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Familiar AM, Fathi Kazerooni A, Vossough A, Ware JB, Bagheri S, Khalili N, Anderson H, Haldar D, Storm PB, Resnick AC, Kann BH, Aboian M, Kline C, Weller M, Huang RY, Chang SM, Fangusaro JR, Hoffman LM, Mueller S, Prados M, Nabavizadeh A. Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation. Neuro Oncol 2024; 26:1557-1571. [PMID: 38769022 PMCID: PMC11376457 DOI: 10.1093/neuonc/noae093] [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: 02/13/2024] [Indexed: 05/22/2024] Open
Abstract
MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumors from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.
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Affiliation(s)
- Ariana M Familiar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- AI2D Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Arastoo Vossough
- Division of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Jeffrey B Ware
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sina Bagheri
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nastaran Khalili
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Hannah Anderson
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Debanjan Haldar
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Phillip B Storm
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Adam C Resnick
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Benjamin H Kann
- Department of Radiation Oncology, Dana-Farber Cancer Institute | Brigham and Women's Hospital | Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mariam Aboian
- Division of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Cassie Kline
- Division of Oncology, Department of Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Susan M Chang
- Division of Neuro-Oncology, Department of Neurosurgery, University of California, San Francisco, California, USA
| | - Jason R Fangusaro
- The Aflac Cancer Center, Children's Healthcare of Atlanta and the Emory University School of Medicine, Atlanta, Georgia, USA
| | - Lindsey M Hoffman
- Division of Hematology/Oncology, Phoenix Children's Hospital, Phoenix, Arizona, USA
| | - Sabine Mueller
- Department of Neurology, Neurosurgery and Pediatrics, University of California, San Francisco, California, USA
| | - Michael Prados
- Department of Neurosurgery and Pediatrics, University of California, San Francisco, California, USA
| | - Ali Nabavizadeh
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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Li Y, Long W, Zhou H, Tan T, Xie H. Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method. Breast Cancer Res Treat 2024; 207:453-468. [PMID: 38853220 DOI: 10.1007/s10549-024-07375-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 05/14/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE This study aims to assess the diagnostic value of ultrasound habitat sub-region radiomics feature parameters using a fully connected neural networks (FCNN) combination method L2,1-norm in relation to breast cancer Ki-67 status. METHODS Ultrasound images from 528 cases of female breast cancer at the Affiliated Hospital of Xiangnan University and 232 cases of female breast cancer at the Affiliated Rehabilitation Hospital of Xiangnan University were selected for this study. We utilized deep learning methods to automatically outline the gross tumor volume and perform habitat clustering. Subsequently, habitat sub-regions were extracted to identify radiomics features and underwent feature engineering using the L1,2-norm. A prediction model for the Ki-67 status of breast cancer patients was then developed using a FCNN. The model's performance was evaluated using accuracy, area under the curve (AUC), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), Recall, and F1. In addition, calibration curves and clinical decision curves were plotted for the test set to visually assess the predictive accuracy and clinical benefit of the models. RESULT Based on the feature engineering using the L1,2-norm, a total of 9 core features were identified. The predictive model, constructed by the FCNN model based on these 9 features, achieved the following scores: ACC 0.856, AUC 0.915, Spe 0.843, PPV 0.920, NPV 0.747, Recall 0.974, and F1 0.890. Furthermore, calibration curves and clinical decision curves of the validation set demonstrated a high level of confidence in the model's performance and its clinical benefit. CONCLUSION Habitat clustering of ultrasound images of breast cancer is effectively supported by the combined implementation of the L1,2-norm and FCNN algorithms, allowing for the accurate classification of the Ki-67 status in breast cancer patients.
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Affiliation(s)
- Yanfeng Li
- Department of Interventional Vascular Surgery, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People's Republic of China
| | - Wengxing Long
- Department of Interventional Vascular Surgery, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People's Republic of China
| | - Hongda Zhou
- Department of Oncology, Affiliated Hospital of Xiangnan University, Chenzhou, 423000, Hunan, People's Republic of China
| | - Tao Tan
- Faulty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China
| | - Hui Xie
- Department of Oncology, Affiliated Hospital of Xiangnan University, Chenzhou, 423000, Hunan, People's Republic of China.
- Faulty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China.
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People's Republic of China.
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Belue MJ, Harmon SA, Chappidi S, Zhuge Y, Tasci E, Jagasia S, Joyce T, Camphausen K, Turkbey B, Krauze AV. Diagnosing Progression in Glioblastoma-Tackling a Neuro-Oncology Problem Using Artificial-Intelligence-Derived Volumetric Change over Time on Magnetic Resonance Imaging to Examine Progression-Free Survival in Glioblastoma. Diagnostics (Basel) 2024; 14:1374. [PMID: 39001264 PMCID: PMC11241823 DOI: 10.3390/diagnostics14131374] [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: 05/06/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 07/16/2024] Open
Abstract
Glioblastoma (GBM) is the most aggressive and the most common primary brain tumor, defined by nearly uniform rapid progression despite the current standard of care involving maximal surgical resection followed by radiation therapy (RT) and temozolomide (TMZ) or concurrent chemoirradiation (CRT), with an overall survival (OS) of less than 30% at 2 years. The diagnosis of tumor progression in the clinic is based on clinical assessment and the interpretation of MRI of the brain using Response Assessment in Neuro-Oncology (RANO) criteria, which suffers from several limitations including a paucity of precise measures of progression. Given that imaging is the primary modality that generates the most quantitative data capable of capturing change over time in the standard of care for GBM, this renders it pivotal in optimizing and advancing response criteria, particularly given the lack of biomarkers in this space. In this study, we employed artificial intelligence (AI)-derived MRI volumetric parameters using the segmentation mask output of the nnU-Net to arrive at four classes (background, edema, non-contrast enhancing tumor (NET), and contrast-enhancing tumor (CET)) to determine if dynamic changes in AI volumes detected throughout therapy can be linked to PFS and clinical features. We identified associations between MR imaging AI-generated volumes and PFS independently of tumor location, MGMT methylation status, and the extent of resection while validating that CET and edema are the most linked to PFS with patient subpopulations separated by district rates of change throughout the disease. The current study provides valuable insights for risk stratification, future RT treatment planning, and treatment monitoring in neuro-oncology.
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Affiliation(s)
- Mason J. Belue
- Artificial Intelligence Resource, Molecular Imaging Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (M.J.B.); (S.A.H.); (B.T.)
| | - Stephanie A. Harmon
- Artificial Intelligence Resource, Molecular Imaging Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (M.J.B.); (S.A.H.); (B.T.)
| | - Shreya Chappidi
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
- Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Ave., Cambridge CB3 0FD, UK
| | - Ying Zhuge
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
| | - Erdal Tasci
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
| | - Sarisha Jagasia
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
| | - Thomas Joyce
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
| | - Kevin Camphausen
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
| | - Baris Turkbey
- Artificial Intelligence Resource, Molecular Imaging Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (M.J.B.); (S.A.H.); (B.T.)
| | - Andra V. Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
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Styliara EI, Astrakas LG, Alexiou G, Xydis VG, Zikou A, Kafritsas G, Voulgaris S, Argyropoulou MI. Survival Outcome Prediction in Glioblastoma: Insights from MRI Radiomics. Curr Oncol 2024; 31:2233-2243. [PMID: 38668068 PMCID: PMC11048751 DOI: 10.3390/curroncol31040165] [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: 03/09/2024] [Revised: 04/10/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024] Open
Abstract
Background: Extracting multiregional radiomic features from multiparametric MRI for predicting pretreatment survival in isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) patients is a promising approach. Methods: MRI data from 49 IDH wild-type glioblastoma patients pre-treatment were utilized. Diffusion and perfusion maps were generated, and tumor subregions segmented. Radiomic features were extracted for each tissue type and map. Feature selection on 1862 radiomic features identified 25 significant features. The Cox proportional-hazards model with LASSO regularization was used to perform survival analysis. Internal and external validation used a 38-patient training cohort and an 11-patient validation cohort. Statistical significance was set at p < 0.05. Results: Age and six radiomic features (shape and first and second order) from T1W, diffusion, and perfusion maps contributed to the final model. Findings suggest that a small necrotic subregion, inhomogeneous vascularization in the solid non-enhancing subregion, and edema-related tissue damage in the enhancing and edema subregions are linked to poor survival. The model's C-Index was 0.66 (95% C.I. 0.54-0.80). External validation demonstrated good accuracy (AUC > 0.65) at all time points. Conclusions: Radiomics analysis, utilizing segmented perfusion and diffusion maps, provide predictive indicators of survival in IDH wild-type glioblastoma patients, revealing associations with microstructural and vascular heterogeneity in the tumor.
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Affiliation(s)
- Effrosyni I. Styliara
- Department of Radiology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (E.I.S.); (V.G.X.); (A.Z.); (M.I.A.)
| | - Loukas G. Astrakas
- Medical Physics Lab, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece;
| | - George Alexiou
- Department of Neurosurgery, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (G.K.); (S.V.)
| | - Vasileios G. Xydis
- Department of Radiology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (E.I.S.); (V.G.X.); (A.Z.); (M.I.A.)
| | - Anastasia Zikou
- Department of Radiology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (E.I.S.); (V.G.X.); (A.Z.); (M.I.A.)
| | - Georgios Kafritsas
- Department of Neurosurgery, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (G.K.); (S.V.)
| | - Spyridon Voulgaris
- Department of Neurosurgery, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (G.K.); (S.V.)
| | - Maria I. Argyropoulou
- Department of Radiology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (E.I.S.); (V.G.X.); (A.Z.); (M.I.A.)
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Khalighi S, Reddy K, Midya A, Pandav KB, Madabhushi A, Abedalthagafi M. Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. NPJ Precis Oncol 2024; 8:80. [PMID: 38553633 PMCID: PMC10980741 DOI: 10.1038/s41698-024-00575-0] [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: 10/19/2023] [Accepted: 03/13/2024] [Indexed: 04/02/2024] Open
Abstract
This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent a significant global health issue. AI has brought transformative innovations to brain tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, and treatment planning. Assessing its influence across all facets of malignant brain tumor management- diagnosis, prognosis, and therapy- AI models outperform human evaluations in terms of accuracy and specificity. Their ability to discern molecular aspects from imaging may reduce reliance on invasive diagnostics and may accelerate the time to molecular diagnoses. The review covers AI techniques, from classical machine learning to deep learning, highlighting current applications and challenges. Promising directions for future research include multimodal data integration, generative AI, large medical language models, precise tumor delineation and characterization, and addressing racial and gender disparities. Adaptive personalized treatment strategies are also emphasized for optimizing clinical outcomes. Ethical, legal, and social implications are discussed, advocating for transparency and fairness in AI integration for neuro-oncology and providing a holistic understanding of its transformative impact on patient care.
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Affiliation(s)
- Sirvan Khalighi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Kartik Reddy
- Department of Radiology, Emory University, Atlanta, GA, USA
| | - Abhishek Midya
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Krunal Balvantbhai Pandav
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Atlanta Veterans Administration Medical Center, Atlanta, GA, USA.
| | - Malak Abedalthagafi
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA.
- The Cell and Molecular Biology Program, Winship Cancer Institute, Atlanta, GA, USA.
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Yimit Y, Yasin P, Tuersun A, Abulizi A, Jia W, Wang Y, Nijiati M. Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach. Eur J Med Res 2023; 28:577. [PMID: 38071384 PMCID: PMC10709961 DOI: 10.1186/s40001-023-01550-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies. PURPOSE This study aims to evaluate the effectiveness of radiomics and machine learning techniques based on magnetic resonance imaging (MRI) to differentiate between CAE and BM. METHODS We retrospectively analyzed MRI images of 130 patients (30 CAE and 100 BM) from Xinjiang Medical University First Affiliated Hospital and The First People's Hospital of Kashi Prefecture, between January 2014 and December 2022. The dataset was divided into training (91 cases) and testing (39 cases) sets. Three dimensional tumors were segmented by radiologists from contrast-enhanced T1WI images on open resources software 3D Slicer. Features were extracted on Pyradiomics, further feature reduction was carried out using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO). Finally, we built five machine learning models, support vector machine, logistic regression, linear discrimination analysis, k-nearest neighbors classifier, and Gaussian naïve bias and evaluated their performance via several metrics including sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, accuracy and the area under the curve (AUC). RESULTS The area under curve (AUC) of support vector classifier (SVC), linear discrimination analysis (LDA), k-nearest neighbors (KNN), and gaussian naïve bias (NB) algorithms in training (testing) sets are 0.99 (0.94), 1.00 (0.87), 0.98 (0.92), 0.97 (0.97), and 0.98 (0.93), respectively. Nested cross-validation demonstrated the robustness and generalizability of the models. Additionally, the calibration plot and decision curve analysis demonstrated the practical usefulness of these models in clinical practice, with lower bias toward different subgroups during decision-making. CONCLUSION The combination of radiomics and machine learning approach based on contrast enhanced T1WI images could well distinguish CAE and BM. This approach holds promise in assisting doctors with accurate diagnosis and clinical decision-making.
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Affiliation(s)
- Yasen Yimit
- Medical Imaging Center, The First People's Hospital of Kashi (Kashgar) Prefecture, Kashi, 844000, People's Republic of China
| | - Parhat Yasin
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China
| | - Abuduresuli Tuersun
- Medical Imaging Center, The First People's Hospital of Kashi (Kashgar) Prefecture, Kashi, 844000, People's Republic of China
| | - Abudoukeyoumujiang Abulizi
- Medical Imaging Center, The First People's Hospital of Kashi (Kashgar) Prefecture, Kashi, 844000, People's Republic of China
| | - Wenxiao Jia
- Medical Imaging Center, Xinjiang Medical University Affiliated First Hospital, Urumqi, 830054, People's Republic of China
| | - Yunling Wang
- Medical Imaging Center, Xinjiang Medical University Affiliated First Hospital, Urumqi, 830054, People's Republic of China
| | - Mayidili Nijiati
- Medical Imaging Center, The First People's Hospital of Kashi (Kashgar) Prefecture, Kashi, 844000, People's Republic of China.
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He Z, Shen X, Wang B, Xu L, Ling Q. CT radiomics for noninvasively predicting NQO1 expression levels in hepatocellular carcinoma. PLoS One 2023; 18:e0290900. [PMID: 37695786 PMCID: PMC10495018 DOI: 10.1371/journal.pone.0290900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 08/18/2023] [Indexed: 09/13/2023] Open
Abstract
Using noninvasive radiomics to predict pathological biomarkers is an innovative work worthy of exploration. This retrospective cohort study aimed to analyze the correlation between NAD(P)H quinone oxidoreductase 1 (NQO1) expression levels and the prognosis of patients with hepatocellular carcinoma (HCC) and to construct radiomic models to predict the expression levels of NQO1 prior to surgery. Data of patients with HCC from The Cancer Genome Atlas (TCGA) and the corresponding arterial phase-enhanced CT images from The Cancer Imaging Archive were obtained for prognosis analysis, radiomic feature extraction, and model development. In total, 286 patients with HCC from TCGA were included. According to the cut-off value calculated using R, patients were divided into high-expression (n = 143) and low-expression groups (n = 143). Kaplan-Meier survival analysis showed that higher NQO1 expression levels were significantly associated with worse prognosis in patients with HCC (p = 0.017). Further multivariate analysis confirmed that high NQO1 expression was an independent risk factor for poor prognosis (HR = 1.761, 95% CI: 1.136-2.73, p = 0.011). Based on the arterial phase-enhanced CT images, six radiomic features were extracted, and a new bi-regional radiomics model was established, which could noninvasively predict higher NQO1 expression with good performance. The area under the curve (AUC) was 0.9079 (95% CI 0.8127-1.0000). The accuracy, sensitivity, and specificity were 0.86, 0.88, and 0.84, respectively, with a threshold value of 0.404. The data verification of our center showed that this model has good predictive efficiency, with an AUC of 0.8791 (95% CI 0.6979-1.0000). In conclusion, there existed a significant correlation between the CT image features and the expression level of NQO1, which could indirectly reflect the prognosis of patients with HCC. The predictive model based on arterial phase CT imaging features has good stability and diagnostic efficiency and is a potential means of identifying the expression level of NQO1 in HCC tissues before surgery.
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Affiliation(s)
- Zenglei He
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| | - Xiaoyong Shen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| | - Bin Wang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| | - Li Xu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| | - Qi Ling
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
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Tabassum M, Suman AA, Suero Molina E, Pan E, Di Ieva A, Liu S. Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review. Cancers (Basel) 2023; 15:3845. [PMID: 37568660 PMCID: PMC10417709 DOI: 10.3390/cancers15153845] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about tumors' features than can be obtained from the visual inspection of the images alone. Radiomics data can be analyzed to determine their correlation with a tumor's genetic status and grade, as well as in the assessment of its recurrence vs. therapeutic response, among other features. In consideration of the multi-parametric and high-dimensional space of features extracted by radiomics, machine learning can further improve tumor diagnosis, treatment response, and patients' prognoses. There is a growing recognition that tumors and their microenvironments (habitats) mutually influence each other-tumor cells can alter the microenvironment to increase their growth and survival. At the same time, habitats can also influence the behavior of tumor cells. In this systematic review, we investigate the current limitations and future developments in radiomics and machine learning in analysing brain tumors and their habitats.
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Affiliation(s)
- Mehnaz Tabassum
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia;
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| | - Abdulla Al Suman
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| | - Eric Suero Molina
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
- Department of Neurosurgery, University Hospital of Münster, 48149 Münster, Germany
| | - Elizabeth Pan
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| | - Sidong Liu
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia;
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
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