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Feng L, Yang X, Wang C, Zhang H, Wang W, Yang J. Predicting event-free survival after induction of remission in high-risk pediatric neuroblastoma: combining 123I-MIBG SPECT-CT radiomics and clinical factors. Pediatr Radiol 2024; 54:805-819. [PMID: 38492045 DOI: 10.1007/s00247-024-05901-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/29/2024] [Accepted: 03/02/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND Accurately quantifying event-free survival after induction of remission in high-risk neuroblastoma can lead to better subsequent treatment decisions, including whether more aggressive therapy or milder treatment is needed to reduce unnecessary treatment side effects, thereby improving patient survival. OBJECTIVE To develop and validate a 123I-metaiodobenzylguanidine (MIBG) single-photon emission computed tomography-computed tomography (SPECT-CT)-based radiomics nomogram and evaluate its value in predicting event-free survival after induction of remission in high-risk neuroblastoma. MATERIALS AND METHODS One hundred and seventy-two patients with high-risk neuroblastoma who underwent an 123I-MIBG SPECT-CT examination were retrospectively reviewed. Eighty-seven patients with high-risk neuroblastoma met the final inclusion and exclusion criteria and were randomized into training and validation cohorts in a 7:3 ratio. The SPECT-CT images of patients were visually analyzed to assess the Curie score. The 3D Slicer software tool was used to outline the region of interest of the lumbar 3-5 vertebral bodies on the SPECT-CT images. Radiomics features were extracted and screened, and a radiomics model was constructed with the selected radiomics features. Univariate and multivariate Cox regression analyses were used to determine clinical risk factors and construct the clinical model. The radiomics nomogram was constructed using multivariate Cox regression analysis by incorporating radiomics features and clinical risk factors. C-index and time-dependent receiver operating characteristic curves were used to evaluate the performance of the different models. RESULTS The Curie score had the lowest efficacy for the assessment of event-free survival, with a C-index of 0.576 and 0.553 in the training and validation cohorts, respectively. The radiomics model, constructed from 11 radiomics features, outperformed the clinical model in predicting event-free survival in both the training cohort (C-index, 0.780 vs. 0.653) and validation cohort (C-index, 0.687 vs. 0.667). The nomogram predicted the best prognosis for event-free survival in both the training and validation cohorts, with C-indices of 0.819 and 0.712, and 1-year areas under the curve of 0.899 and 0.748, respectively. CONCLUSION 123I-MIBG SPECT-CT-based radiomics can accurately predict the event-free survival of high-risk neuroblastoma after induction of remission The constructed nomogram may enable an individualized assessment of high-risk neuroblastoma prognosis and assist clinicians in optimizing patient treatment and follow-up plans, thereby potentially improving patient survival.
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Affiliation(s)
- Lijuan Feng
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Xu Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Chao Wang
- SinoUnion Healthcare Inc, Beijing, China
| | - Hui Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Wei Wang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Jigang Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China.
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Feng L, Zhou Z, Liu J, Yao S, Wang C, Zhang H, Xiong P, Wang W, Yang J. 18F-FDG PET/CT-Based Radiomics Nomogram for Prediction of Bone Marrow Involvement in Pediatric Neuroblastoma: A Two-Center Study. Acad Radiol 2024; 31:1111-1121. [PMID: 37643929 DOI: 10.1016/j.acra.2023.07.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 08/31/2023]
Abstract
RATIONALE AND OBJECTIVES To assess the predictive ability of an 18F-FDG PET/CT-based radiomics nomogram for bone marrow involvement in pediatric neuroblastoma. MATERIALS AND METHODS A total of 241 neuroblastoma patients who underwent 18F-FDG PET/CT at two medical centers were retrospectively evaluated. Data from center A (n = 200) were randomized into a training cohort (n = 140) and an internal validation cohort (n = 60), while data from center B (n = 41) constituted the external validation cohort. For each patient, two regions of interest were defined using the tumor and axial skeleton. The clinical factors and radiomics features were derived to construct the clinical and radiomics models. The radiomics nomogram was built by combining clinical factors and radiomics features. The area under the receiver operating characteristic curves (AUCs) were used to assess the performance of the models. RESULTS Radiomics models created from tumor and axial skeleton achieved AUCs of 0.773 and 0.900, and the clinical model had an AUC of 0.858 in the training cohort. By incorporating clinical risk factors and axial skeleton-based radiomics features, the AUC of the radiomics nomogram in the training cohort, internal validation cohort, and external validation cohort was 0.932, 0.887, and 0.733, respectively. CONCLUSION The axial skeleton-based radiomics model performed better than the tumor-based radiomics model in predicting bone marrow involvement. Moreover, the radiomics nomogram showed that combining axial skeleton-based radiomics features with clinical risk factors improved their performance.
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Affiliation(s)
- Lijuan Feng
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., Z.Z., J.L., W.W., J.Y.)
| | - Ziang Zhou
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., Z.Z., J.L., W.W., J.Y.)
| | - Jun Liu
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., Z.Z., J.L., W.W., J.Y.)
| | - Shuang Yao
- Department of Nuclear Medicine, Beijing Fengtai YouAnMen Hospital, Beijing, China (S.Y.)
| | - Chao Wang
- Department of Clinical Research, SinoUnion Healthcare Inc., Beijing, China (C.W.)
| | - Hui Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China (H.Z.)
| | - Pingxiang Xiong
- Nanchang Rimag Medical Diagnosis Center, Nanchang, China (P.X.)
| | - Wei Wang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., Z.Z., J.L., W.W., J.Y.)
| | - Jigang Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., Z.Z., J.L., W.W., J.Y.).
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Feng L, Zhang S, Lu X, Yang X, Kan Y, Wang C, Zhang H, Wang W, Yang J. An Optimal Radiomics Nomogram Based on 18F-FDG PET/CT for Identifying Event-Free Survival in Pediatric Neuroblastoma. Acad Radiol 2023; 30:2309-2320. [PMID: 37393177 DOI: 10.1016/j.acra.2023.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/13/2023] [Accepted: 06/02/2023] [Indexed: 07/03/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate whether the 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics features that combine tumor and bone marrow can more accurately identify event-free survival (EFS) in pediatric neuroblastoma. MATERIALS AND METHODS A total of 126 patients with neuroblastoma were retrospectively included and randomly divided into the training and validation cohorts (7:3 ratio). Radiomics features were extracted to develop a tumor- and bone marrow-based radiomics risk score (RRS). The Kaplan-Meier method was used to evaluate the effectiveness of RRS in EFS risk stratification. Univariate and multivariate Cox regression analyses were used to determine independent clinical risk factors and construct the clinical models. The conventional PET model was constructed based on conventional PET parameters, and the noninvasive combined model integrated the RRS and the noninvasive independent clinical risk factors. The performance of the models was evaluated using C-index, calibration curves, and decision curve analysis (DCA). RESULTS A total of 15 radiomics features were selected to build the RRS. According to Kaplan-Meier analysis, there was a significant difference in EFS between the low-risk and high-risk groups as defined by the value of RRS (P < .05). The noninvasive combined model combining RRS and the International Neuroblastoma Risk Group stage achieved the best prognostic prediction of EFS, with a C-index of 0.810 and 0.783 in the training and validation cohorts, respectively. The calibration curves and DCA indicated that the noninvasive combined model had good consistency and clinical utility. CONCLUSION The 18F-FDG PET/CT-based radiomics of neuroblastoma allows a reliable evaluation of EFS. The performance of the noninvasive combined model was superior to the clinical and conventional PET models.
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Affiliation(s)
- Lijuan Feng
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.)
| | - Shuxin Zhang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.)
| | - Xia Lu
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.)
| | - Xu Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.)
| | - Ying Kan
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.)
| | - Chao Wang
- SinoUnion Healthcare Inc., Beijing, China (C.W.)
| | - Hui Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China (H,Z,)
| | - Wei Wang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.)
| | - Jigang Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.).
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Menon N, Guidozzi N, Chidambaram S, Markar SR. Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy. Dis Esophagus 2023; 36:doad034. [PMID: 37236811 PMCID: PMC10789236 DOI: 10.1093/dote/doad034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 05/04/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023]
Abstract
Radiomics can interpret radiological images with more detail and in less time compared to the human eye. Some challenges in managing esophageal cancer can be addressed by incorporating radiomics into image interpretation, treatment planning, and predicting response and survival. This systematic review and meta-analysis provides a summary of the evidence of radiomics in esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE, and Ovid EMBASE databases-articles describing radiomics in esophageal cancer were included. A meta-analysis was also performed; 50 studies were included. For the assessment of treatment response using 18F-FDG PET/computed tomography (CT) scans, seven studies (443 patients) were included in the meta-analysis. The pooled sensitivity and specificity were 86.5% (81.1-90.6) and 87.1% (78.0-92.8). For the assessment of treatment response using CT scans, five studies (625 patients) were included in the meta-analysis, with a pooled sensitivity and specificity of 86.7% (81.4-90.7) and 76.1% (69.9-81.4). The remaining 37 studies formed the qualitative review, discussing radiomics in diagnosis, radiotherapy planning, and survival prediction. This review explores the wide-ranging possibilities of radiomics in esophageal cancer management. The sensitivities of 18F-FDG PET/CT scans and CT scans are comparable, but 18F-FDG PET/CT scans have improved specificity for AI-based prediction of treatment response. Models integrating clinical and radiomic features facilitate diagnosis and survival prediction. More research is required into comparing models and conducting large-scale studies to build a robust evidence base.
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Affiliation(s)
- Nainika Menon
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
| | - Nadia Guidozzi
- Department of General Surgery, University of Witwatersrand, Johannesburg, South Africa
| | - Swathikan Chidambaram
- Academic Surgical Unit, Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London, UK
| | - Sheraz Rehan Markar
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
- Nuffield Department of Surgery, University of Oxford, Oxford, UK
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Taber P, Armin JS, Orozco G, Del Fiol G, Erdrich J, Kawamoto K, Israni ST. Artificial Intelligence and Cancer Control: Toward Prioritizing Justice, Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support Technologies. Curr Oncol Rep 2023; 25:387-424. [PMID: 36811808 DOI: 10.1007/s11912-023-01376-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/06/2022] [Indexed: 02/24/2023]
Abstract
PURPOSE FOR REVIEW This perspective piece has two goals: first, to describe issues related to artificial intelligence-based applications for cancer control as they may impact health inequities or disparities; and second, to report on a review of systematic reviews and meta-analyses of artificial intelligence-based tools for cancer control to ascertain the extent to which discussions of justice, equity, diversity, inclusion, or health disparities manifest in syntheses of the field's best evidence. RECENT FINDINGS We found that, while a significant proportion of existing syntheses of research on AI-based tools in cancer control use formal bias assessment tools, the fairness or equitability of models is not yet systematically analyzable across studies. Issues related to real-world use of AI-based tools for cancer control, such as workflow considerations, measures of usability and acceptance, or tool architecture, are more visible in the literature, but still addressed only in a minority of reviews. Artificial intelligence is poised to bring significant benefits to a wide range of applications in cancer control, but more thorough and standardized evaluations and reporting of model fairness are required to build the evidence base for AI-based tool design for cancer and to ensure that these emerging technologies promote equitable healthcare.
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Affiliation(s)
- Peter Taber
- Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Salt Lake City, UT, 84108, USA.
| | - Julie S Armin
- Department of Family and Community Medicine, University of Arizona College of Medicine, Tucson, AZ, USA
| | | | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Salt Lake City, UT, 84108, USA
| | - Jennifer Erdrich
- Division of Surgical Oncology, University of Arizona College of Medicine, Tucson, AZ, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Salt Lake City, UT, 84108, USA
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Pakkasjärvi N, Luthra T, Anand S. Artificial Intelligence in Surgical Learning. Surgeries 2023; 4:86-97. [DOI: 10.3390/surgeries4010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
(1) Background: Artificial Intelligence (AI) is transforming healthcare on all levels. While AI shows immense potential, the clinical implementation is lagging. We present a concise review of AI in surgical learning; (2) Methods: A non-systematic review of AI in surgical learning of the literature in English is provided; (3) Results: AI shows utility for all components of surgical competence within surgical learning. AI presents with great potential within robotic surgery specifically (4) Conclusions: Technology will evolve in ways currently unimaginable, presenting us with novel applications of AI and derivatives thereof. Surgeons must be open to new modes of learning to be able to implement all evidence-based applications of AI in the future. Systematic analyses of AI in surgical learning are needed.
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Nakanishi R, Morooka K, Omori K, Toyota S, Tanaka Y, Hasuda H, Koga N, Nonaka K, Hu Q, Nakaji Y, Nakanoko T, Ando K, Ota M, Kimura Y, Oki E, Oda Y, Yoshizumi T. Artificial Intelligence-Based Prediction of Recurrence after Curative Resection for Colorectal Cancer from Digital Pathological Images. Ann Surg Oncol 2022; 30:3506-3514. [PMID: 36512260 DOI: 10.1245/s10434-022-12926-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 10/19/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND To develop an artificial intelligence-based model to predict recurrence after curative resection for stage I-III colorectal cancer from digitized pathological slides. PATIENTS AND METHODS In this retrospective study, 471 consecutive patients who underwent curative resection for stage I-III colorectal cancer at our institution from 2004 to 2015 were enrolled, and 512 randomly selected tiles from digitally scanned images of hematoxylin and eosin-stained tumor tissue sections were used to train a convolutional neural network. Five-fold cross-validation was used to validate the model. The association between recurrence and the model's output scores were analyzed in the test cohorts. RESULTS The area under the receiver operating characteristic curve of the cross-validation was 0.7245 [95% confidence interval (CI) 0.6707-0.7783; P < 0.0001]. The score successfully classified patients into those with better and worse recurrence free survival (P < 0.0001). Multivariate analysis revealed that a high score was significantly associated with worse recurrence free survival [odds ratio (OR) 1.857; 95% CI 1.248-2.805; P = 0.0021], which was independent from other predictive factors: male sex (P = 0.0238), rectal cancer (P = 0.0396), preoperative abnormal carcinoembryonic antigen (CEA) level (P = 0.0216), pathological T3/T4 stage (P = 0.0162), and pathological positive lymph node metastasis (P < 0.0001). CONCLUSIONS The artificial intelligence-based prediction model discriminated patients with a high risk of recurrence. This approach could help decision-makers consider the benefits of adjuvant chemotherapy.
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Feng L, Yang X, Lu X, Kan Y, Wang C, Zhang H, Wang W, Yang J. Diagnostic Value of 18F-FDG PET/CT-Based Radiomics Nomogram in Bone Marrow Involvement of Pediatric Neuroblastoma. Acad Radiol 2022; 30:940-951. [PMID: 36117128 DOI: 10.1016/j.acra.2022.08.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/06/2022] [Accepted: 08/16/2022] [Indexed: 11/17/2022]
Abstract
OBJECTIVES To develop and validate an 18F-FDG PET/CT-based radiomics nomogram and evaluate the value of the 18F-FDG PET/CT-based radiomics nomogram for the diagnosis of bone marrow involvement (BMI) in pediatric neuroblastoma. MATERIALS AND METHODS A total of 144 patients with neuroblastoma (100 in the training cohort and 44 in the validation cohort) were retrospectively included. The PET/CT images of patients were visually assessed. The results of bone marrow aspirates or biopsies were used as the gold standard for BMI. Radiomics features and conventional PET parameters were extracted using the 3D slicer. Features were selected by the least absolute shrinkage and selection operator regression, and radiomics signature was constructed. Univariate and multivariate logistic regression analyses were applied to identify the independent clinical risk factors and construct the clinical model. Other different models, including the conventional PET model, combined PET-clinical model and combined radiomics model, were built using logistic regression. The combined radiomics model was based on clinical factors, conventional PET parameters and radiomics signature, which was presented as a radiomics nomogram. The diagnostic performance of the different models was evaluated by receiver operating characteristic (ROC) curves and decision curve analysis (DCA). RESULTS By visual assessment, BMI was observed in 80 patients. Four conventional PET parameters (SUVmax, SUVmean, metabolic tumor volume, and total lesion glycolysis) were extracted. And 15 radiomics features were selected to build the radiomics signature. The 11q aberration, neuron-specific enolase and vanillylmandelic acid were identified as the independent clinical risk factors to establish the clinical model. The radiomics nomogram incorporating the radiomics signature, the independent clinical risk factors and SUVmean demonstrated the best diagnostic value for identifying BMI, with an area under the curve (AUC) of 0.963 and 0.931 in the training and validation cohorts, respectively. And the DCA demonstrated that the radiomics nomogram was clinically useful. CONCLUSION The 18F-FDG PET/CT-based radiomics nomogram which incorporates radiomics signature, independent clinical risk factors and conventional PET parameters could improve the diagnostic performance for BMI of pediatric neuroblastoma without additional medical costs and radiation exposure.
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Affiliation(s)
- Lijuan Feng
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China
| | - Xu Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China
| | - Xia Lu
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China
| | - Ying Kan
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China
| | - Chao Wang
- Sinounion Medical Technology (Beijing) Co., Ltd. Beijing, China
| | - Hui Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Wei Wang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China
| | - Jigang Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China.
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Wenderott K, Gambashidze N, Weigl M. Integration of artificial intelligence into sociotechnical work systems — Effects of artificial intelligence solutions in medical imaging on clinical efficiency: Protocol for a systematic literature review (Preprint). JMIR Res Protoc 2022; 11:e40485. [DOI: 10.2196/40485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/16/2022] [Accepted: 10/20/2022] [Indexed: 11/07/2022] Open
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Pellat A, Dohan A, Soyer P, Veziant J, Coriat R, Barret M. The Role of Magnetic Resonance Imaging in the Management of Esophageal Cancer. Cancers (Basel) 2022; 14:cancers14051141. [PMID: 35267447 PMCID: PMC8909473 DOI: 10.3390/cancers14051141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 02/16/2022] [Accepted: 02/18/2022] [Indexed: 02/01/2023] Open
Abstract
Esophageal cancer (EC) is the eighth more frequent cancer worldwide, with a poor prognosis. Initial staging is critical to decide on the best individual treatment approach. Current modalities for the assessment of EC are irradiating techniques, such as computed tomography (CT) and positron emission tomography/CT, or invasive techniques, such as digestive endoscopy and endoscopic ultrasound. Magnetic resonance imaging (MRI) is a non-invasive and non-irradiating imaging technique that provides high degrees of soft tissue contrast, with good depiction of the esophageal wall and the esophagogastric junction. Various sequences of MRI have shown good performance in initial tumor and lymph node staging in EC. Diffusion-weighted MRI has also demonstrated capabilities in the evaluation of tumor response to chemoradiotherapy. To date, there is not enough data to consider whole body MRI as a routine investigation for the detection of initial metastases or for prediction of distant recurrence. This narrative review summarizes the current knowledge on MRI for the management of EC.
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Affiliation(s)
- Anna Pellat
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint Jacques, 75014 Paris, France; (A.P.); (R.C.)
- Université de Paris, 75006 Paris, France; (A.D.); (P.S.); (J.V.)
| | - Anthony Dohan
- Université de Paris, 75006 Paris, France; (A.D.); (P.S.); (J.V.)
- Department of Radiology, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Philippe Soyer
- Université de Paris, 75006 Paris, France; (A.D.); (P.S.); (J.V.)
- Department of Radiology, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Julie Veziant
- Université de Paris, 75006 Paris, France; (A.D.); (P.S.); (J.V.)
- Department of Digestive Surgery, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Romain Coriat
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint Jacques, 75014 Paris, France; (A.P.); (R.C.)
- Université de Paris, 75006 Paris, France; (A.D.); (P.S.); (J.V.)
| | - Maximilien Barret
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint Jacques, 75014 Paris, France; (A.P.); (R.C.)
- Université de Paris, 75006 Paris, France; (A.D.); (P.S.); (J.V.)
- Correspondence:
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Chidambaram S, Sounderajah V, Maynard N, Markar SR. ASO Author Reflections: Applications of Artificial Intelligence in Oesophago-Gastric Malignancies-Present Work and Future Directions. Ann Surg Oncol 2021; 29:1991-1992. [PMID: 34792695 PMCID: PMC8810447 DOI: 10.1245/s10434-021-10907-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 11/18/2022]
Abstract
Our paper highlights the use of artificial intelligence (AI) in oesophageal and gastric malignancies with acceptable levels of accuracy for both diagnostic and surveillance purposes. Here, we comment on the past, present and future work necessary for incorporating AI into the clinical framework and practice.
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Affiliation(s)
| | - Viknesh Sounderajah
- Department of Surgery and Cancer, Imperial College London, London, UK.,Institute of Global Health Innovation, Imperial College London, London, UK
| | - Nick Maynard
- Department of Surgery, Churchill Hospital, Oxford University Hospitals NHS Trust, Oxford, UK
| | - Sheraz R Markar
- Department of Surgery and Cancer, Imperial College London, London, UK. .,Department of Surgery, Churchill Hospital, Oxford University Hospitals NHS Trust, Oxford, UK. .,Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
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