMRI in the external validation set). The integrated models combining the PTR radiomics signature with clinical characteristics performed better than corresponding radiomics signatures in the internal validation set (eg. AUC: 0.718 vs. 0.671, P < 0.05 on mpMRI) but performed similar in the external validation set (eg. AUC: 0.684, vs. 0.682, P = 0.45 on mpMRI). DATA CONCLUSION The peritumoral radiomics can better predict the presence of ECE preoperatively compared with the intratumoral radiomics and may have better generalization than clinical characteristics. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Honglin Bai
- School of Biomedical Engineering (Suzhou), Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230026, China.,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Wei Xia
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Xuefu Ji
- The School of Electro-Optical Engineering, Changchun University of Science and Technology, Changchun, 130013, China
| | - Dong He
- Department of Urology, The First Affiliated Hospital of SooChow University, Suzhou, 215006, China
| | - Xingyu Zhao
- School of Biomedical Engineering (Suzhou), Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230026, China.,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Jie Bao
- Department of Radiology, The First Affiliated Hospital of SooChow University, Suzhou, 215006, China
| | - Jian Zhou
- Department of Radiology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Xuedong Wei
- Department of Urology, The First Affiliated Hospital of SooChow University, Suzhou, 215006, China
| | - Yuhua Huang
- Department of Urology, The First Affiliated Hospital of SooChow University, Suzhou, 215006, China
| | - Qiong Li
- Department of Radiology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Xin Gao
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.,Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
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Leger S, Zwanenburg A, Leger K, Lohaus F, Linge A, Schreiber A, Kalinauskaite G, Tinhofer I, Guberina N, Guberina M, Balermpas P, von der Grün J, Ganswindt U, Belka C, Peeken JC, Combs SE, Boeke S, Zips D, Richter C, Krause M, Baumann M, Troost EG, Löck S. Comprehensive Analysis of Tumour Sub-Volumes for Radiomic Risk Modelling in Locally Advanced HNSCC. Cancers (Basel) 2020; 12:cancers12103047. [PMID: 33086761 PMCID: PMC7589463 DOI: 10.3390/cancers12103047] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/07/2020] [Accepted: 10/13/2020] [Indexed: 12/19/2022] Open
Abstract
Simple Summary Radiomic risk models are usually based on imaging features, which are extracted from the entire gross tumour volume (GTVentire). This approach does not explicitly consider the complex biological structure of the tumours. Therefore, in this retrospective study, we investigated the prognostic value of radiomic analyses based on different tumour sub-volumes using computed tomography imaging of patients with locally advanced head and neck squamous cell carcinoma who were treated with primary radio-chemotherapy. The GTVentire was cropped by different margins to define the rim and corresponding core sub-volumes of the tumour. Furthermore, the best performing tumour rim sub-volume was extended into surrounding tissue with different margins. As a result, the models based on the 5 mm tumour rim and on the 3 mm extended rim sub-volume showed an improved performance compared to models based on the corresponding tumour core. This indicates that the consideration of tumour sub-volumes may help to improve radiomic risk models. Abstract Imaging features for radiomic analyses are commonly calculated from the entire gross tumour volume (GTVentire). However, tumours are biologically complex and the consideration of different tumour regions in radiomic models may lead to an improved outcome prediction. Therefore, we investigated the prognostic value of radiomic analyses based on different tumour sub-volumes using computed tomography imaging of patients with locally advanced head and neck squamous cell carcinoma. The GTVentire was cropped by different margins to define the rim and the corresponding core sub-volumes of the tumour. Subsequently, the best performing tumour rim sub-volume was extended into surrounding tissue with different margins. Radiomic risk models were developed and validated using a retrospective cohort consisting of 291 patients in one of the six Partner Sites of the German Cancer Consortium Radiation Oncology Group treated between 2005 and 2013. The validation concordance index (C-index) averaged over all applied learning algorithms and feature selection methods using the GTVentire achieved a moderate prognostic performance for loco-regional tumour control (C-index: 0.61 ± 0.04 (mean ± std)). The models based on the 5 mm tumour rim and on the 3 mm extended rim sub-volume showed higher median performances (C-index: 0.65 ± 0.02 and 0.64 ± 0.05, respectively), while models based on the corresponding tumour core volumes performed less (C-index: 0.59 ± 0.01). The difference in C-index between the 5 mm tumour rim and the corresponding core volume showed a statistical trend (p = 0.10). After additional prospective validation, the consideration of tumour sub-volumes may be a promising way to improve prognostic radiomic risk models.
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Affiliation(s)
- Stefan Leger
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, 01307 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (M.K.); (M.B.); (E.G.C.T.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 01307 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden of the German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus and Technische Universität Dresden, 01307 Dresden, Germany
- Correspondence:
| | - Alex Zwanenburg
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, 01307 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (M.K.); (M.B.); (E.G.C.T.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 01307 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden of the German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus and Technische Universität Dresden, 01307 Dresden, Germany
| | - Karoline Leger
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, 01307 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (M.K.); (M.B.); (E.G.C.T.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 01307 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden of the German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus and Technische Universität Dresden, 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
| | - Fabian Lohaus
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, 01307 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (M.K.); (M.B.); (E.G.C.T.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 01307 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden of the German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus and Technische Universität Dresden, 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
| | - Annett Linge
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, 01307 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (M.K.); (M.B.); (E.G.C.T.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 01307 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden of the German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus and Technische Universität Dresden, 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
| | - Andreas Schreiber
- Department of Radiotherapy, Hospital Dresden-Friedrichstadt, 01067 Dresden, Germany;
| | - Goda Kalinauskaite
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 10117 Berlin, Germany; (G.K.); (I.T.)
- Department of Radiooncology and Radiotherapy, Charité University Hospital, 10117 Berlin, Germany
| | - Inge Tinhofer
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 10117 Berlin, Germany; (G.K.); (I.T.)
- Department of Radiooncology and Radiotherapy, Charité University Hospital, 10117 Berlin, Germany
| | - Nika Guberina
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 45147 Essen, Germany; (N.G.); (M.G.)
- Department of Radiotherapy, University Hospital Essen, Medical Faculty, University of Duisburg-Essen, 45147 Essen, Germany
| | - Maja Guberina
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 45147 Essen, Germany; (N.G.); (M.G.)
- Department of Radiotherapy, University Hospital Essen, Medical Faculty, University of Duisburg-Essen, 45147 Essen, Germany
| | - Panagiotis Balermpas
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 60596 Frankfurt, Germany; (P.B.); (J.v.d.G.)
- Department of Radiotherapy and Oncology, Goethe-University Frankfurt, 60596 Frankfurt, Germany
| | - Jens von der Grün
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 60596 Frankfurt, Germany; (P.B.); (J.v.d.G.)
- Department of Radiotherapy and Oncology, Goethe-University Frankfurt, 60596 Frankfurt, Germany
| | - Ute Ganswindt
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 81377 Munich, Germany; (U.G.); (C.B.); (J.C.P.); (S.E.C.)
- Department of Radiation Oncology, Ludwig-Maximilians-Universität, 81377 Munich, Germany
- Clinical Cooperation Group, Personalized Radiotherapy in Head and Neck Cancer, Helmholtz Zentrum, 81377 Munich, Germany
- Department of Radiation Oncology, Medical University of Innsbruck, Anichstraße 35, A-6020 Innsbruck, Austria
| | - Claus Belka
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 81377 Munich, Germany; (U.G.); (C.B.); (J.C.P.); (S.E.C.)
- Department of Radiation Oncology, Ludwig-Maximilians-Universität, 81377 Munich, Germany
- Clinical Cooperation Group, Personalized Radiotherapy in Head and Neck Cancer, Helmholtz Zentrum, 81377 Munich, Germany
| | - Jan C. Peeken
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 81377 Munich, Germany; (U.G.); (C.B.); (J.C.P.); (S.E.C.)
- Department of Radiation Oncology, Technische Universität München, 81675 Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Stephanie E. Combs
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 81377 Munich, Germany; (U.G.); (C.B.); (J.C.P.); (S.E.C.)
- Department of Radiation Oncology, Technische Universität München, 81675 Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Simon Boeke
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 72076 Tübingen, Germany; (S.B.); (D.Z.)
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Tübingen, Eberhard Karls Universität Tübingen, 72076 Tübingen, Germany
| | - Daniel Zips
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 72076 Tübingen, Germany; (S.B.); (D.Z.)
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Tübingen, Eberhard Karls Universität Tübingen, 72076 Tübingen, Germany
| | - Christian Richter
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, 01307 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (M.K.); (M.B.); (E.G.C.T.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
- Institute of Radiooncology—OncoRay, Helmholtz-Zentrum Dresden—Rossendorf, 01328 Dresden, Germany
| | - Mechthild Krause
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, 01307 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (M.K.); (M.B.); (E.G.C.T.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 01307 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden of the German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus and Technische Universität Dresden, 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
- Institute of Radiooncology—OncoRay, Helmholtz-Zentrum Dresden—Rossendorf, 01328 Dresden, Germany
| | - Michael Baumann
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, 01307 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (M.K.); (M.B.); (E.G.C.T.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 01307 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden of the German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus and Technische Universität Dresden, 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
- Institute of Radiooncology—OncoRay, Helmholtz-Zentrum Dresden—Rossendorf, 01328 Dresden, Germany
- German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - 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, 01307 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (M.K.); (M.B.); (E.G.C.T.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 01307 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden of the German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus and Technische Universität Dresden, 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
- Institute of Radiooncology—OncoRay, Helmholtz-Zentrum Dresden—Rossendorf, 01328 Dresden, Germany
| | - Steffen Löck
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, 01307 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (M.K.); (M.B.); (E.G.C.T.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 01307 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden of the German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus and Technische Universität Dresden, 01307 Dresden, Germany
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