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Jiang L, Zhou Y, Miao W, Zhu H, Zou N, Tian Y, Pan H, Jin W, Huang J, Luo Q. Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules. Ann Med 2024; 56:2405075. [PMID: 39297299 DOI: 10.1080/07853890.2024.2405075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 09/21/2024] Open
Abstract
INTRODUCTION Artificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters for invasiveness prediction. METHODS Patients with stage 0-IB NSCLC after surgical resection were retrospectively analysed. Preoperative CTs were evaluated with specialized software for nodule segmentation and CT quantification. Pathology was the reference for invasiveness. Univariate and multivariate logistic regression assessed predictors of high-risk SPN. RESULTS Three hundred and fifty-five SPN were included. On multivariate analysis, CT value mean and nodule type (ground glass opacity vs. solid) were independent predictors of high-risk SPN. The area under the curve (AUC) was 0.811 for identifying high-risk nodules. CONCLUSIONS Quantitative CT measures and nodule type correlated with invasiveness. Software-based CT assessment shows potential for noninvasive prediction to guide extent of resection. Further prospective validation is needed, including comparison with benign nodules.
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Affiliation(s)
- Long Jiang
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Zhou
- Department of Purchasing Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wang Miao
- Department of Thoracic Surgery, The Third People's Hospital of Zhengzhou, Zhengzhou, China
| | - Hongda Zhu
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ningyuan Zou
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Tian
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hanbo Pan
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiqiu Jin
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jia Huang
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qingquan Luo
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Xie PY, Zeng ZM, Li ZH, Niu KX, Xia T, Ma DC, Fu S, Zhu JY, Li B, Zhu P, Xie SD, Meng XC. MRI-based radiomics for stratifying recurrence risk of early-onset rectal cancer: a multicenter study. ESMO Open 2024; 9:103735. [PMID: 39368416 DOI: 10.1016/j.esmoop.2024.103735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 07/19/2024] [Accepted: 07/20/2024] [Indexed: 10/07/2024] Open
Abstract
BACKGROUND Early-onset rectal cancer (EORC) is characterized by a unique disease process with different clinicopathological features compared with late-onset rectal cancer (LORC). Research on the risk of recurrence in EORC patients, however, is limited. We aim to develop a predictive model to accurately predict EORC recurrence risk. MATERIALS AND METHODS Rectal cancer patients who underwent radical surgery and T2-weighted imaging and diffusion-weighted imaging magnetic resonance imaging (MRI) were retrospectively enrolled from three medical institutions from November 2012 to November 2018. Differences in clinicopathological characteristics between EORC and LORC were compared. Five prediction models for disease-free survival were constructed based on clinicopathological variables and five radiomic features from pretreatment MRI of the EORC. A fixed cut-off value calculated in the training set was used to stratify EORC patients into high-risk and low-risk groups of post-operative recurrence. Model performance was evaluated by concordance index (C-index) and receiver operating characteristic curve. RESULTS A total of 264 EORC patients (median age, 43 years, 163 males) and 778 LORC patients (median age, 62 years, 520 males) were enrolled. Pretreatment positive carcinoembryonic antigen [hazard ratio (HR) = 2.84, P = 0.006], pathological positive lymph node status (pN positive) [HR = 2.86, P = 0.011] and MRI-based radiomics score [HR = 2.72, P < 0.001] are independent risk factors for disease-free survival in EORC patients. The EORC-ClinPathRadiom model, constructed by integrating the clinicopathological characteristics and MRI-based radiomics features of EORC, showed C-index of 0.82, 0.82, and 0.81 in the training, internal, and external test sets, respectively. This model effectively stratified EORC patients into high risk and low risk of recurrence (HRs for the training, internal, and external test sets were 8.96, 6.81, and 7.46, respectively). CONCLUSION The EORC-ClinPathRadiom model can effectively predict and stratify the risk of post-operative recurrence in EORC patients.
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Affiliation(s)
- P-Y Xie
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Z-M Zeng
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Z-H Li
- Department of Radiology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, People's Republic of China
| | - K-X Niu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - T Xia
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - D-C Ma
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - S Fu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - J-Y Zhu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - B Li
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - P Zhu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - S-D Xie
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
| | - X-C Meng
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
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Jabal MS, Mohammed MA, Nesvick CL, Kobeissi H, Graffeo CS, Pollock BE, Brinjikji W. DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery. AJNR Am J Neuroradiol 2024; 45:1521-1527. [PMID: 39266257 PMCID: PMC11448972 DOI: 10.3174/ajnr.a8351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 05/09/2024] [Indexed: 09/14/2024]
Abstract
BACKGROUND AND PURPOSE Stereotactic radiosurgery is a key treatment modality for cerebral AVMs, particularly for small lesions and those located in eloquent brain regions. Predicting obliteration remains challenging due to evolving treatment paradigms and complex AVM presentations. With digital subtraction angiography (DSA) being the gold standard for outcome evaluation, radiomic approaches offer potential for more objective and detailed analysis. We aimed to develop machine learning modeling using DSA quantitative features for post-SRS obliteration prediction. MATERIALS AND METHODS A prospective registry of patients with cerebral AVMs was screened to include patients with digital prestereotactic radiosurgery DSA. Anterior-posterior and lateral views were retrieved and manually segmented. Quantitative features were computed from the lesion ROI. Following feature selection, machine learning models were developed to predict unsuccessful 2-year total obliteration using processed radiomics features in comparison with clinical and radiosurgical features. When we evaluated through area under the receiver operating characteristic curve (AUROC), accuracy, area under the precision-recall curve F1, recall, and precision, the best performing model predictions on the test set were interpreted using the Shapley additive explanations approach. RESULTS DSA images of 100 included patients were retrieved and analyzed. The best-performing clinical radiosurgical model was a gradient boosting classifier with an AUROC of 68% and a recall of 67%. When we used radiomics variables as input, the AdaBoost classifier had the best evaluation metrics with an AUROC of 79% and a recall of 75%. The most important clinico-radiosurgical features, ranked by model contribution, were lesion volume, patient age, treatment dose rate, the presence of seizure at presentation, and prior resection. The most important ranked radiomics features were the following: gray-level size zone matrix, gray-level nonuniformity, kurtosis, sphericity, skewness, and gray-level dependence matrix dependence nonuniformity. CONCLUSIONS The combination of radiomics with machine learning is a promising approach for predicting cerebral AVM obliteration status following stereotactic radiosurgery. DSA could enhance prognostication of stereotactic radiosurgery-treated AVMs due to its high spatial resolution. Model interpretation is essential for building transparent models and establishing clinically valid radiomic signatures.
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Affiliation(s)
- Mohamed Sobhi Jabal
- From the Department of Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota
- Department of Computer and Information Science (M.S.J.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Marwa A Mohammed
- From the Department of Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota
| | - Cody L Nesvick
- Department of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota
| | - Hassan Kobeissi
- From the Department of Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota
| | - Christopher S Graffeo
- Department of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota
| | - Bruce E Pollock
- Department of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota
| | - Waleed Brinjikji
- From the Department of Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota
- Department of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota
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Doganay MT, Chakraborty P, Bommakanti SM, Jammalamadaka S, Battalapalli D, Madabhushi A, Draz MS. Artificial intelligence performance in testing microfluidics for point-of-care. LAB ON A CHIP 2024. [PMID: 39360887 PMCID: PMC11448392 DOI: 10.1039/d4lc00671b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Accepted: 09/16/2024] [Indexed: 10/06/2024]
Abstract
Artificial intelligence (AI) is revolutionizing medicine by automating tasks like image segmentation and pattern recognition. These AI approaches support seamless integration with existing platforms, enhancing diagnostics, treatment, and patient care. While recent advancements have demonstrated AI superiority in advancing microfluidics for point of care (POC) diagnostics, a gap remains in comparative evaluations of AI algorithms in testing microfluidics. We conducted a comparative evaluation of AI models specifically for the two-class classification problem of identifying the presence or absence of bubbles in microfluidic channels under various imaging conditions. Using a model microfluidic system with a single channel loaded with 3D transparent objects (bubbles), we challenged each of the tested machine learning (ML) (n = 6) and deep learning (DL) (n = 9) models across different background settings. Evaluation revealed that the random forest ML model achieved 95.52% sensitivity, 82.57% specificity, and 97% AUC, outperforming other ML algorithms. Among DL models suitable for mobile integration, DenseNet169 demonstrated superior performance, achieving 92.63% sensitivity, 92.22% specificity, and 92% AUC. Remarkably, DenseNet169 integration into a mobile POC system demonstrated exceptional accuracy (>0.84) in testing microfluidics at under challenging imaging settings. Our study confirms the transformative potential of AI in healthcare, emphasizing its capacity to revolutionize precision medicine through accurate and accessible diagnostics. The integration of AI into healthcare systems holds promise for enhancing patient outcomes and streamlining healthcare delivery.
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Affiliation(s)
- Mert Tunca Doganay
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
| | - Purbali Chakraborty
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
| | - Sri Moukthika Bommakanti
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
| | - Soujanya Jammalamadaka
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
| | | | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
- Atlanta Veterans Administration Medical Center, Atlanta, GA, USA
| | - Mohamed S Draz
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, 44106, USA
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Jia X, Carter BW, Duffton A, Harris E, Hobbs R, Li H. Advancing the Collaboration Between Imaging and Radiation Oncology. Semin Radiat Oncol 2024; 34:402-417. [PMID: 39271275 PMCID: PMC11407744 DOI: 10.1016/j.semradonc.2024.07.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
The fusion of cutting-edge imaging technologies with radiation therapy (RT) has catalyzed transformative breakthroughs in cancer treatment in recent decades. It is critical for us to review our achievements and preview into the next phase for future synergy between imaging and RT. This paper serves as a review and preview for fostering collaboration between these two domains in the forthcoming decade. Firstly, it delineates ten prospective directions ranging from technological innovations to leveraging imaging data in RT planning, execution, and preclinical research. Secondly, it presents major directions for infrastructure and team development in facilitating interdisciplinary synergy and clinical translation. We envision a future where seamless integration of imaging technologies into RT will not only meet the demands of RT but also unlock novel functionalities, enhancing accuracy, efficiency, safety, and ultimately, the standard of care for patients worldwide.
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Affiliation(s)
- Xun Jia
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD..
| | - Brett W Carter
- Department of Thoracic Imaging, Division of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Aileen Duffton
- Beatson West of Scotland Cancer Centre, Glasgow, UK.; Institute of Cancer Science, University of Glasgow, UK
| | - Emma Harris
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London, UK
| | - Robert Hobbs
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Heng Li
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
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Yang Y, Yuan Z, Ren Q, Wang J, Guan S, Tang X, Jiang Q, Meng X. Machine Learning-Enabled Fuhrman Grade in Clear-cell Renal Carcinoma Prediction Using Two-dimensional Ultrasound Images. ULTRASOUND IN MEDICINE & BIOLOGY 2024:S0301-5629(24)00330-2. [PMID: 39317624 DOI: 10.1016/j.ultrasmedbio.2024.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 08/22/2024] [Accepted: 08/24/2024] [Indexed: 09/26/2024]
Abstract
OBJECTIVE Accurate assessment of Fuhrman grade is crucial for optimal clinical management and personalized treatment strategies in patients with clear cell renal cell carcinoma (CCRCC). In this study, we developed a predictive model using ultrasound (US) images to accurately predict the Fuhrman grade. METHODS Between March 2013 and July 2023, a retrospective analysis was conducted on the US imaging and clinical data of 235 patients with pathologically confirmed CCRCC, including 67 with Fuhrman grades Ⅲ and Ⅳ. This study included 201 patients from Hospital A who were divided into training set (n = 161) and an internal validation set (n = 40) in an 8:2 ratio. Additionally, 34 patients from Hospital B were included for external validation. US images were delineated using ITK software, and radiomics features were extracted using PyRadiomics software. Subsequently, separate models for clinical factors, radiomics features, and their combinations were constructed. The model's performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA). RESULTS In total, 235 patients diagnosed with CCRCC, comprising 168 low-grade and 67 high-grade tumors, were included in this study. A comparison of the predictive performances of different models revealed that the logistic regression model exhibited relatively good stability and robustness. The AUC of the combined model for the training, internal validation and external validation sets were 0.871, 0.785 and 0.826, respectively, which were higher than those of the clinical and imaging histology models. Furthermore, the calibration curve demonstrated excellent concordance between the predicted Fuhrman grade probability of CCRCC using the combined model and the observed values in both the training and validation sets. Additionally, within the threshold range of 0-0.93, the combined model demonstrated substantial clinical utility, as evidenced by DCA. CONCLUSION The application of US radiomics techniques enabled objective prediction of Fuhrman grading in patients with CCRCC. Nevertheless, certain clinical indicators remain indispensable, underscoring the pressing need for their integrated use in clinical practice. ADVANCES IN KNOWLEDGE Previous studies have predominantly focused on using computed tomography or magnetic resonance imaging modalities to predict the Fuhrman grade of CCRCC. Our findings demonstrate that a prediction model based on US images is more cost-effective, easily accessible and exhibits commendable performance. Consequently, this study offers a promising approach to maximizing the use of US examinations in future research.
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Affiliation(s)
- Youchang Yang
- Department of Radiology, Qingdao Medical and Industrial Cross Key Laboratory, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China
| | - Ziyi Yuan
- School of Medicine, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qingguo Ren
- Department of Radiology, Qingdao Medical and Industrial Cross Key Laboratory, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China
| | - Jiajia Wang
- School of Medicine, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Shuai Guan
- Department of Radiology, Qingdao Medical and Industrial Cross Key Laboratory, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China
| | - Xiaoqiang Tang
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Qingjun Jiang
- Department of Radiology, Qingdao Medical and Industrial Cross Key Laboratory, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China
| | - Xiangshui Meng
- Department of Radiology, Qingdao Medical and Industrial Cross Key Laboratory, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China.
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Keller M, Rohner M, Honigmann P. The potential benefit of artificial intelligence regarding clinical decision-making in the treatment of wrist trauma patients. J Orthop Surg Res 2024; 19:579. [PMID: 39294720 PMCID: PMC11411868 DOI: 10.1186/s13018-024-05063-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2024] [Accepted: 09/07/2024] [Indexed: 09/21/2024] Open
Abstract
PURPOSE The implementation of artificial intelligence (AI) in health care is gaining popularity. Many publications describe powerful AI-enabled algorithms. Yet there's only scarce evidence for measurable value in terms of patient outcomes, clinical decision-making or socio-economic impact. Our aim was to investigate the significance of AI in the emergency treatment of wrist trauma patients. METHOD Two groups of physicians were confronted with twenty realistic cases of wrist trauma patients and had to find the correct diagnosis and provide a treatment recommendation. One group was assisted by an AI-enabled application which detects and localizes distal radius fractures (DRF) with near-to-perfect precision while the other group had no help. Primary outcome measurement was diagnostic accuracy. Secondary outcome measurements were required time, number of added CT scans and senior consultations, correctness of the treatment, subjective and objective stress levels. RESULTS The AI-supported group was able to make a diagnosis without support (no additional CT, no senior consultation) in significantly more of the cases than the control group (75% vs. 52%, p = 0.003). The AI-enhanced group detected DRF with superior sensitivity (1.00 vs. 0.96, p = 0.06) and specificity (0.99 vs. 0.93, p = 0.17), used significantly less additional CT scans to reach the correct diagnosis (14% vs. 28%, p = 0.02) and was subjectively significantly less stressed (p = 0.05). CONCLUSION The results indicate that physicians can diagnose wrist trauma more accurately and faster when aided by an AI-tool that lessens the need for extra diagnostic procedures. The AI-tool also seems to lower physicians' stress levels while examining cases. We anticipate that these benefits will be amplified in larger studies as skepticism towards the new technology diminishes.
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Affiliation(s)
- Marco Keller
- Hand and Peripheral Nerve Surgery, Department of Orthopaedic Surgery and Traumatology, Kantonsspital Baselland (Bruderholz, Liestal, Laufen), Bruderholz, Switzerland.
- Medical Additive Manufacturing Research Group (MAM), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland.
- Hand and Peripheral Nerve Surgery, Department of Orthopaedic Surgery, Traumatology and Hand Surgery, Spital Limmattal, Schlieren, Switzerland.
| | - Meret Rohner
- Hand and Peripheral Nerve Surgery, Department of Orthopaedic Surgery and Traumatology, Kantonsspital Baselland (Bruderholz, Liestal, Laufen), Bruderholz, Switzerland
- Medical Additive Manufacturing Research Group (MAM), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
- Medical Faculty, University of Basel, Basel, Switzerland
| | - Philipp Honigmann
- Hand and Peripheral Nerve Surgery, Department of Orthopaedic Surgery and Traumatology, Kantonsspital Baselland (Bruderholz, Liestal, Laufen), Bruderholz, Switzerland
- Medical Additive Manufacturing Research Group (MAM), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
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Li M, Fu S, Du J, Han X, Duan C, Ren Y, Qiao Y, Tang Y. Preoperative MRI-based radiomic nomogram for distinguishing solitary fibrous tumor from angiomatous meningioma: a multicenter study. Front Oncol 2024; 14:1399270. [PMID: 39359426 PMCID: PMC11445187 DOI: 10.3389/fonc.2024.1399270] [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: 03/11/2024] [Accepted: 08/27/2024] [Indexed: 10/04/2024] Open
Abstract
Purpose This study evaluates the efficacy of radiomics-based machine learning methodologies in differentiating solitary fibrous tumor (SFT) from angiomatous meningioma (AM). Materials and methods A retrospective analysis was conducted on 171 pathologically confirmed cases (94 SFT and 77 AM) spanning from January 2009 to September 2020 across four institutions. The study comprised a training set (n=137) and a validation set (n=34). All patients underwent contrast-enhanced T1-weighted (CE-T1WI) and T2-weighted(T2WI) MRI scans, from which 1166 radiomics features were extracted. Subsequently, seventeen features were selected through minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was employed to assess the independence of these features as predictors. A clinical model, established via both univariate and multivariate logistic regression based on MRI morphological features, was integrated with the optimal radiomics model to formulate a radiomics nomogram. The performance of the models was assessed utilizing the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV). Results The radiomics nomogram demonstrated exceptional discriminative performance in the validation set, achieving an AUC of 0.989. This outperformance was evident when compared to both the radiomics algorithm (AUC= 0.968) and the clinical model (AUC = 0.911) in the same validation sets. Notably, the radiomics nomogram exhibited impressive values for ACC, SEN, and SPE at 97.1%, 93.3%, and 100%, respectively, in the validation set. Conclusions The machine learning-based radiomic nomogram proves to be highly effective in distinguishing between SFT and AM.
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Affiliation(s)
- Mengjie Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Shengli Fu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jingjing Du
- Department of Radiology, Shizuishan First People's Hospital, Shizuishan, China
| | - Xiaoyu Han
- Department of Radiology, Qilu Hospital, Shandong University, Jinan, China
| | - Chongfeng Duan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yaqian Qiao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yueshan Tang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Qi YJ, Su GH, You C, Zhang X, Xiao Y, Jiang YZ, Shao ZM. Radiomics in breast cancer: Current advances and future directions. Cell Rep Med 2024; 5:101719. [PMID: 39293402 DOI: 10.1016/j.xcrm.2024.101719] [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: 05/11/2024] [Revised: 07/10/2024] [Accepted: 08/14/2024] [Indexed: 09/20/2024]
Abstract
Breast cancer is a common disease that causes great health concerns to women worldwide. During the diagnosis and treatment of breast cancer, medical imaging plays an essential role, but its interpretation relies on radiologists or clinical doctors. Radiomics can extract high-throughput quantitative imaging features from images of various modalities via traditional machine learning or deep learning methods following a series of standard processes. Hopefully, radiomic models may aid various processes in clinical practice. In this review, we summarize the current utilization of radiomics for predicting clinicopathological indices and clinical outcomes. We also focus on radio-multi-omics studies that bridge the gap between phenotypic and microscopic scale information. Acknowledging the deficiencies that currently hinder the clinical adoption of radiomic models, we discuss the underlying causes of this situation and propose future directions for advancing radiomics in breast cancer research.
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Affiliation(s)
- Ying-Jia Qi
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Guan-Hua Su
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xu Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
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Wei ZY, Zhang Z, Zhao DL, Zhao WM, Meng YG. Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer. World J Clin Cases 2024; 12:5908-5921. [PMID: 39286374 PMCID: PMC11287501 DOI: 10.12998/wjcc.v12.i26.5908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 06/19/2024] [Accepted: 07/03/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC. AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI. METHODS The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators. RESULTS Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature. CONCLUSION The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.
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Affiliation(s)
- Zhi-Yao Wei
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
| | - Zhe Zhang
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
| | - Dong-Li Zhao
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
| | - Wen-Ming Zhao
- National Genomics Data Center and Chinese Academy of Sciences Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100700, China
| | - Yuan-Guang Meng
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
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11
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Wang X, Bi Q, Deng C, Wang Y, Miao Y, Kong R, Chen J, Li C, Liu X, Gong X, Zhang Y, Bi G. Multiparametric MRI-based radiomics combined with 3D deep transfer learning to predict cervical stromal invasion in patients with endometrial carcinoma. Abdom Radiol (NY) 2024:10.1007/s00261-024-04577-1. [PMID: 39276192 DOI: 10.1007/s00261-024-04577-1] [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: 06/28/2024] [Revised: 09/06/2024] [Accepted: 09/09/2024] [Indexed: 09/16/2024]
Abstract
OBJECTIVE To develop and compare various preoperative cervical stromal invasion (CSI) prediction models, including radiomics, three-dimensional (3D) deep transfer learning (DTL), and integrated models, using single-sequence and multiparametric MRI. METHODS Data from 466 early-stage endometrial carcinoma (EC) patients from three centers were collected. Radiomics models were constructed based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) mapping, contrast-enhanced T1-weighted imaging (CE-T1WI), and four combined sequences as well as 3D DTL models. Two integrated models were created using ensemble and stacking algorithms based on optimal radiomics and DTL models. Model performance and clinical benefits were assessed using area under the curve (AUC), decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination index (IDI), and the Delong test for model comparisons. RESULTS Multiparametric MRI models were superior to single-sequence models for radiomics or DTL models. Ensemble and stacking integrated models displayed excellent performance. The stacking model had the highest average AUC (0.908) and accuracy (0.883) in external validation groups 1 and 2 (AUC = 0.965 and 0.851, respectively) and emerged as the best predictive model for CSI. All models significantly outperformed the radiologist (P < 0.05). In terms of net benefits, all models demonstrated favorable outcomes in DCA, NRI, and IDI, with the stacking model yielding the highest net benefit. CONCLUSION Multiparametric MRI-based radiomics combined with 3D DTL can be used to noninvasively predict CSI in EC patients with greater diagnostic accuracy than the radiologist. Stacking integrated models showed significant potential utility in predicting CSI. Which helps to provide new treatment strategy for clinicians to treat early-stage EC patients.
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Affiliation(s)
- Xianhong Wang
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Qiu Bi
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Cheng Deng
- Department of Radiology, the Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yaoxin Wang
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, the Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Yunbo Miao
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of Geriatrie Medicine, the First People's Hospital of Yunnan Province, Kunming, China
| | - Ruize Kong
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of Vascular Surgery, the First People's Hospital of Yunnan Province, Kunming, China
| | - Jie Chen
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chenrong Li
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Xiulan Liu
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Xiarong Gong
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Ya Zhang
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Guoli Bi
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China.
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Wang L, Peng J, Wen B, Zhai Z, Yuan S, Zhang Y, Ii L, Li W, Ding Y, Wang Y, Ye F. Contrast-Enhanced Computed Tomography-Based Machine Learning Radiomics Predicts IDH1 Expression and Clinical Prognosis in Head and Neck Squamous Cell Carcinoma. Acad Radiol 2024:S1076-6332(24)00601-9. [PMID: 39256086 DOI: 10.1016/j.acra.2024.08.038] [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: 05/02/2024] [Revised: 08/06/2024] [Accepted: 08/17/2024] [Indexed: 09/12/2024]
Abstract
RATIONALE AND OBJECTIVES Isocitrate dehydrogenase 1 (IDH1) is a potential therapeutic target across various tumor types. Here, we aimed to devise a radiomic model capable of predicting the IDH1 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) and examined its prognostic significance. MATERIALS AND METHODS We utilized genomic data, clinicopathological features, and contrast-enhanced computed tomography (CECT) images from The Cancer Genome Atlas and the Cancer Imaging Archive for prognosis analysis and radiomic model construction. The selection of optimal features was conducted using the intraclass correlation coefficient, minimum redundancy maximum relevance, and recursive feature elimination algorithms. A radiomic model for IDH1 prediction and radiomic score (RS) were established using a gradient-boosting machine. Associations between IDH1 expression, RS, clinicopathological variables, and overall survival (OS) were determined using univariate and multivariate Cox proportional hazards regression analyses and Kaplan-Meier curves. RESULTS IDH1 emerged as a distinct predictive factor in patients with HNSCC (hazard ratio [HR] 1.535, 95% confidence interval [CI]: 1.117-2.11, P = 0.008). The radiomic model, built on eight optimal features, demonstrated area under the curve values of 0.848 and 0.779 in the training and validation sets, respectively, for predicting IDH1 expression levels. Calibration and decision curve analyses validated the model's suitability and clinical utility. RS was significantly associated with OS (HR=2.22, 95% CI: 1.026-4.805, P = 0.043). CONCLUSION IDH1 expression is a significant prognostic marker. The developed radiomic model, derived from CECT features, offers a promising approach for diagnosing and prognosticating HNSCC.
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Affiliation(s)
- Le Wang
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
| | - Jilin Peng
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Ziyu Zhai
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Sijie Yuan
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Yulin Zhang
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Ling Ii
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Weijie Li
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Yinghui Ding
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Yixu Wang
- Department of Otolaryngology, Head and Neck Surgery, People's Hospital, Peking University, Beijing 100044, China
| | - Fanglei Ye
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
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Li S, Hu Y, Tian C, Luan J, Zhang X, Wei Q, Li X, Bian Y. Prediction of EGFR-TP53 genes co-mutations in patients with lung adenocarcinoma (LUAD) by 18F-FDG PET/CT radiomics. Clin Transl Oncol 2024:10.1007/s12094-024-03685-0. [PMID: 39251494 DOI: 10.1007/s12094-024-03685-0] [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: 06/16/2024] [Accepted: 08/20/2024] [Indexed: 09/11/2024]
Abstract
PURPOSE This retrospective study was undertaken to assess the predictive efficacy of 18F-FDG PET/CT -derived radiomic features concerning the co-mutation status of epidermal growth factor receptor (EGFR) and TP53 in LUAD. METHODS A cohort of 150 LUAD patients underwent pretreatment 18F-FDG PET/CT scans with known mutation status of EGFR and TP53 were collected. The feature extraction based on their PET/CT images utilized the Pyradiomics package based on the 3D Slicer. The optimal radiomic features were selected through correlation analysis and the Gradient Boosting Decision Tree (GBDT) algorithm, followed by the construction of the radiomic model. The clinical model incorporated meaningful clinical variables, whereas the complex model integrated both the radiomic and clinical models. The area under the receiver operating characteristic curve (AUC) facilitated the comparison of prediction performance across the three models. The DCA gauged the clinical utility of these models. RESULTS The patient cohort was randomly allocated into a training set (n = 105) and a validation set (n = 45) in a 7:3 ratio. Eleven PET and eleven CT optimal radiomic features were selected to construct the radiomic model. The model showed a good ability to discriminate the co-occurrence of EGFR and TP53, with AUC equal to 0.850 in the training set, and 0.748 in the validation set, compared with 0.750 and 0.626 for the clinical model. The complex model exhibited the highest AUC values, with 0.880 and 0.794 in both sets, but there were no significant differences compared to the radiomic model. The DCA revealed favorable clinical value. CONCLUSION
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Affiliation(s)
- Shuheng Li
- Hebei Medical University, Shijiazhuang, Hebei, China
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
- Department of Nuclear Medicine, Affiliated Hospital of Hebei University, Baoding, Hebei, China
| | - Yujing Hu
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Congna Tian
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Jiusong Luan
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Hebei University, Baoding, Hebei, China
| | - Xinchao Zhang
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Qiang Wei
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Xiaodong Li
- Department of Nuclear Medicine, Affiliated Hospital of Hebei University, Baoding, Hebei, China
| | - Yanzhu Bian
- Hebei Medical University, Shijiazhuang, Hebei, China.
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China.
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Shukla A, Chaudhary R, Nayyar N. Role of artificial intelligence in gastrointestinal surgery. Artif Intell Cancer 2024; 5:97317. [DOI: 10.35713/aic.v5.i2.97317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/11/2024] [Accepted: 07/17/2024] [Indexed: 09/05/2024] Open
Abstract
Artificial intelligence is rapidly evolving and its application is increasing day-by-day in the medical field. The application of artificial intelligence is also valuable in gastrointestinal diseases, by calculating various scoring systems, evaluating radiological images, preoperative and intraoperative assistance, processing pathological slides, prognosticating, and in treatment responses. This field has a promising future and can have an impact on many management algorithms. In this minireview, we aimed to determine the basics of artificial intelligence, the role that artificial intelligence may play in gastrointestinal surgeries and malignancies, and the limitations thereof.
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Affiliation(s)
- Ankit Shukla
- Department of Surgery, Dr Rajendra Prasad Government Medical College, Kangra 176001, Himachal Pradesh, India
| | - Rajesh Chaudhary
- Department of Renal Transplantation, Dr Rajendra Prasad Government Medical College, Kangra 176001, India
| | - Nishant Nayyar
- Department of Radiology, Dr Rajendra Prasad Government Medical College, Kangra 176001, Himachal Pradesh, India
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15
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You C, Su GH, Zhang X, Xiao Y, Zheng RC, Sun SY, Zhou JY, Lin LY, Wang ZZ, Wang H, Chen Y, Peng WJ, Jiang YZ, Shao ZM, Gu YJ. Multicenter radio-multiomic analysis for predicting breast cancer outcome and unravelling imaging-biological connection. NPJ Precis Oncol 2024; 8:193. [PMID: 39244594 PMCID: PMC11380684 DOI: 10.1038/s41698-024-00666-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 07/24/2024] [Indexed: 09/09/2024] Open
Abstract
Radiomics offers a noninvasive avenue for predicting clinicopathological factors. However, thorough investigations into a robust breast cancer outcome-predicting model and its biological significance remain limited. This study develops a robust radiomic model for prognosis prediction, and further excavates its biological foundation and transferring prediction performance. We retrospectively collected preoperative dynamic contrast-enhanced MRI data from three distinct breast cancer patient cohorts. In FUSCC cohort (n = 466), Lasso was used to select features correlated with patient prognosis and multivariate Cox regression was utilized to integrate these features and build the radiomic risk model, while multiomic analysis was conducted to investigate the model's biological implications. DUKE cohort (n = 619) and I-SPY1 cohort (n = 128) were used to test the performance of the radiomic signature in outcome prediction. A thirteen-feature radiomic signature was identified in the FUSCC cohort training set and validated in the FUSCC cohort testing set, DUKE cohort and I-SPY1 cohort for predicting relapse-free survival (RFS) and overall survival (OS) (RFS: p = 0.013, p = 0.024 and p = 0.035; OS: p = 0.036, p = 0.005 and p = 0.027 in the three cohorts). Multiomic analysis uncovered metabolic dysregulation underlying the radiomic signature (ATP metabolic process: NES = 1.84, p-adjust = 0.02; cholesterol biosynthesis: NES = 1.79, p-adjust = 0.01). Regarding the therapeutic implications, the radiomic signature exhibited value when combining clinical factors for predicting the pathological complete response to neoadjuvant chemotherapy (DUKE cohort, AUC = 0.72; I-SPY1 cohort, AUC = 0.73). In conclusion, our study identified a breast cancer outcome-predicting radiomic signature in a multicenter radio-multiomic study, along with its correlations with multiomic features in prognostic risk assessment, laying the groundwork for future prospective clinical trials in personalized risk stratification and precision therapy.
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Affiliation(s)
- Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Guan-Hua Su
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xu Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ren-Cheng Zheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Shi-Yun Sun
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jia-Yin Zhou
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Lu-Yi Lin
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ze-Zhou Wang
- Department of Cancer Prevention, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Yan Chen
- School of Medicine, University of Nottingham, Nottingham, UK
| | - Wei-Jun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Ya-Jia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Pham TD, Teh MT, Chatzopoulou D, Holmes S, Coulthard P. Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions. Curr Oncol 2024; 31:5255-5290. [PMID: 39330017 PMCID: PMC11430806 DOI: 10.3390/curroncol31090389] [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: 08/07/2024] [Revised: 09/01/2024] [Accepted: 09/03/2024] [Indexed: 09/28/2024] Open
Abstract
Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.
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Affiliation(s)
- Tuan D. Pham
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Turner Street, London E1 2AD, UK; (M.-T.T.); (D.C.); (S.H.); (P.C.)
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Tang Y, Liu X, Zhou W, Qin X. Interpretable Machine Learning Model Based on Superb Microvascular Imaging for Non-Invasive Determination of Crescent Status of IgAN. J Inflamm Res 2024; 17:5943-5955. [PMID: 39247842 PMCID: PMC11378797 DOI: 10.2147/jir.s476716] [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: 05/04/2024] [Accepted: 08/28/2024] [Indexed: 09/10/2024] Open
Abstract
Purpose To assess the crescentic status of IgA nephropathy (IgAN) non-invasively using a superb microvascular imaging (SMI)-based radiomics machine learning (ML) model. Patients and Methods IgAN patients who underwent renal biopsy from June 2022 to October 2023, with two-dimensional ultrasound (US) and SMI examinations conducted one day prior to the renal biopsy. The patients selected were divided randomly into a training group and a test group in a 7:3 ratio. Radiomic features were extracted from US and SMI images, then radiomic features were constructed and ML models were further established using logistic regression (LR) and extreme gradient boosting (XGBoost)XGBoost to determine the crescentic status. The utility of the proposed model was evaluated using receiver operating characteristics, calibration, and decision curve analysis. The SHapley Additive exPlanations (SHAP) was utilized to explain the best-performing ML model. Results A total of 147 IgAN patients were included in the study, with 103 in the training group and 44 in the test group .Among them, the US-SMI based XGBoost model achieved the best results, with an the area under the curve (AUC) of 0.839 (95% CI,0.756-0.910) and an accuracy of 78.6% in the training group.In the test group, the AUC was 0.859 (95% CI,0.721-0.964), and the accuracy was 81.8%, significantly surpassing the ML model of a single modality and the clinical model established based on occult blood. Additionally, the decision curve analysis (DCA) demonstrated that the XGBoost model provided a higher overall net benefit in the both groups. Conclusion The SMI radiomics ML model has the capability to accurately predict the crescentic status of IgAN patients, providing effective assistance for clinical treatment decisions.
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Affiliation(s)
- Yan Tang
- Department of Ultrasound, Beijing Anzhen Hospital Nanchong Hospital, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nan Chong, Sichuan, 637000, People's Republic of China
| | - Xiaoling Liu
- Department of Ultrasound, Beijing Anzhen Hospital Nanchong Hospital, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nan Chong, Sichuan, 637000, People's Republic of China
| | - Wang Zhou
- Department of Ultrasound, The First Affiliated hospital of Anhui medical University, Hefei, Anhui, 230022, People's Republic of China
| | - Xiachuan Qin
- Department of Ultrasound, Chengdu Second People's Hospital, Chengdu, Sichuan, 610000, People's Republic of China
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Wang SR, Cao CL, Du TT, Wang JL, Li J, Li WX, Chen M. Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:1611-1625. [PMID: 38808580 DOI: 10.1002/jum.16483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 04/22/2024] [Accepted: 05/05/2024] [Indexed: 05/30/2024]
Abstract
OBJECTIVE This study seeks to construct a machine learning model that merges clinical characteristics with ultrasound radiomic analysis-encompassing both the intratumoral and peritumoral-to predict the status of axillary lymph nodes in patients with early-stage breast cancer. METHODS The study employed retrospective methods, collecting clinical information, ultrasound data, and postoperative pathological results from 321 breast cancer patients (including 224 in the training group and 97 in the validation group). Through correlation analysis, univariate analysis, and Lasso regression analysis, independent risk factors related to axillary lymph node metastasis in breast cancer were identified from conventional ultrasound and immunohistochemical indicators, and a clinical feature model was constructed. Additionally, features were extracted from ultrasound images of the intratumoral and its 1-5 mm peritumoral to establish a radiomics feature formula. Furthermore, by combining clinical features and ultrasound radiomics features, six machine learning models (Logistic Regression, Decision Tree, Support Vector Machine, Extreme Gradient Boosting, Random Forest, and K-Nearest Neighbors) were compared for diagnostic efficacy, and constructing a joint prediction model based on the optimal ML algorithm. The use of Shapley Additive Explanations (SHAP) enhanced the visualization and interpretability of the model during the diagnostic process. RESULTS Among the 321 breast cancer patients, 121 had axillary lymph node metastasis, and 200 did not. The clinical feature model had an AUC of 0.779 and 0.777 in the training and validation groups, respectively. Radiomics model analysis showed that the model including the Intratumor +3 mm peritumor area had the best diagnostic performance, with AUCs of 0.847 and 0.844 in the training and validation groups, respectively. The joint prediction model based on the XGBoost algorithm reached AUCs of 0.917 and 0.905 in the training and validation groups, respectively. SHAP analysis indicated that the Rad Score had the highest weight in the prediction model, playing a significant role in predicting axillary lymph node metastasis in breast cancer. CONCLUSION The predictive model, which integrates clinical features and radiomic characteristics using the XGBoost algorithm, demonstrates significant diagnostic value for axillary lymph node metastasis in breast cancer. This model can provide significant references for preoperative surgical strategy selection and prognosis evaluation for breast cancer patients, helping to reduce postoperative complications and improve long-term survival rates. Additionally, the utilization of SHAP enhancing the global and local interpretability of the model.
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Affiliation(s)
- Si-Rui Wang
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
| | - Chun-Li Cao
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
| | - Ting-Ting Du
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
| | - Jin-Li Wang
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
| | - Jun Li
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
| | - Wen-Xiao Li
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
| | - Ming Chen
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
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Stojanović Z, Simić K, Tepšić Ostojić V, Gojković Z, Petković-Ćurčin A. Electroconvulsive therapy in the Fourth Industrial Revolution (Review). Biomed Rep 2024; 21:129. [PMID: 39070111 PMCID: PMC11273193 DOI: 10.3892/br.2024.1817] [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: 04/22/2024] [Accepted: 06/21/2024] [Indexed: 07/30/2024] Open
Abstract
There are very few treatments in psychiatry, even in medicine, that have experienced longevity and effectiveness such as electroconvulsive therapy (ECT), despite the controversies and stigma that accompany it. The experience of the COVID-19 pandemic has highlighted the need to strengthen mental health systems in most countries, given that depression is one of the leading health problems and that there is an evident shortage of psychiatrists worldwide. The Fourth Industrial Revolution, has witnessed great progress in artificial intelligence (AI) technology, which opens up the possibility of its application both in the diagnosis and in the therapy of mental disorders. It is no exaggeration to suggest that tools such as AI, neuroimaging and blood tests will bring significant change to psychiatry in the coming years, but even so, treating severe mental disorders remains a challenge. The present review summarized the development of ECT over time, its application in clinical practice, neurobiological correlates and mechanisms of action and sheds light on the important place of ECT in the era of technological development, considering that ECT is still the most effective therapy for the treatment of severe mental disorders, especially depressive disorder.
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Affiliation(s)
- Zvezdana Stojanović
- Clinic for Psychiatry, Military Medical Academy, 11000 Belgrade, Serbia
- Medical Faculty of the Military Medical Academy, University of Defence, 11000 Belgrade, Serbia
| | - Katarina Simić
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Vesna Tepšić Ostojić
- Clinic for Psychiatry, Military Medical Academy, 11000 Belgrade, Serbia
- Medical Faculty of the Military Medical Academy, University of Defence, 11000 Belgrade, Serbia
| | - Zagorka Gojković
- Clinic for Psychiatry, Military Medical Academy, 11000 Belgrade, Serbia
| | - Aleksandra Petković-Ćurčin
- Medical Faculty of the Military Medical Academy, University of Defence, 11000 Belgrade, Serbia
- Institute for Medical Research, Military Medical Academy, 11000 Belgrade, Serbia
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20
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Iftikhar M, Saqib M, Zareen M, Mumtaz H. Artificial intelligence: revolutionizing robotic surgery: review. Ann Med Surg (Lond) 2024; 86:5401-5409. [PMID: 39238994 PMCID: PMC11374272 DOI: 10.1097/ms9.0000000000002426] [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: 05/28/2024] [Accepted: 07/25/2024] [Indexed: 09/07/2024] Open
Abstract
Robotic surgery, known for its minimally invasive techniques and computer-controlled robotic arms, has revolutionized modern medicine by providing improved dexterity, visualization, and tremor reduction compared to traditional methods. The integration of artificial intelligence (AI) into robotic surgery has further advanced surgical precision, efficiency, and accessibility. This paper examines the current landscape of AI-driven robotic surgical systems, detailing their benefits, limitations, and future prospects. Initially, AI applications in robotic surgery focused on automating tasks like suturing and tissue dissection to enhance consistency and reduce surgeon workload. Present AI-driven systems incorporate functionalities such as image recognition, motion control, and haptic feedback, allowing real-time analysis of surgical field images and optimizing instrument movements for surgeons. The advantages of AI integration include enhanced precision, reduced surgeon fatigue, and improved safety. However, challenges such as high development costs, reliance on data quality, and ethical concerns about autonomy and liability hinder widespread adoption. Regulatory hurdles and workflow integration also present obstacles. Future directions for AI integration in robotic surgery include enhancing autonomy, personalizing surgical approaches, and refining surgical training through AI-powered simulations and virtual reality. Overall, AI integration holds promise for advancing surgical care, with potential benefits including improved patient outcomes and increased access to specialized expertise. Addressing challenges and promoting responsible adoption are essential for realizing the full potential of AI-driven robotic surgery.
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Poonia RC, Al-Alshaikh HA. Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0. Comput Biol Med 2024; 179:108874. [PMID: 39013343 DOI: 10.1016/j.compbiomed.2024.108874] [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: 08/13/2023] [Revised: 05/29/2024] [Accepted: 06/18/2024] [Indexed: 07/18/2024]
Abstract
Smart healthcare has advanced the medical industry with the integration of data-driven approaches. Artificial intelligence and machine learning provided remarkable progress, but there is a lack of transparency and interpretability in such applications. To overcome such limitations, explainable AI (EXAI) provided a promising result. This paper applied the EXAI for disease diagnosis in the advancement of smart healthcare. The paper combined the approach of transfer learning, vision transformer, and explainable AI and designed an ensemble approach for prediction of disease and its severity. The result is evaluated on a dataset of Alzheimer's disease. The result analysis compared the performance of transfer learning models with the ensemble model of transfer learning and vision transformer. For training, InceptionV3, VGG19, Resnet50, and Densenet121 transfer learning models were selected for ensembling with vision transformer. The result compares the performance of two models: a transfer learning (TL) model and an ensemble transfer learning (Ensemble TL) model combined with vision transformer (ViT) on ADNI dataset. For the TL model, the accuracy is 58 %, precision is 52 %, recall is 42 %, and the F1-score is 44 %. Whereas, the Ensemble TL model with ViT shows significantly improved performance i.e., 96 % of accuracy, 94 % of precision, 90 % of recall and 92 % of F1-score on ADNI dataset. This shows the efficacy of the ensemble model over transfer learning models.
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Affiliation(s)
- Ramesh Chandra Poonia
- Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, 201003, India.
| | - Halah A Al-Alshaikh
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia.
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22
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Llorián-Salvador Ó, Windeler N, Martin N, Etzel L, Andrade-Navarro MA, Bernhardt D, Rost B, Borm KJ, Combs SE, Duma MN, Peeken JC. CT-based radiomics for predicting breast cancer radiotherapy side effects. Sci Rep 2024; 14:20051. [PMID: 39209947 PMCID: PMC11362146 DOI: 10.1038/s41598-024-70723-w] [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: 05/26/2024] [Accepted: 08/20/2024] [Indexed: 09/04/2024] Open
Abstract
Skin inflammation with the potential sequel of moist epitheliolysis and edema constitute the most frequent breast radiotherapy (RT) acute side effects. The aim of this study was to compare the predictive value of tissue-derived radiomics features to the total breast volume (TBV) for the moist cells epitheliolysis as a surrogate for skin inflammation, and edema. Radiomics features were extracted from computed tomography (CT) scans of 252 breast cancer patients from two volumes of interest: TBV and glandular tissue (GT). Machine learning classifiers were trained on radiomics and clinical features, which were evaluated for both side effects. The best radiomics model was a least absolute shrinkage and selection operator (LASSO) classifier, using TBV features, predicting moist cells epitheliolysis, achieving an area under the receiver operating characteristic (AUROC) of 0.74. This was comparable to TBV breast volume (AUROC of 0.75). Combined models of radiomics and clinical features did not improve performance. Exclusion of volume-correlated features slightly reduced the predictive performance (AUROC 0.71). We could demonstrate the general propensity of planning CT-based radiomics models to predict breast RT-dependent side effects. Mammary tissue was more predictive than glandular tissue. The radiomics features performance was influenced by their high correlation to TBV volume.
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Affiliation(s)
- Óscar Llorián-Salvador
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.
- Department of Informatics, Bioinformatics and Computational Biology-i12, Technische Universität München, Boltzmannstr. 3, 85748, Munich, Germany.
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch- Weg 15, 55128, Mainz, Germany.
| | - Nora Windeler
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Nicole Martin
- Department of Informatics, Bioinformatics and Computational Biology-i12, Technische Universität München, Boltzmannstr. 3, 85748, Munich, Germany
| | - Lucas Etzel
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120, Heidelberg, Germany
| | - Miguel A Andrade-Navarro
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch- Weg 15, 55128, Mainz, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120, Heidelberg, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum, 85764, München, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology-i12, Technische Universität München, Boltzmannstr. 3, 85748, Munich, Germany
| | - Kai J Borm
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120, Heidelberg, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum, 85764, München, Germany
| | - Marciana N Duma
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
- Department of Radiation Oncology, Helios Clinics of Schwerin - University Campus of MSH Medical School Hamburg, Schwerin, Germany
- Department for Human Medicine, MSH Medical School Hamburg, Hamburg, Germany
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120, Heidelberg, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum, 85764, München, Germany
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Chen M, Cai R, Zhang A, Chi X, Qian J. The diagnostic value of artificial intelligence-assisted imaging for developmental dysplasia of the hip: a systematic review and meta-analysis. J Orthop Surg Res 2024; 19:522. [PMID: 39210407 PMCID: PMC11360681 DOI: 10.1186/s13018-024-05003-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024] Open
Abstract
OBJECTIVE To clarify the efficacy of artificial intelligence (AI)-assisted imaging in the diagnosis of developmental dysplasia of the hip (DDH) through a meta-analysis. METHODS Relevant literature on AI for early DDH diagnosis was searched in PubMed, Web of Science, Embase, and The Cochrane Library databases until April 4, 2024. The Quality Assessment of Diagnostic Accuracy Studies tool was used to assess the quality of included studies. Revman5.4 and StataSE-64 software were used to calculate the combined sensitivity, specificity, AUC value, and DOC value of AI-assisted imaging for DDH diagnosis. RESULTS The meta-analysis included 13 studies (6 prospective and 7 retrospective) with 28 AI models and a total of 10,673 samples. The summary sensitivity, specificity, AUC value, and DOC value were 99.0% (95% CI: 97.0-100.0%), 94.0% (95% CI: 89.0-96.0%), 99.0% (95% CI: 98.0-100.0%), and 1342 (95% CI: 469-3842), respectively. CONCLUSION AI-assisted imaging demonstrates high diagnostic efficacy for DDH detection, improving the accuracy of early DDH imaging examination. More prospective studies are needed to further confirm the value of AI-assisted imaging for early DDH diagnosis.
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Affiliation(s)
- Min Chen
- Department of the Child Health Department, Women's Hospital of Nanjing Medical University, (Nanjing Women and Children's Healthcare Hospital), Nanjing, Jiangsu, 21000, China
| | - Ruyi Cai
- Department of the Child Health Department, Women's Hospital of Nanjing Medical University, (Nanjing Women and Children's Healthcare Hospital), Nanjing, Jiangsu, 21000, China
| | - Aixia Zhang
- Department of the Child Health Department, Women's Hospital of Nanjing Medical University, (Nanjing Women and Children's Healthcare Hospital), Nanjing, Jiangsu, 21000, China
| | - Xia Chi
- Department of the Child Health Department, Women's Hospital of Nanjing Medical University, (Nanjing Women and Children's Healthcare Hospital), Nanjing, Jiangsu, 21000, China
- School of Pediatrics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China
| | - Jun Qian
- Department of the Child Health Department, Women's Hospital of Nanjing Medical University, (Nanjing Women and Children's Healthcare Hospital), Nanjing, Jiangsu, 21000, China.
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24
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Zhu Y, Wei Y, Chen Z, Li X, Zhang S, Wen C, Cao G, Zhou J, Wang M. Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study. Insights Imaging 2024; 15:211. [PMID: 39186173 PMCID: PMC11347551 DOI: 10.1186/s13244-024-01795-5] [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: 06/23/2024] [Accepted: 08/06/2024] [Indexed: 08/27/2024] Open
Abstract
OBJECTIVES To explore the performance differences of multiple annotations in radiomics analysis and provide a reference for tumour annotation in large-scale medical image analysis. METHODS A total of 342 patients from two centres who underwent radical resection for rectal cancer were retrospectively studied and divided into training, internal validation, and external validation cohorts. Three predictive tasks of tumour T-stage (pT), lymph node metastasis (pLNM), and disease-free survival (pDFS) were performed. Twelve radiomics models were constructed using Lasso-Logistic or Lasso-Cox to evaluate and four annotation methods, 2D detailed annotation along tumour boundaries (2D), 3D detailed annotation along tumour boundaries (3D), 2D bounding box (2DBB), and 3D bounding box (3DBB) on T2-weighted images, were compared. Radiomics models were used to establish combined models incorporating clinical risk factors. The DeLong test was performed to compare the performance of models using the receiver operating characteristic curves. RESULTS For radiomics models, the area under the curve values ranged from 0.627 (0.518-0.728) to 0.811 (0.705-0.917) in the internal validation cohort and from 0.619 (0.469-0.754) to 0.824 (0.689-0.918) in the external validation cohort. Most radiomics models based on four annotations did not differ significantly, except between the 3D and 3DBB models for pLNM (p = 0.0188) in the internal validation cohort. For combined models, only the 2D model significantly differed from the 2DBB (p = 0.0372) and 3D models (p = 0.0380) for pDFS. CONCLUSION Radiomics and combined models constructed with 2D and bounding box annotations showed comparable performances to those with 3D and detailed annotations along tumour boundaries in rectal cancer characterisation and prognosis prediction. CRITICAL RELEVANCE STATEMENT For quantitative analysis of radiological images, the selection of 2D maximum tumour area or bounding box annotation is as representative and easy to operate as 3D whole tumour or detailed annotations along tumour boundaries. KEY POINTS There is currently a lack of discussion on whether different annotation efforts in radiomics are predictively representative. No significant differences were observed in radiomics and combined models regardless of the annotations (2D, 3D, detailed, or bounding box). Prioritise selecting the more time and effort-saving 2D maximum area bounding box annotation.
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Affiliation(s)
- Ying Zhu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yaru Wei
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhongwei Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiang Li
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shiwei Zhang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Caiyun Wen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Guoquan Cao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiejie Zhou
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Meihao Wang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
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25
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Pasha SA, Khalid A, Levy T, Demyan L, Hartman S, Newman E, Weiss MJ, King DA, Zanos T, Melis M. Machine learning to predict completion of treatment for pancreatic cancer. J Surg Oncol 2024. [PMID: 39155666 DOI: 10.1002/jso.27812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 08/02/2024] [Indexed: 08/20/2024]
Abstract
BACKGROUND Chemotherapy enhances survival rates for pancreatic cancer (PC) patients postsurgery, yet less than 60% complete adjuvant therapy, with a smaller fraction undergoing neoadjuvant treatment. Our study aimed to predict which patients would complete pre- or postoperative chemotherapy through machine learning (ML). METHODS Patients with resectable PC identified in our institutional pancreas database were grouped into two categories: those who completed all intended treatments (i.e., surgery plus either neoadjuvant or adjuvant chemotherapy), and those who did not. We applied logistic regression with lasso penalization and an extreme gradient boosting model for prediction, and further examined it through bootstrapping for sensitivity. RESULTS Among 208 patients, the median age was 69, with 49.5% female and 62% white participants. Most had an Eastern Cooperative Oncology Group (ECOG) performance status of ≤2. The PC predominantly affected the pancreatic head. Neoadjuvant and adjuvant chemotherapies were received by 26% and 47.1%, respectively, but only 49% completed all treatments. Incomplete therapy was correlated with older age and lower ECOG status. Negative prognostic factors included worsening diabetes, age, congestive heart failure, high body mass index, family history of PC, initial bilirubin levels, and tumor location in the pancreatic head. The models also flagged other factors, such as jaundice and specific cancer markers, impacting treatment completion. The predictive accuracy (area under the receiver operating characteristic curve) was 0.67 for both models, with performance expected to improve with larger datasets. CONCLUSIONS Our findings underscore the potential of ML to forecast PC treatment completion, highlighting the importance of specific preoperative factors. Increasing data volumes may enhance predictive accuracy, offering valuable insights for personalized patient strategies.
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Affiliation(s)
- Shamsher A Pasha
- Department of Surgery, Northwell Health, North Shore/Long Island Jewish, Manhasset, New York, USA
| | - Abdullah Khalid
- Department of Surgery, Northwell Health, North Shore/Long Island Jewish, Manhasset, New York, USA
| | - Todd Levy
- Department of Surgery, Northwell Health, North Shore/Long Island Jewish, Manhasset, New York, USA
| | - Lyudmyla Demyan
- Department of Surgery, Northwell Health, North Shore/Long Island Jewish, Manhasset, New York, USA
| | - Sarah Hartman
- Department of Surgery, Northwell Health, North Shore/Long Island Jewish, Manhasset, New York, USA
| | - Elliot Newman
- Department of Surgery, Northwell Health, Lenox Hill Hospital, New York City, New York, USA
| | - Matthew J Weiss
- Department of Surgery, Northwell Health, North Shore/Long Island Jewish, Manhasset, New York, USA
| | - Daniel A King
- Department of Surgery, Northwell Health, North Shore/Long Island Jewish, Manhasset, New York, USA
| | - Theodoros Zanos
- Department of Surgery, Northwell Health, North Shore/Long Island Jewish, Manhasset, New York, USA
| | - Marcovalerio Melis
- Department of Surgery, Northwell Health, Lenox Hill Hospital, New York City, New York, USA
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Kang WY, Yang Z, Park H, Lee J, Hong SJ, Shim E, Woo OH. Automated Opportunistic Osteoporosis Screening Using Low-Dose Chest CT among Individuals Undergoing Lung Cancer Screening in a Korean Population. Diagnostics (Basel) 2024; 14:1789. [PMID: 39202277 PMCID: PMC11354205 DOI: 10.3390/diagnostics14161789] [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: 07/07/2024] [Revised: 08/14/2024] [Accepted: 08/14/2024] [Indexed: 09/03/2024] Open
Abstract
Opportunistic osteoporosis screening using deep learning (DL) analysis of low-dose chest CT (LDCT) scans is a potentially promising approach for the early diagnosis of this condition. We explored bone mineral density (BMD) profiles across all adult ages and prevalence of osteoporosis using LDCT with DL in a Korean population. This retrospective study included 1915 participants from two hospitals who underwent LDCT during general health checkups between 2018 and 2021. Trabecular volumetric BMD of L1-2 was automatically calculated using DL and categorized according to the American College of Radiology quantitative computed tomography diagnostic criteria. BMD decreased with age in both men and women. Women had a higher peak BMD in their twenties, but lower BMD than men after 50. Among adults aged 50 and older, the prevalence of osteoporosis and osteopenia was 26.3% and 42.0%, respectively. Osteoporosis prevalence was 18.0% in men and 34.9% in women, increasing with age. Compared to previous data obtained using dual-energy X-ray absorptiometry, the prevalence of osteoporosis, particularly in men, was more than double. The automated opportunistic BMD measurements using LDCT can effectively predict osteoporosis for opportunistic screening and identify high-risk patients. Patients undergoing lung cancer screening may especially profit from this procedure requiring no additional imaging or radiation exposure.
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Affiliation(s)
- Woo Young Kang
- Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea; (W.Y.K.); (Z.Y.); (H.P.); (S.-J.H.)
| | - Zepa Yang
- Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea; (W.Y.K.); (Z.Y.); (H.P.); (S.-J.H.)
| | - Heejun Park
- Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea; (W.Y.K.); (Z.Y.); (H.P.); (S.-J.H.)
| | - Jemyoung Lee
- Department of Applied Bioengineering, Seoul National University, Seoul 08826, Republic of Korea;
- ClariPi Research, ClariPi Inc., Seoul 03088, Republic of Korea
| | - Suk-Joo Hong
- Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea; (W.Y.K.); (Z.Y.); (H.P.); (S.-J.H.)
| | - Euddeum Shim
- Department of Radiology, Korea University Ansan Hospital, Ansan 15355, Republic of Korea;
| | - Ok Hee Woo
- Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea; (W.Y.K.); (Z.Y.); (H.P.); (S.-J.H.)
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Luo H, Chen Z, Xu H, Ren J, Zhou P. Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer. Sci Rep 2024; 14:18900. [PMID: 39143315 PMCID: PMC11324898 DOI: 10.1038/s41598-024-69725-5] [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: 01/06/2024] [Accepted: 08/08/2024] [Indexed: 08/16/2024] Open
Abstract
To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) burden in breast cancer. Invasive breast cancer patients with preoperative MRI were retrospectively enrolled and categorized into low (< 2 lymph nodes involved (LNs+)) and high (≥ 2 LNs+) burden groups based on surgical pathology. PE was evaluated on T2WI, and intra- and peri-tumoral radiomic features were extracted from MRI-visible tumors in DCE-MRI. Deep learning models were developed for LN burden prediction in the training cohort and validated in an independent cohort. The incremental value of PE was evaluated through receiver operating characteristic (ROC) analysis, confirming the improvement in the area under the curve (AUC) using the Delong test. This was complemented by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics. The deep learning combined model, incorporating PE with selected radiomic features, demonstrated significantly higher AUC values compared to the MRI model and the DLR model in the training cohort (n = 177) (AUC: 0.953 vs. 0.849 and 0.867, p < 0.05) and the validation cohort (n = 111) (AUC: 0.963 vs. 0.883 and 0.882, p < 0.05). The complementary analysis demonstrated that PE significantly enhances the prediction performance of the DLR model (Categorical NRI: 0.551, p < 0.001; IDI = 0.343, p < 0.001). These findings were confirmed in the validation cohort (Categorical NRI: 0.539, p < 0.001; IDI = 0.387, p < 0.001). PE improved preoperative ALNM burden prediction of DLR model, facilitating personalized axillary management in breast cancer patients.
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Affiliation(s)
- Hongbing Luo
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, 4th Section of South Ren-Min Road, Chengdu, 610041, China.
| | - Zhe Chen
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, 4th Section of South Ren-Min Road, Chengdu, 610041, China
| | - Hao Xu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, 4th Section of South Ren-Min Road, Chengdu, 610041, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, 4th Section of South Ren-Min Road, Chengdu, 610041, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, 4th Section of South Ren-Min Road, Chengdu, 610041, China.
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28
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Xing ZC, Guo HZ, Hou ZL, Zhang HX, Zhang S. The value of computed tomography-based radiomics for predicting malignant pleural effusions. Front Oncol 2024; 14:1419343. [PMID: 39188676 PMCID: PMC11345134 DOI: 10.3389/fonc.2024.1419343] [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: 04/18/2024] [Accepted: 07/23/2024] [Indexed: 08/28/2024] Open
Abstract
Background Malignant pleural effusion (MPE) is a common clinical problem that requires cytological and/or histological confirmation obtained by invasive examination to establish a definitive diagnosis. Radiomics is rapidly evolving and can provide a non-invasive tool to identify MPE. Objectives We aimed to develop a model based on radiomic features extracted from unenhanced chest computed tomography (CT) images and investigate its value in predicting MPE. Method This retrospective study included patients with pleural effusions between January 2016 and June 2020. All patients underwent a chest CT scanning and medical thoracoscopy after artificial pneumothorax. Cases were divided into a training cohort and a test cohort for modelling and verifying respectively. The Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) were applied to determine the optimal features. We built a radiomics model based on support vector machines (SVM) and evaluated its performance using ROC and calibration curve analysis. Results Twenty-nine patients with MPE and fifty-two patients with non-MPE were enrolled. A total of 944 radiomic features were quantitatively extracted from each sample and reduced to 14 features for modeling after selection. The AUC of the radiomics model was 0.96 (95% CI: 0.912-0.999) and 0.86 (95% CI: 0.657~1.000) in the training and test cohorts, respectively. The calibration curves for model were in good agreement between predicted and actual data. Conclusions The radiomics model based on unenhanced chest CT has good performance for predicting MPE and may provide a powerful tool for doctors in clinical decision-making.
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Affiliation(s)
- Zhen-Chuan Xing
- Department of Pulmonary and Critical Care Medicine, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Hua-Zheng Guo
- Department of Infectious Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Zi-Liang Hou
- Department of Pulmonary and Critical Care Medicine, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Hong-Xia Zhang
- Department of Pulmonary and Critical Care Medicine, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Shuai Zhang
- Department of Pulmonary and Critical Care Medicine, Beijing Luhe Hospital, Capital Medical University, Beijing, China
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Wang J, Liu G, Zhou C, Cui X, Wang W, Wang J, Huang Y, Jiang J, Wang Z, Tang Z, Zhang A, Cui D. Application of artificial intelligence in cancer diagnosis and tumor nanomedicine. NANOSCALE 2024; 16:14213-14246. [PMID: 39021117 DOI: 10.1039/d4nr01832j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Cancer is a major health concern due to its high incidence and mortality rates. Advances in cancer research, particularly in artificial intelligence (AI) and deep learning, have shown significant progress. The swift evolution of AI in healthcare, especially in tools like computer-aided diagnosis, has the potential to revolutionize early cancer detection. This technology offers improved speed, accuracy, and sensitivity, bringing a transformative impact on cancer diagnosis, treatment, and management. This paper provides a concise overview of the application of artificial intelligence in the realms of medicine and nanomedicine, with a specific emphasis on the significance and challenges associated with cancer diagnosis. It explores the pivotal role of AI in cancer diagnosis, leveraging structured, unstructured, and multimodal fusion data. Additionally, the article delves into the applications of AI in nanomedicine sensors and nano-oncology drugs. The fundamentals of deep learning and convolutional neural networks are clarified, underscoring their relevance to AI-driven cancer diagnosis. A comparative analysis is presented, highlighting the accuracy and efficiency of traditional methods juxtaposed with AI-based approaches. The discussion not only assesses the current state of AI in cancer diagnosis but also delves into the challenges faced by AI in this context. Furthermore, the article envisions the future development direction and potential application of artificial intelligence in cancer diagnosis, offering a hopeful prospect for enhanced cancer detection and improved patient prognosis.
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Affiliation(s)
- Junhao Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Guan Liu
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Cheng Zhou
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Xinyuan Cui
- Imaging Department of Rui Jin Hospital, Medical School of Shanghai Jiao Tong University, Shanghai, China
| | - Wei Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jiulin Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Yixin Huang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jinlei Jiang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Zhitao Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Zengyi Tang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Amin Zhang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China.
| | - Daxiang Cui
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- School of Medicine, Henan University, Henan, China
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Fitzek S, Choi KEA. Shaping future practices: German-speaking medical and dental students' perceptions of artificial intelligence in healthcare. BMC MEDICAL EDUCATION 2024; 24:844. [PMID: 39107732 PMCID: PMC11304766 DOI: 10.1186/s12909-024-05826-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 07/26/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND The growing use of artificial intelligence (AI) in healthcare necessitates understanding the perspectives of future practitioners. This study investigated the perceptions of German-speaking medical and dental students regarding the role of artificial intelligence (AI) in their future practices. METHODS A 28-item survey adapted from the AI in Healthcare Education Questionnaire (AIHEQ) and the Medical Student's Attitude Toward AI in Medicine (MSATAIM) scale was administered to students in Austria, Germany, and Switzerland from April to July 2023. Participants were recruited through targeted advertisements on Facebook and Instagram and were required to be proficient in German and enrolled in medical or dental programs. The data analysis included descriptive statistics, correlations, t tests, and thematic analysis of the open-ended responses. RESULTS Of the 409 valid responses (mean age = 23.13 years), only 18.2% of the participants reported receiving formal training in AI. Significant positive correlations were found between self-reported tech-savviness and AI familiarity (r = 0.67) and between confidence in finding reliable AI information and positive attitudes toward AI (r = 0.72). While no significant difference in AI familiarity was found between medical and dental students, dental students exhibited slightly more positive attitudes toward the integration of AI into their future practices. CONCLUSION This study underscores the need for comprehensive AI education in medical and dental curricula to address knowledge gaps and prepare future healthcare professionals for the ethical and effective integration of AI in practice.
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Affiliation(s)
- Sebastian Fitzek
- Health Services Research, Faculty of Medicine/Dentistry, Danube Private University, Steiner Landstraße 124, Krems‑Stein, 3500, Austria.
| | - Kyung-Eun Anna Choi
- Health Services Research, Faculty of Medicine/Dentistry, Danube Private University, Steiner Landstraße 124, Krems‑Stein, 3500, Austria
- Center for Health Services Research, Brandenburg Medical School, Seebad 82/83, 15562 Rüdersdorf b. Berlin, Neuruppin, Germany
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Eun NL, Lee E, Park AY, Son EJ, Kim JA, Youk JH. Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2024; 45:412-417. [PMID: 38593859 DOI: 10.1055/a-2230-2455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
PURPOSE To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis. MATERIALS AND METHODS We retrospectively collected a dataset consisting of 516 breast lesions (364 benign and 152 malignant) in 471 women who underwent B-mode US and MFI. The internal dataset was split into training (n = 410) and test datasets (n = 106) for developing AI algorithms from deep convolutional neural networks from MFI. AI algorithms were trained to provide malignancy risk (0-100%). The developed AI algorithms were further validated with an independent external dataset of 264 lesions (229 benign and 35 malignant). The diagnostic performance of B-mode US, AI algorithms, or their combinations was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). RESULTS The AUROC of the developed three AI algorithms (0.955-0.966) was higher than that of B-mode US (0.842, P < 0.0001). The AUROC of the AI algorithms on the external validation dataset (0.892-0.920) was similar to that of the test dataset. Among the AI algorithms, no significant difference was found in all performance metrics combined with or without B-mode US. Combined B-mode US and AI algorithms had a higher AUROC (0.963-0.972) than that of B-mode US (P < 0.0001). Combining B-mode US and AI algorithms significantly decreased the false-positive rate of BI-RADS category 4A lesions from 87% to 13% (P < 0.0001). CONCLUSION AI-based MFI diagnosed breast cancers with better performance than B-mode US, eliminating 74% of false-positive diagnoses in BI-RADS category 4A lesions.
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Affiliation(s)
- Na Lae Eun
- Radiology, Gangnam Severance Hospital, Seoul, Korea (the Republic of)
| | - Eunjung Lee
- Computational Science and Engineering, Yonsei University, Seoul, Korea (the Republic of)
| | - Ah Young Park
- Radiology, Bundang CHA Medical Center, Seongnam, Korea (the Republic of)
| | - Eun Ju Son
- Radiology, Gangnam Severance Hospital, Seoul, Korea (the Republic of)
| | - Jeong-Ah Kim
- Radiology, Gangnam Severance Hospital, Seoul, Korea (the Republic of)
| | - Ji Hyun Youk
- Department of Radiology, Yonsei University College of Medicine, Seoul, Korea, Republic of
- Radiology, Gangnam Severance Hospital, Seoul, Korea (the Republic of)
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Arzamasov K, Vasilev Y, Zelenova M, Pestrenin L, Busygina Y, Bobrovskaya T, Chetverikov S, Shikhmuradov D, Pankratov A, Kirpichev Y, Sinitsyn V, Son I, Omelyanskaya O. Independent evaluation of the accuracy of 5 artificial intelligence software for detecting lung nodules on chest X-rays. Quant Imaging Med Surg 2024; 14:5288-5303. [PMID: 39144030 PMCID: PMC11320553 DOI: 10.21037/qims-24-160] [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: 01/25/2024] [Accepted: 06/11/2024] [Indexed: 08/16/2024]
Abstract
Background The integration of artificial intelligence (AI) into medicine is growing, with some experts predicting its standalone use soon. However, skepticism remains due to limited positive outcomes from independent validations. This research evaluates AI software's effectiveness in analyzing chest X-rays (CXR) to identify lung nodules, a possible lung cancer indicator. Methods This retrospective study analyzed 7,670,212 record pairs from radiological exams conducted between 2020 and 2022 during the Moscow Computer Vision Experiment, focusing on CXR and computed tomography (CT) scans. All images were acquired during clinical routine. The final dataset comprised 100 CXR images (50 with lung nodules, 50 without), selected consecutively and based on inclusion and exclusion criteria, to evaluate the performance of all five AI-based solutions, participating in the Moscow Computer Vision Experiment and analyzing CXR. The evaluation was performed in 3 stages. In the first stage, the probability of a nodule in the lung obtained from AI services was compared with the Ground Truth (1-there is a nodule, 0-there is no nodule). In the second stage, 3 radiologists evaluated the segmentation of nodules performed by the AI services (1-nodule correctly segmented, 0-nodule incorrectly segmented or not segmented at all). In the third stage, the same radiologists additionally evaluated the classification of the nodules (1-nodule correctly segmented and classified, 0-all other cases). The results obtained in stages 2 and 3 were compared with Ground Truth, which was common to all three stages. For each stage, diagnostic accuracy metrics were calculated for each AI service. Results Three software solutions (Celsus, Lunit INSIGHT CXR, and qXR) demonstrated diagnostic metrics that matched or surpassed the vendor specifications, and achieved the highest area under the receiver operating characteristic curve (AUC) of 0.956 [95% confidence interval (CI): 0.918 to 0.994]. However, when evaluated by three radiologists for accurate nodule segmentation and classification, all solutions performed below the vendor-declared metrics, with the highest AUC reaching 0.812 (95% CI: 0.744 to 0.879). Meanwhile, all AI services demonstrated 100% specificity at stages 2 and 3 of the study. Conclusions To ensure the reliability and applicability of AI-based software, it is crucial to validate performance metrics using high-quality datasets and engage radiologists in the evaluation process. Developers are recommended to improve the accuracy of the underlying models before allowing the standalone use of the software for lung nodule detection. The dataset created during the study may be accessed at https://mosmed.ai/datasets/mosmeddatargogksnalichiemiotsutstviemlegochnihuzlovtipvii/.
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Affiliation(s)
- Kirill Arzamasov
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
- MIREA – Russian Technological University, Moscow, Russian Federation
| | - Yuriy Vasilev
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
- Federal State Budgetary Institution “National Medical and Surgical Center named after N.I. Pirogov” of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
| | - Maria Zelenova
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Lev Pestrenin
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Yulia Busygina
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Tatiana Bobrovskaya
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Sergey Chetverikov
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - David Shikhmuradov
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Andrey Pankratov
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Yury Kirpichev
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Valentin Sinitsyn
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
- Lomonosov Moscow State University, Moscow, Russian Federation
| | - Irina Son
- Federal State Budgetary Educational Institution of Further Professional Education “Russian Medical Academy of Continuous Professional Education” of the Ministry of Healthcare of the Russian Federation, Moscow, Russian Federation
| | - Olga Omelyanskaya
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
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Lin DF, Li HL, Liu T, Lv XF, Xie CM, Ou XM, Guan J, Zhang Y, Yan WB, He ML, Mao MY, Zhao X, Zhong LZ, Chen WH, Chen QY, Mai HQ, Peng RJ, Tian J, Tang LQ, Dong D. Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma. J Natl Cancer Inst 2024; 116:1294-1302. [PMID: 38637942 DOI: 10.1093/jnci/djae081] [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: 08/08/2023] [Revised: 01/09/2024] [Accepted: 04/01/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma is limited because of their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in locally recurrent nasopharyngeal carcinoma. METHODS This multicenter, retrospective study included 921 patients with locally recurrent nasopharyngeal carcinoma. A machine learning signature and nomogram based on pretreatment magnetic resonance imaging features were developed for predicting overall survival in a training cohort and validated in 2 independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA-sequencing analysis. RESULTS The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving concordance indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables statistically improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with statistically distinct overall survival rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-sequencing analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. CONCLUSIONS A magnetic resonance imaging-based radiomic signature predicted overall survival more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for locally recurrent nasopharyngeal carcinoma patients.
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Affiliation(s)
- Da-Feng Lin
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Guangzhou, China
- Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Hai-Lin Li
- School of Engineering Medicine, Beihang University, Beijing, China
- Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Ting Liu
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Guangzhou, China
- Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
- Department of Breast Surgery, Breast Disease Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiao-Fei Lv
- State Key Laboratory of Oncology in South China, Guangzhou, China
- Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chuan-Miao Xie
- State Key Laboratory of Oncology in South China, Guangzhou, China
- Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiao-Min Ou
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian Guan
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ye Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wen-Bin Yan
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Mei-Lin He
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meng-Yuan Mao
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xun Zhao
- Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Lian-Zhen Zhong
- Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Wen-Hui Chen
- Department of Oncology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Qiu-Yan Chen
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Guangzhou, China
- Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Hai-Qiang Mai
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Guangzhou, China
- Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Rou-Jun Peng
- State Key Laboratory of Oncology in South China, Guangzhou, China
- Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
- Department of VIP Inpatient, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Key Laboratory of Kidney Diseases, Beijing, China
| | - Lin-Quan Tang
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Guangzhou, China
- Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Di Dong
- Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- National Key Laboratory of Kidney Diseases, Beijing, China
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Guo R, Wei J, Sun L, Yu B, Chang G, Liu D, Zhang S, Yao Z, Xu M, Bu L. A survey on advancements in image-text multimodal models: From general techniques to biomedical implementations. Comput Biol Med 2024; 178:108709. [PMID: 38878398 DOI: 10.1016/j.compbiomed.2024.108709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 06/01/2024] [Accepted: 06/03/2024] [Indexed: 07/24/2024]
Abstract
With the significant advancements of Large Language Models (LLMs) in the field of Natural Language Processing (NLP), the development of image-text multimodal models has garnered widespread attention. Current surveys on image-text multimodal models mainly focus on representative models or application domains, but lack a review on how general technical models influence the development of domain-specific models, which is crucial for domain researchers. Based on this, this paper first reviews the technological evolution of image-text multimodal models, from early explorations of feature space to visual language encoding structures, and then to the latest large model architectures. Next, from the perspective of technological evolution, we explain how the development of general image-text multimodal technologies promotes the progress of multimodal technologies in the biomedical field, as well as the importance and complexity of specific datasets in the biomedical domain. Then, centered on the tasks of image-text multimodal models, we analyze their common components and challenges. After that, we summarize the architecture, components, and data of general image-text multimodal models, and introduce the applications and improvements of image-text multimodal models in the biomedical field. Finally, we categorize the challenges faced in the development and application of general models into external factors and intrinsic factors, further refining them into 2 external factors and 5 intrinsic factors, and propose targeted solutions, providing guidance for future research directions. For more details and data, please visit our GitHub page: https://github.com/i2vec/A-survey-on-image-text-multimodal-models.
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Affiliation(s)
- Ruifeng Guo
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Jingxuan Wei
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Linzhuang Sun
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Bihui Yu
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Guiyong Chang
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Dawei Liu
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Sibo Zhang
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Zhengbing Yao
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Mingjun Xu
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Liping Bu
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
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Zhang S, Li K, Sun Y, Wan Y, Ao Y, Zhong Y, Liang M, Wang L, Chen X, Pei X, Hu Y, Chen D, Li M, Shan H. Deep Learning for Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study. Int J Radiat Oncol Biol Phys 2024; 119:1590-1600. [PMID: 38432286 DOI: 10.1016/j.ijrobp.2024.02.035] [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: 11/02/2023] [Revised: 02/02/2024] [Accepted: 02/18/2024] [Indexed: 03/05/2024]
Abstract
PURPOSE To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation therapy treatment planning. METHODS AND MATERIALS In this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by 2 experts via consensus were used as ground truth. A 3-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in 3 validation cohorts. The AI tool was compared against 12 board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance. Additionally, our previously established radiomics model for predicting pathologic complete response was used to compare AI-generated and ground truth contours, to assess the potential of the AI contouring tool in radiomics analysis. RESULTS The AI tool demonstrated good GTV contouring performance in multicenter validation cohorts, with median DSC values of 0.865, 0.876, and 0.866 and median average surface distance values of 0.939, 0.789, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of 12 board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = .003-0.048), reduced the intra- and interobserver variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pathologic complete response prediction performance for these contours (P = .430) was observed. CONCLUSIONS Our AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicates its potential for GTV contouring during radiation therapy treatment planning and radiomics studies.
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Affiliation(s)
- Shuaitong Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Kunwei Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China; Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Yuchen Sun
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Yun Wan
- Department of Radiology, Xinyi City People's Hospital, Xinyi, Guangdong, China
| | - Yong Ao
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China; State Key Laboratory of Oncology in South China, Guangdong Esophageal Cancer Institute, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Yinghua Zhong
- Department of Radiology, The Third People's Hospital of Zhuhai, Zhuhai, Guangdong, China
| | - Mingzhu Liang
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Lizhu Wang
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Xiangmeng Chen
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Xiaofeng Pei
- Department of Radiation Oncology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Yi Hu
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China; State Key Laboratory of Oncology in South China, Guangdong Esophageal Cancer Institute, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Man Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China.
| | - Hong Shan
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China; Department of Interventional Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China.
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Chapla D, Chorya HP, Ishfaq L, Khan A, Vr S, Garg S. An Artificial Intelligence (AI)-Integrated Approach to Enhance Early Detection and Personalized Treatment Strategies in Lung Cancer Among Smokers: A Literature Review. Cureus 2024; 16:e66688. [PMID: 39268329 PMCID: PMC11390952 DOI: 10.7759/cureus.66688] [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: 06/26/2024] [Accepted: 08/11/2024] [Indexed: 09/15/2024] Open
Abstract
Lung cancer (LC) is a significant global health issue, particularly among smokers, and is characterized by high rates of incidence and mortality. This comprehensive review offers detailed insights into the potential of artificial intelligence (AI) to revolutionize early detection and personalized treatment strategies for LC. By critically evaluating the limitations of conventional methodologies, we emphasize the innovative potential of AI-driven risk prediction models and imaging analyses to enhance diagnostic precision and improve patient outcomes. Our in-depth analysis of the current state of AI integration in LC care highlights the achievements and challenges encountered in real-world applications, thereby shedding light on practical implementation. Furthermore, we examined the profound implications of AI on treatment response, survival rates, and patient satisfaction, addressing ethical considerations to ensure responsible deployment. In the future, we will outline emerging technologies, anticipate potential barriers to their adoption, and identify areas for further research, emphasizing the importance of collaborative efforts to fully harness the transformative potential of AI in reshaping LC therapy. Ultimately, this review underscores the transformative impact of AI on LC care and advocates for a collective commitment to innovation, collaboration, and ethical stewardship in healthcare.
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Affiliation(s)
- Deep Chapla
- Medicine, Jiangsu University, Zhenjiang, CHN
| | | | - Lyluma Ishfaq
- Medicine, Directorate of Health Services Kashmir, Srinagar, IND
| | - Afrasayab Khan
- Internal Medicine, Central Michigan University College of Medicine, Saginaw, USA
| | - Subrahmanyan Vr
- Internal Medicine Pediatrics, Armed Forces Medical College, Pune, IND
| | - Sheenam Garg
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
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Bhangale PN, Kashikar SV, Kasat PR, Shrivastava P, Kumari A. A Comprehensive Review on the Role of MRI in the Assessment of Supratentorial Neoplasms: Comparative Insights Into Adult and Pediatric Cases. Cureus 2024; 16:e67553. [PMID: 39310617 PMCID: PMC11416707 DOI: 10.7759/cureus.67553] [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: 08/03/2024] [Accepted: 08/23/2024] [Indexed: 09/25/2024] Open
Abstract
Magnetic resonance imaging (MRI) is a critical diagnostic tool in assessing supratentorial neoplasms, offering unparalleled detail and specificity in brain imaging. Supratentorial neoplasms in the cerebral hemispheres, basal ganglia, thalamus, and other structures above the tentorium cerebelli present significant diagnostic and therapeutic challenges. These challenges vary notably between adult and pediatric populations due to differences in tumor types, biological behavior, and patient management strategies. This comprehensive review explores the role of MRI in diagnosing, planning treatment, monitoring response, and detecting recurrence in supratentorial neoplasms, providing comparative insights into adult and pediatric cases. The review begins with an overview of the epidemiology and pathophysiology of these tumors in different age groups, followed by a detailed examination of standard and advanced MRI techniques, including diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), and magnetic resonance spectroscopy (MRS). We discuss the specific imaging characteristics of various neoplasms and the importance of tailored approaches to optimize diagnostic accuracy and therapeutic efficacy. The review also addresses the technical and interpretative challenges unique to pediatric imaging and the implications for long-term patient outcomes. By highlighting the comparative utility of MRI in adult and pediatric cases, this review aims to enhance the understanding of its pivotal role in managing supratentorial neoplasms. It underscores the necessity of age-specific diagnostic and therapeutic strategies. Emerging MRI technologies and future research directions are also discussed, emphasizing the potential for advancements in personalized imaging approaches and improved patient care across all age groups.
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Affiliation(s)
- Paritosh N Bhangale
- Radiodiagnosis, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Shivali V Kashikar
- Radiodiagnosis, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Paschyanti R Kasat
- Radiodiagnosis, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Priyal Shrivastava
- Radiodiagnosis, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Anjali Kumari
- Radiodiagnosis, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Zhang Z, Yu G, Eresen A, Chen Z, Yu Z, Yaghmai V, Zhang Z. Dendritic cell vaccination combined with irreversible electroporation for treating pancreatic cancer-a narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2024; 12:77. [PMID: 39118942 PMCID: PMC11304422 DOI: 10.21037/atm-23-1882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 02/25/2024] [Indexed: 08/10/2024]
Abstract
Background and Objective Pancreatic ductal adenocarcinoma (PDAC) is 3rd most lethal cancer in the USA leading to a median survival of six months and less than 5% 5-year overall survival (OS). As the only potentially curative treatment, surgical resection is not suitable for up to 90% of the patients with PDAC due to late diagnosis. Highly fibrotic PDAC with an immunosuppressive tumor microenvironment restricts cytotoxic T lymphocyte (CTL) infiltration and functions causing limited success with systemic therapies like dendritic cell (DC)-based immunotherapy. In this study, we investigated the potential benefits of irreversible electroporation (IRE) ablation therapy in combination with DC vaccine therapy against PDAC. Methods We performed a literature search to identify studies focused on DC vaccine therapy and IRE ablation to boost therapeutic response against PDAC indexed in PubMed, Web of Science, and Scopus until February 20th, 2023. Key Content and Findings IRE ablation destructs tumor structure while preserving extracellular matrix and blood vessels facilitating local inflammation. The studies demonstrated IRE ablation reduces tumor fibrosis and promotes CTL tumor infiltration to PDAC tumors in addition to boosting immune response in rodent models. The administration of the DC vaccine following IRE ablation synergistically enhances therapeutic response and extends OS rates compared to the use of DC vaccination or IRE alone. Moreover, the implementation of data-driven approaches further allows dynamic and longitudinal monitoring of therapeutic response and OS following IRE plus DC vaccine immunoablation. Conclusions The combination of IRE ablation and DC vaccine immunotherapy is a potent strategy to enhance the therapeutic outcomes in patients with PDAC.
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Affiliation(s)
- Zigeng Zhang
- Department of Radiological Sciences, University of California Irvine, Irvine, CA, USA
| | - Guangbo Yu
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, USA
| | - Aydin Eresen
- Department of Radiological Sciences, University of California Irvine, Irvine, CA, USA
| | - Zhilin Chen
- Department of Human Biology and Business Administration, University of Southern California, Los Angeles, CA, USA
| | - Zeyang Yu
- Information School, University of Washington, Seattle, WA, USA
| | - Vahid Yaghmai
- Department of Radiological Sciences, University of California Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA, USA
| | - Zhuoli Zhang
- Department of Radiological Sciences, University of California Irvine, Irvine, CA, USA
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA, USA
- Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, CA, USA
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Guo J, Miao J, Sun W, Li Y, Nie P, Xu W. Predicting bone metastasis-free survival in non-small cell lung cancer from preoperative CT via deep learning. NPJ Precis Oncol 2024; 8:161. [PMID: 39068240 PMCID: PMC11283482 DOI: 10.1038/s41698-024-00649-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 07/09/2024] [Indexed: 07/30/2024] Open
Abstract
Accurate prediction of bone metastasis-free survival (BMFS) after complete surgical resection in patients with non-small cell lung cancer (NSCLC) may facilitate appropriate follow-up planning. The aim of this study was to establish and validate a preoperative CT-based deep learning (DL) signature to predict BMFS in NSCLC patients. We performed a retrospective analysis of 1547 NSCLC patients who underwent complete surgical resection, followed by at least 36 months of monitoring at two hospitals. We constructed a DL signature from multiparametric CT images using 3D convolutional neural networks, and we integrated this signature with clinical-imaging factors to establish a deep learning clinical-imaging signature (DLCS). We evaluated performance using Harrell's concordance index (C-index) and the time-dependent receiver operating characteristic. We also assessed the risk of bone metastasis (BM) in NSCLC patients at different clinical stages using DLCS. The DL signature successfully predicted BM, with C-indexes of 0.799 and 0.818 for the validation cohorts. DLCS outperformed the DL signature with corresponding C-indexes of 0.806 and 0.834. Ranges for area under the curve at 1, 2, and 3 years were 0.820-0.865 for internal and 0.860-0.884 for external validation cohorts. Furthermore, DLCS successfully stratified patients with different clinical stages of NSCLC as high- and low-risk groups for BM (p < 0.05). CT-based DL can predict BMFS in NSCLC patients undergoing complete surgical resection, and may assist in the assessment of BM risk for patients at different clinical stages.
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Affiliation(s)
- Jia Guo
- Department of Radiology, The Affiliated Hospital of Qingdao University, 266001, Qingdao, China
| | - Jianguo Miao
- College of Computer Science and Technology, Qingdao University, 266071, Qingdao, China
| | - Weikai Sun
- Department of Radiology, Qilu Hospital of Shandong University, 250012, Jinan, Shandong, China
| | - Yanlei Li
- Third department of medical oncology, Qingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, China
| | - Pei Nie
- Department of Radiology, The Affiliated Hospital of Qingdao University, 266001, Qingdao, China.
| | - Wenjian Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, 266001, Qingdao, China.
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Wu L, Li S, Wu C, Wu S, Lin Y, Wei D. Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer. BMC Med Imaging 2024; 24:189. [PMID: 39060962 PMCID: PMC11282842 DOI: 10.1186/s12880-024-01353-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 07/02/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND The purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC). METHODS 50 cases of MM and 180 cases of IBC with ultrasound Breast Imaging Reporting and Data System 4 category were recruited (training cohort, n = 161, validation cohort, n = 69). Based on PyRadiomics and ResNet50 extractors, radiomics and deep learning features were extracted, respectively. Based on supervised machine learning methods such as logistic regression, random forest, and support vector machine, as well as unsupervised machine learning methods using K-means clustering analysis, the differences in features between MM and IBC were analyzed to develop DLRN. The performance of DLRN had been evaluated by receiver operating characteristic curve, calibration, and clinical practicality. RESULTS Supervised machine learning results showed that compared with radiomics models, especially random forest models, deep learning models were better at recognizing MM and IBC. The area under the curve (AUC) of the validation cohort was 0.84, the accuracy was 0.83, the sensitivity was 0.73, and the specificity was 0.83. Compared to radiomics or deep learning models, DLRN even further improved discrimination ability (AUC of 0.90 and 0.90, accuracy of 0.83 and 0.88 for training and validation cohorts), which had better clinical benefits and good calibratability. In addition, the information heterogeneity of deep learning features in MM and IBC was validated again through unsupervised machine learning clustering analysis, indicating that MM had a unique features phenotype. CONCLUSION The DLRN developed based on radiomics and deep learning features of ultrasound images has potential clinical value in effectively distinguishing between MM and IBC. DLRN breaks through visual limitations and quantifies more image information related to MM based on computers, further utilizing machine learning to effectively utilize this information for clinical decision-making. As DLRN becomes an autonomous screening system, it will improve the recognition rate of MM in grassroots hospitals and reduce the possibility of incorrect treatment and overtreatment.
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Affiliation(s)
- Linyong Wu
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Songhua Li
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Chaojun Wu
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Shaofeng Wu
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Yan Lin
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Dayou Wei
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China.
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Pahud de Mortanges A, Luo H, Shu SZ, Kamath A, Suter Y, Shelan M, Pöllinger A, Reyes M. Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging. NPJ Digit Med 2024; 7:195. [PMID: 39039248 PMCID: PMC11263688 DOI: 10.1038/s41746-024-01190-w] [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: 09/16/2023] [Accepted: 07/15/2024] [Indexed: 07/24/2024] Open
Abstract
Explainable artificial intelligence (XAI) has experienced a vast increase in recognition over the last few years. While the technical developments are manifold, less focus has been placed on the clinical applicability and usability of systems. Moreover, not much attention has been given to XAI systems that can handle multimodal and longitudinal data, which we postulate are important features in many clinical workflows. In this study, we review, from a clinical perspective, the current state of XAI for multimodal and longitudinal datasets and highlight the challenges thereof. Additionally, we propose the XAI orchestrator, an instance that aims to help clinicians with the synopsis of multimodal and longitudinal data, the resulting AI predictions, and the corresponding explainability output. We propose several desirable properties of the XAI orchestrator, such as being adaptive, hierarchical, interactive, and uncertainty-aware.
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Affiliation(s)
| | - Haozhe Luo
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Shelley Zixin Shu
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Amith Kamath
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Yannick Suter
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mohamed Shelan
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Alexander Pöllinger
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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Cai W, Guo K, Chen Y, Shi Y, Chen J. Sub-regional CT Radiomics for the Prediction of Ki-67 Proliferation Index in Gastrointestinal Stromal Tumors: A Multi-center Study. Acad Radiol 2024:S1076-6332(24)00421-5. [PMID: 39033048 DOI: 10.1016/j.acra.2024.06.036] [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: 04/12/2024] [Revised: 06/18/2024] [Accepted: 06/22/2024] [Indexed: 07/23/2024]
Abstract
RATIONALE AND OBJECTIVES The objective was to assess and examine radiomics models derived from contrast-enhanced CT for their predictive capacity using the sub-regional radiomics regarding the Ki-67 proliferation index (PI) in patients with pathologically confirmed gastrointestinal stromal tumors (GIST). METHODS In this retrospective study, a total of 412 GIST patients across three institutions (223 from center 1, 106 from center 2, and 83 from center 3) was enrolled. Radiomic features were derived from various sub-regions of the tumor region of interest employing the K-means approach. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify features correlated with Ki-67 PI level in GIST patients. A support vector machine (SVM) model was then constructed to predict the high level of Ki-67 (Ki-67 index >8%), drawing on the radiomics features from each sub-region within the training cohort. RESULTS After features selection process, 6, 9, 9, 7 features were obtained to construct SVM models based on sub-region 1, 2, 3 and the entire tumor, respectively. Among different models, the model developed by the sub-region 1 achieved an area under the receiver operating characteristic curve (AUC) of 0.880 (95% confidence interval [CI]: 0.830 to 0.919), 0.852 (95% CI: 0.770-0.914), 0.799 (95% CI: 0.697-0.879) in the training, external test set 1, and 2, respectively. CONCLUSION The results of the present study suggested that SVM model based on the sub-regional radiomics features had the potential of predicting Ki-67 PI level in patients with GIST.
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Affiliation(s)
- Wemin Cai
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou 325000, China; Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Kun Guo
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yongxian Chen
- Department of Chest cancer, Xiamen Second People's Hospital, Xiamen 36100, China
| | - Yubo Shi
- Department of Pulmonary, Yueqing People's Hospital, Wenzhou 325000, China
| | - Junkai Chen
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou 325000, China.
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Tang T, Zhou Z, Chen M, Li N, Sun J, Chen Z, Xiao T, Wang X, Zhang L, Wang Y, Zhang H, Zheng X, Chen B, Ye F, Guan J. Plasma Metabolic Profiles-Based Prediction of Induction Chemotherapy Efficacy in Nasopharyngeal Carcinoma: Results of a Bidirectional Clinical Trial. Clin Cancer Res 2024; 30:2925-2936. [PMID: 38713248 PMCID: PMC11247322 DOI: 10.1158/1078-0432.ccr-23-3608] [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: 11/22/2023] [Revised: 02/23/2024] [Accepted: 05/03/2024] [Indexed: 05/08/2024]
Abstract
PURPOSE The efficacy of induction chemotherapy (IC) as a primary treatment for advanced nasopharyngeal carcinoma (NPC) remains a topic of debate, with a lack of dependable biomarkers for predicting its efficacy. This study seeks to establish a predictive classifier using plasma metabolomics profiles. PATIENTS AND METHODS A total of 166 NPC patients enrolled in the clinical trial NCT05682703 who were undergoing IC were included in the study. Plasma lipoprotein profiles were obtained using 1H-nuclear magnetic resonance before and after IC treatment. An artificial intelligence-assisted radiomics method was developed to effectively evaluate its efficacy. Metabolic biomarkers were identified through a machine learning approach based on a discovery cohort and subsequently validated in a validation cohort that mimicked the most unfavorable real-world scenario. RESULTS Our research findings indicate that the effectiveness of IC varies among individual patients, with a correlation observed between efficacy and changes in metabolite profiles. Using machine learning techniques, it was determined that the extreme gradient boosting model exhibited notable efficacy, attaining an area under the curve (AUC) value of 0.792 (95% CI, 0.668-0.913). In the validation cohort, the model exhibited strong stability and generalizability, with an AUC of 0.786 (95% CI, 0.533-0.922). CONCLUSIONS In this study, we found that dysregulation of plasma lipoprotein may result in resistance to IC in NPC patients. The prediction model constructed based on the plasma metabolites' profile has good predictive capabilities and potential for real-world generalization. This discovery has implications for the development of treatment strategies and may offer insight into potential targets for enhancing the effectiveness of IC.
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Affiliation(s)
- Tingxi Tang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Zhenhua Zhou
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
- Chronic Airways Diseases Laboratory, Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Min Chen
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
- Department of Radiation Oncology, Peking University Shenzhen Hospital, Shenzhen, China.
| | - Nan Li
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Jianda Sun
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
- Department of Radiation Oncology, Meizhou People’s Hospital, Meizhou, China.
| | - Zekai Chen
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Ting Xiao
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Xiaoqing Wang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Longshan Zhang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Yingqiao Wang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Hanbin Zhang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Xiuting Zheng
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Bei Chen
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Feng Ye
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Jian Guan
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
- Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou, China.
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Hamza MN, Koziel S, Pietrenko-Dabrowska A. Design and experimental validation of a metamaterial-based sensor for microwave imaging in breast, lung, and brain cancer detection. Sci Rep 2024; 14:16177. [PMID: 39003304 PMCID: PMC11246499 DOI: 10.1038/s41598-024-67103-9] [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: 01/15/2024] [Accepted: 07/08/2024] [Indexed: 07/15/2024] Open
Abstract
This study proposes an innovative geometry of a microstrip sensor for high-resolution microwave imaging (MWI). The main intended application of the sensor is early detection of breast, lung, and brain cancer. The proposed design consists of a microstrip patch antenna fed by a coplanar waveguide with a metamaterial (MTM) layer-based lens implemented on the back side, and an artificial magnetic conductor (AMC) realized on as a separate layer. The analysis of the AMC's permeability and permittivity demonstrate that the structure exhibits negative epsilon (ENG) qualities near the antenna resonance point. In addition, reflectivity, transmittance, and absorption are also studied. The sensor prototype has been manufactures using the FR4 laminate. Excellent electrical and field characteristics of the structure are confirmed through experimental validation. At the resonance frequency of 4.56 GHz, the realized gain reaches 8.5 dBi, with 3.8 dBi gain enhancement contributed by the AMC. The suitability of the presented sensor for detecting brain tumors, lung cancer, and breast cancer has been corroborated through extensive simulation-based experiments performed using the MWI system model, which employs four copies of the proposed sensor, as well as the breast, lung, and brain phantoms. As demonstrated, the directional radiation pattern and enhanced gain of the sensor enable precise tumor size discrimination. The proposed sensor offers competitive performance in comparison the state-of-the-art sensors described in the recent literature, especially with respect to as gain, pattern directivity, and impedance matching, all being critical for MWI.
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Affiliation(s)
- Musa N Hamza
- Department of Physics, College of Science, University of Raparin, Sulaymaniyah, 46012, Iraq.
| | - Slawomir Koziel
- Engineering Optimization & Modeling Center, Reykjavik University, 102, Reykjavik, Iceland
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland
| | - Anna Pietrenko-Dabrowska
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland
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Wang L, Zhou L, Liu S, Zheng Y, Liu Q, Yu M, Lu X, Lei W, Chen G. Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI. Prog Neuropsychopharmacol Biol Psychiatry 2024; 133:111026. [PMID: 38735428 DOI: 10.1016/j.pnpbp.2024.111026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/26/2024] [Accepted: 05/09/2024] [Indexed: 05/14/2024]
Abstract
It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted radiomic features of subcortical structures in high-resolution T1-weighted MRI. Different combinations of four feature selection methods (analysis of variance, Kruskal-Wallis, recursive feature elimination and relief) and ten classification algorithms were used to identify the most robust combined models for distinguishing IGD patients from HCs. Furthermore, a nomogram incorporating radiomic signatures and independent clinical factors was developed. Calibration curve and decision curve analyses were used to evaluate the nomogram. The combination of analysis of variance selector and logistic regression classifier identified that the radiomic model constructed with 20 features from the right caudate nucleus and amygdala showed better IGD screening performance. The radiomic model produced good areas under the curves (AUCs) in the training, validation and test cohorts (AUCs of 0.961, 0.903 and 0.895, respectively). In addition, sex, internet addiction test scores and radiomic scores were included in the nomogram as independent risk factors for IGD. Analysis of the correction curve and decision curve showed that the clinical-radiomic model has good reliability (C-index: 0.987). The nomogram incorporating radiomic features of subcortical structures and clinical characteristics achieved satisfactory classification performance and could serve as an effective tool for distinguishing IGD patients from HCs.
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Affiliation(s)
- Li Wang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Li Zhou
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Shengdan Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Yurong Zheng
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Qianhan Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Minglin Yu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Xiaofei Lu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Wei Lei
- Department of Psychiatry, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Guangxiang Chen
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China.
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Moro F, Ciancia M, Zace D, Vagni M, Tran HE, Giudice MT, Zoccoli SG, Mascilini F, Ciccarone F, Boldrini L, D'Antonio F, Scambia G, Testa AC. Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review. Int J Cancer 2024. [PMID: 38989809 DOI: 10.1002/ijc.35092] [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: 04/12/2024] [Revised: 06/03/2024] [Accepted: 06/27/2024] [Indexed: 07/12/2024]
Abstract
The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.
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Affiliation(s)
- Francesca Moro
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Marianna Ciancia
- Dipartimento Universitario Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
- Dipartimento di Salute della Donna e del Bambino, Università degli studi di Padova, Padova, Italy
| | - Drieda Zace
- Infectious Disease Clinic, Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | - Marica Vagni
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Huong Elena Tran
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Maria Teresa Giudice
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Sofia Gambigliani Zoccoli
- Dipartimento Universitario Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
- Department of Medical and Surgical Sciences for Mother, Child and Adult, University of Modena and Reggio Emilia, Azienda Ospedaliero Universitaria Policlinico, Modena, Italy
| | - Floriana Mascilini
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Francesca Ciccarone
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | | | - Giovanni Scambia
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
- Dipartimento Universitario Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Antonia Carla Testa
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
- Dipartimento Universitario Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
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Sun C, Jiang C, Wang X, Ma S, Zhang D, Jia W. MR-Based Radiomics Predicts CDK6 Expression and Prognostic Value in High-grade Glioma. Acad Radiol 2024:S1076-6332(24)00364-7. [PMID: 38964985 DOI: 10.1016/j.acra.2024.06.006] [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: 02/28/2024] [Revised: 05/28/2024] [Accepted: 06/03/2024] [Indexed: 07/06/2024]
Abstract
RATIONALE AND OBJECTIVES This study aims to assess the prognostic value of Cyclin-dependent kinases 6 (CDK6) expression levels and establish a machine learning-based radiomics model for predicting the expression levels of CDK6 in high-grade gliomas (HGG). MATERIALS AND METHODS Clinical parameters and genomic data were extracted from 310 HGG patients in the Cancer Genome Atlas (TCGA) database and 27 patients in the Repository of Molecular Brain Neoplasia Data (REMBRANDT) database. Univariate and multivariate Cox regression, as well as Kaplan-Meier analysis, were performed for prognosis analysis. The correlation between immune cell Infiltration with CDK6 was assessed using spearman correlation analysis. Radiomic features were extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) in the Cancer Imaging Archive (TCIA) database (n = 82) and REMBRANDT database (n = 27). Logistic regression (LR) and support vector machine (SVM) were employed to establish the radiomics model for predicting CDK6 expression. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were utilized to assess the predictive performance of the radiomics model. Generate radiomic scores (RS) based on the LR model. An RS-based nomogram was constructed to predict the prognosis of HGG. RESULTS CDK6 was significantly overexpressed in HGG tissues and was related to lower overall survival. A significant elevation in infiltrating M0 macrophages was observed in the CDK6 high group (P < 0.001). The LR radiomics model for the prediction of CDK6 expression levels (AUC=0.810 in the training cohort, AUC = 0.784 after cross-validation, AUC=0.750 in the testing cohort) was established utilizing three radiomic features. The predictive efficiencies of the RS-based nomogram, as measured by AUC, were 0.769 for 1-year, 0.815 for 3-year, and 0.780 for 5-year, respectively. CONCLUSION The expression level of CDK6 can impact the prognosis of patients with HGG. The expression level of HGG can be noninvasively prognosticated utilizing a radiomics model.
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Affiliation(s)
- Chen Sun
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road, South Fourth Ring Road, Beijing, China
| | - Chenggang Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road, South Fourth Ring Road, Beijing, China
| | - Xi Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road, South Fourth Ring Road, Beijing, China
| | - Shunchang Ma
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road, South Fourth Ring Road, Beijing, China
| | - Dainan Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road, South Fourth Ring Road, Beijing, China
| | - Wang Jia
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road, South Fourth Ring Road, Beijing, China.
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Szymaszek P, Tyszka-Czochara M, Ortyl J. Application of Photoactive Compounds in Cancer Theranostics: Review on Recent Trends from Photoactive Chemistry to Artificial Intelligence. Molecules 2024; 29:3164. [PMID: 38999115 PMCID: PMC11243723 DOI: 10.3390/molecules29133164] [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: 05/23/2024] [Revised: 06/14/2024] [Accepted: 06/25/2024] [Indexed: 07/14/2024] Open
Abstract
According to the World Health Organization (WHO) and the International Agency for Research on Cancer (IARC), the number of cancer cases and deaths worldwide is predicted to nearly double by 2030, reaching 21.7 million cases and 13 million fatalities. The increase in cancer mortality is due to limitations in the diagnosis and treatment options that are currently available. The close relationship between diagnostics and medicine has made it possible for cancer patients to receive precise diagnoses and individualized care. This article discusses newly developed compounds with potential for photodynamic therapy and diagnostic applications, as well as those already in use. In addition, it discusses the use of artificial intelligence in the analysis of diagnostic images obtained using, among other things, theranostic agents.
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Affiliation(s)
- Patryk Szymaszek
- Department of Biotechnology and Physical Chemistry, Faculty of Chemical Engineering and Technology, Cracow University of Technology, Warszawska 24, 31-155 Kraków, Poland
| | | | - Joanna Ortyl
- Department of Biotechnology and Physical Chemistry, Faculty of Chemical Engineering and Technology, Cracow University of Technology, Warszawska 24, 31-155 Kraków, Poland
- Photo HiTech Ltd., Bobrzyńskiego 14, 30-348 Kraków, Poland
- Photo4Chem Ltd., Juliusza Lea 114/416A-B, 31-133 Cracow, Poland
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Liu X, Qin X, Luo Q, Qiao J, Xiao W, Zhu Q, Liu J, Zhang C. A Transvaginal Ultrasound-Based Deep Learning Model for the Noninvasive Diagnosis of Myometrial Invasion in Patients with Endometrial Cancer: Comparison with Radiologists. Acad Radiol 2024; 31:2818-2826. [PMID: 38182443 DOI: 10.1016/j.acra.2023.12.035] [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: 11/09/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 01/07/2024]
Abstract
RATIONALE AND OBJECTIVES This study aimed to determine the feasibility of using the deep learning (DL) method to determine the degree (whether myometrial invasion [MI] >50%) of MI in patients with endometrial cancer (EC) based on ultrasound (US) images. MATERIALS AND METHODS From September 2017 to April 2023, 1289 US images of 604 patients with EC who underwent surgical resection at center 1, center 2 or center 3 were obtained and divided into a training set and an internal validation set. Ninety-five patients from center 4 and center 5 were randomly selected as the external testing set according to the same criteria as those for the primary cohort. This study evaluated three DL models trained on the training set and tested them on the validation and testing sets. The models' performance was analyzed based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), and the performance of the models was subsequently compared with that of 15 radiologists. RESULTS In the final clinical diagnosis of MI in patients with EC, EfficientNet-B6 showed the best performance in the testing set in terms of area under the curve (AUC) [0.814, 95% CI (0.746-0.882]; accuracy [0.802, 95% CI (0.733-0.855]; sensitivity [0.623]; specificity [0.879]; positive likelihood ratio (PLR) [6.750]; and negative likelihood ratio (NLR) [0.389]. The diagnostic efficacy of EfficientNet-B6 was significantly better than that of the 15 radiologists, with an average diagnostic accuracy of 0.681, average AUC of 0.678, AUC of the best performance of 0.739, accuracy of 0.716, sensitivity of 0.806, specificity 0.672, PLR2.457, and NLR 0.289. CONCLUSION Based on the preoperative US images of patients with EC, the DL model can accurately determine the degree of endometrial MI; the performance of this model is significantly better than that of radiologists, and it can effectively assist in clinical treatment decisions.
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Affiliation(s)
- Xiaoling Liu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.); Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China (X.L., X.Q.)
| | - Xiachuan Qin
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.); Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China (X.L., X.Q.)
| | - Qi Luo
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.)
| | - Jing Qiao
- Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China (J.Q.)
| | - Weihan Xiao
- North Sichuan Medical College, Nanchong, China (W.X.)
| | - Qiwei Zhu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.)
| | - Jian Liu
- Department of Ultrasound, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China (J.L.)
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.).
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Armocida D, Zancana G, Bianconi A, Cofano F, Pesce A, Ascenzi BM, Bini P, Marchioni E, Garbossa D, Frati A. Brain metastases: Comparing clinical radiological differences in patients with lung and breast cancers treated with surgery. World Neurosurg X 2024; 23:100391. [PMID: 38725976 PMCID: PMC11079529 DOI: 10.1016/j.wnsx.2024.100391] [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: 02/28/2024] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
Purpose Brain metastases (BMs) most frequently originate from the primary tumors of the lung and breast. Survival in patients with BM can improve if they are detected early. No studies attempt to consider all potential surgical predictive factors together by including clinical, radiological variables for their recognition. Methods The study aims to simultaneously analyze all clinical, radiologic, and surgical variables on a cohort of 314 patients with surgically-treated BMs to recognize the main features and differences between the two histotypes. Results The two groups consisted of 179 BM patients from lung cancer (Group A) and 135 patients from breast cancer (Group B). Analysis showed that BMs from breast carcinoma are more likely to appear in younger patients, tend to occur in the infratentorial site and are frequently found in patients who have other metastases outside of the brain (46 %, p = 0.05), particularly in bones. On the other hand, BMs from lung cancer often occur simultaneously with primitive diagnosis, are more commonly cystic, and have a larger edema volume. However, no differences were found in the extent of resection, postoperative complications or the presence of decreased postoperative performance status. Conclusion The data presented in this study reveal that while the two most prevalent forms of BM exhibit distinctions with respect to clinical onset, age, tumor location, presence of extra-cranial metastases, and lesion morphology from a strictly surgical standpoint, they are indistinguishable with regard to outcome, demonstrating comparable resection rates and a low risk of complications.
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Affiliation(s)
- Daniele Armocida
- Experimental Neurosurgery Unit, IRCCS “Neuromed”, via Atinense 18, 86077, Pozzilli, IS, Italy
- Department of Neuroscience “Rita Levi Montalcini”, Neurosurgery Unit, University of Turin, Via cherasco 15, 10126, Turin, TO, Italy
| | - Giuseppa Zancana
- Human Neurosciences Department Neurosurgery Division “La Sapienza” University, Policlinico Umberto 6 I, viale del Policlinico 155, 00161, Rome, RM, Italy
| | - Andrea Bianconi
- Department of Neuroscience “Rita Levi Montalcini”, Neurosurgery Unit, University of Turin, Via cherasco 15, 10126, Turin, TO, Italy
| | - Fabio Cofano
- Department of Neuroscience “Rita Levi Montalcini”, Neurosurgery Unit, University of Turin, Via cherasco 15, 10126, Turin, TO, Italy
| | - Alessandro Pesce
- Neurosurgery Unit Department, Santa Maria Goretti Hospital, Via Guido Reni, 04100, Latina, LT, Italy
| | - Brandon Matteo Ascenzi
- Independent Neuroresearcher Member of Marie Curie Alumni Association (MCAA), Via Dante Alighieri 103, 03012, Anagni, FR, Italy
| | - Paola Bini
- IRCCS foundation Istituto Neurologico Nazionale Mondino, Via Mondino, 2, 27100, Pavia, Italy
| | - Enrico Marchioni
- IRCCS foundation Istituto Neurologico Nazionale Mondino, Via Mondino, 2, 27100, Pavia, Italy
| | - Diego Garbossa
- Department of Neuroscience “Rita Levi Montalcini”, Neurosurgery Unit, University of Turin, Via cherasco 15, 10126, Turin, TO, Italy
| | - Alessandro Frati
- Experimental Neurosurgery Unit, IRCCS “Neuromed”, via Atinense 18, 86077, Pozzilli, IS, Italy
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