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Tan HQ, Cai J, Tay SH, Sim AY, Huang L, Chua ML, Tang Y. Cluster-based radiomics reveal spatial heterogeneity of bevacizumab response for treatment of radiotherapy-induced cerebral necrosis. Comput Struct Biotechnol J 2024; 23:43-51. [PMID: 38125298 PMCID: PMC10730953 DOI: 10.1016/j.csbj.2023.11.040] [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: 08/02/2023] [Revised: 11/21/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
Background Bevacizumab is used in the treatment of radiation necrosis (RN), which is a debilitating toxicity following head and neck radiotherapy. However, there is no biomarker to predict if a patient would respond to bevacizumab. Purpose We aimed to develop a cluster-based radiomics approach to characterize the spatial heterogeneity of RN and map their responses to bevacizumab. Methods 118 consecutive nasopharyngeal carcinoma patients diagnosed with RN were enrolled. We divided 152 lesions from the patients into 101 for training, and 51 for validation. We extracted voxel-level radiomics features from each lesion segmented on T1-weighted+contrast and T2 FLAIR sequences of pre- and post-bevacizumab magnetic resonance images, followed by a three-step analysis involving individual- and population-level clustering, before delta-radiomics to derive five radiomics clusters within the lesions. We tested the association of each cluster with response to bevacizumab and developed a clinico-radiomics model using clinical predictors and cluster-specific features. Results 71 (70.3%) and 34 (66.7%) lesions had responded to bevacizumab in the training and validation datasets, respectively. Two radiomics clusters were spatially mapped to the edema region, and the volume changes were significantly associated with bevacizumab response (OR:11.12 [95% CI: 2.54-73.47], P = 0.004; and 1.63[1.07-2.78], P = 0.042). The combined clinico-radiomics model based on textural features extracted from the most significant cluster improved the prediction of bevacizumab response, compared with a clinical-only model (AUC:0.755 [0.645-0.865] to 0.852 [0.764-0.940], training; 0.708 [0.554-0.861] to 0.816 [0.699-0.933], validation). Conclusion Our radiomics approach yielded intralesional resolution, enabling a more refined feature selection for predicting bevacizumab efficacy in the treatment of RN.
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Affiliation(s)
- Hong Qi Tan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Jinhua Cai
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Shi Hui Tay
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore
| | - Adelene Y.L. Sim
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore
| | - Luo Huang
- Department of Radiation Oncology, Chongqing University Cancer Hospital, People's Republic of China
| | - Melvin L.K. Chua
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore
- Oncology Academic Programme, Duke-NUS Medical School, Singapore
| | - Yamei Tang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
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Luo HJ, Ren JL, Mei Guo L, Liang Niu J, Song XL. MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma. Eur J Radiol Open 2024; 13:100592. [PMID: 39149534 PMCID: PMC11324846 DOI: 10.1016/j.ejro.2024.100592] [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: 04/18/2024] [Revised: 07/08/2024] [Accepted: 07/14/2024] [Indexed: 08/17/2024] Open
Abstract
Background Human epidermal growth factor receptor 2 (HER2) is a tumor biomarker with significant prognostic and therapeutic implications for invasive ductal breast carcinoma (IDC). Objective This study aimed to explore the effectiveness of a multisequence magnetic resonance imaging (MRI)-based machine learning radiomics model in classifying the expression status of HER2, including HER2-positive, HER2-low, and HER2 completely negative (HER2-zero), among patients with IDC. Methods A total of 402 female patients with IDC confirmed through surgical pathology were enrolled and subsequently divided into a training group (n = 250, center I) and a validation group (n = 152, center II). Radiomics features were extracted from the preoperative MRI. A simulated annealing algorithm was used for key feature selection. Two classification tasks were performed: task 1, the classification of HER2-positive vs. HER2-negative (HER2-low and HER2-zero), and task 2, the classification of HER2-low vs. HER2-zero. Logistic regression, random forest (RF), and support vector machine were conducted to establish radiomics models. The performance of the models was evaluated using the area under the curve (AUC) of the operating characteristics (ROC). Results In total, 4506 radiomics features were extracted from multisequence MRI. A radiomics model for prediction of expression state of HER2 was successfully developed. Among the three classification algorithms, RF achieved the highest performance in classifying HER2-positive from HER2-negative and HER2-low from HER2-zero, with AUC values of 0.777 and 0.731, respectively. Conclusions Machine learning-based MRI radiomics may aid in the non-invasive prediction of the different expression status of HER2 in IDC.
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Affiliation(s)
- Hong-Jian Luo
- Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University (The First People's Hospital of Zunyi), Zuiyi, Guizhou province, China
| | | | - Li Mei Guo
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi province, China
| | - Jin Liang Niu
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi province, China
| | - Xiao-Li Song
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi province, China
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Hatamikia S, George G, Schwarzhans F, Mahbod A, Woitek R. Breast MRI radiomics and machine learning-based predictions of response to neoadjuvant chemotherapy - How are they affected by variations in tumor delineation? Comput Struct Biotechnol J 2024; 23:52-63. [PMID: 38125296 PMCID: PMC10730996 DOI: 10.1016/j.csbj.2023.11.016] [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: 05/14/2023] [Revised: 11/08/2023] [Accepted: 11/08/2023] [Indexed: 12/23/2023] Open
Abstract
Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variability. A quantitative assessment of the impact of variations in these delineations on the performance of the radiomics predictors is required to develop robust radiomics based prediction models. In this study, we developed radiomics models for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with two different breast cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired prior to treatment (baseline MRI scans). Different mathematical operations such as erosion, smoothing, dilation, randomization, and ellipse fitting were applied to the original VOIs delineated by experts to simulate variations of segmentation masks. The effects of such VOI modifications on various steps of the radiomics workflow, including feature extraction, feature selection, and prediction performance, were evaluated. Using manual tumor VOIs and radiomics features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was achieved for human epidermal growth receptor 2 positive and triple-negative breast cancer, respectively. For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes. At most 28% of the selected features were similar to manual VOIs when different VOI delineation data were used. Differences in VOI delineation affect different steps of radiomics analysis, and their quantification is therefore important for development of standardized radiomics research.
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Affiliation(s)
- Sepideh Hatamikia
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
- Austrian Center for Medical Innovation and Technology (ACMIT), Viktor Kaplan-Straße 2/1, Wiener Neustadt 2700, Austria
| | - Geevarghese George
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
| | - Florian Schwarzhans
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
| | - Amirreza Mahbod
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
| | - Ramona Woitek
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
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Le TK, Comte V, Darcourt J, Razzouk-Cadet M, Rollet AC, Orlhac F, Humbert O. Performance and Clinical Impact of Radiomics and 3D-CNN Models for the Diagnosis of Neurodegenerative Parkinsonian Syndromes on 18 F-FDOPA PET. Clin Nucl Med 2024; 49:924-930. [PMID: 39104036 DOI: 10.1097/rlu.0000000000005392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
PURPOSE The aim of this study was to compare the performance and added clinical value of a semiautomated radiomics model and an automated 3-dimensinal convolutional neural network (3D-CNN) model for diagnosing neurodegenerative parkinsonian syndromes on 18 F-FDOPA PET images. PATIENTS AND METHODS This 2-center retrospective study included 687 patients with motor symptoms consistent with parkinsonian syndrome. All patients underwent 18 F-FDOPA brain PET scans, acquired on 3 PET systems from 2 different hospitals, and classified as pathological or nonpathological (by an expert nuclear physician). Artificial intelligence models were trained to replicate this medical expert's classification using 2 pipelines. The radiomics pipeline was semiautomated and involved manually segmenting the bilateral caudate and putamen nuclei; 43 radiomic features were extracted and combined using the support vector machine method. The deep learning pipeline was fully automatic and used a 3D-CNN model. Both models were trained on 417 patients and tested on an internal (n = 100) and an external (n = 170) test set. The final models' performance was evaluated using balanced accuracy and compared with that of a junior medical expert and nonexpert nuclear physician. RESULTS On the internal test set, the 3D-CNN model outperformed the radiomic model with a balanced accuracy of 99% (vs 96%). It led to diagnostic performance similar to that of a junior medical expert (only 1 in 100 patients misclassified by both). On the external test set from a less experienced hospital, the 3D-CNN model allowed physicians to correctly reclassify the diagnosis of 10 out 170 patients (6%). CONCLUSIONS The developed 3D-CNN model can automatically diagnose neurodegenerative parkinsonian syndromes, also reducing diagnostic errors by 6% in less-experienced hospitals.
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Affiliation(s)
- Thi Khuyen Le
- From the Université Côte D'Azur, CNRS, Inserm, iBV, Nice, France
| | | | - Jacques Darcourt
- Department of Nuclear Medicine, Centre Antoine Lacassagne, UCA, Nice, France
| | | | | | - Fanny Orlhac
- Laboratory of Translational Imaging in Oncology (LITO-U1288), Curie Institute, Inserm, PSL University, Orsay, France
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Salimi Y, Hajianfar G, Mansouri Z, Sanaat A, Amini M, Shiri I, Zaidi H. Organomics: A Concept Reflecting the Importance of PET/CT Healthy Organ Radiomics in Non-Small Cell Lung Cancer Prognosis Prediction Using Machine Learning. Clin Nucl Med 2024; 49:899-908. [PMID: 39192505 DOI: 10.1097/rlu.0000000000005400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
PURPOSE Non-small cell lung cancer is the most common subtype of lung cancer. Patient survival prediction using machine learning (ML) and radiomics analysis proved to provide promising outcomes. However, most studies reported in the literature focused on information extracted from malignant lesions. This study aims to explore the relevance and additional value of information extracted from healthy organs in addition to tumoral tissue using ML algorithms. PATIENTS AND METHODS This study included PET/CT images of 154 patients collected from available online databases. The gross tumor volume and 33 volumes of interest defined on healthy organs were segmented using nnU-Net deep learning-based segmentation. Subsequently, 107 radiomic features were extracted from PET and CT images (Organomics). Clinical information was combined with PET and CT radiomics from organs and gross tumor volumes considering 19 different combinations of inputs. Finally, different feature selection (FS; 5 methods) and ML (6 algorithms) algorithms were tested in a 3-fold data split cross-validation scheme. The performance of the models was quantified in terms of the concordance index (C-index) metric. RESULTS For an input combination of all radiomics information, most of the selected features belonged to PET Organomics and CT Organomics. The highest C-index (0.68) was achieved using univariate C-index FS method and random survival forest ML model using CT Organomics + PET Organomics as input as well as minimum depth FS method and CoxPH ML model using PET Organomics as input. Considering all 17 combinations with C-index higher than 0.65, Organomics from PET or CT images were used as input in 16 of them. CONCLUSIONS The selected features and C-indices demonstrated that the additional information extracted from healthy organs of both PET and CT imaging modalities improved the ML performance. Organomics could be a step toward exploiting the whole information available from multimodality medical images, contributing to the emerging field of digital twins in health care.
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Affiliation(s)
- Yazdan Salimi
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Ghasem Hajianfar
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Zahra Mansouri
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Amirhosein Sanaat
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Mehdi Amini
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
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Zhou W, Zhou Y, Zhang X, Huang T, Zhang R, Li D, Xie X, Wang Y, Xu M. Development and Validation of an Explainable Machine Learning Model for Identification of Hyper-Functioning Parathyroid Glands from High-Frequency Ultrasonographic Images. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1506-1514. [PMID: 39054242 DOI: 10.1016/j.ultrasmedbio.2024.05.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/25/2024] [Accepted: 05/30/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVE To develop and validate a machine learning (ML) model based on high-frequency ultrasound (HFUS) images with the aim to identify the functional status of parathyroid glands (PTGs) in secondary hyper-parathyroidism (SHPT) patients. METHODS This retrospective study enrolled 60 SHPT patients (27 female, 33 male; mean age: 51.2 years) with 184 PTGs detected from February 2016 to June 2022. All enrollments underwent single-photon emission computed tomography/computed tomography and contrast-enhanced ultrasound examinations. The PTGs were randomly divided into training (n = 147) and testing datasets (n = 37). Four effective ML classifiers were used and combined models incorporating multi-modal HFUS visual signs and radiomics features was constructed based on the optimal classifier. Model performance was compared in terms of discrimination, calibration and clinical utility. The Shapley additive explanation method was used to explain and visualize the main predictors of the optimal model. RESULTS This model, using a random forest classifier algorithm, outperformed other classifiers. Based on optimal classifier features, the model constructed from ultrasound visual and ML features achieved a favorable performance in the prediction of hyper-functioning PTGs. Compared with the traditional visual model, the ultrasound-based ML model achieved significant (p = 0.03) improvement (area under the curve: 0.859 vs. 0.629) and higher sensitivity (100.0% vs. 94.1%) and accuracy (86.5% vs. 67.6%). Among the predictors attributed to model development, large size and high echogenic heterogeneity of PTGs in ultrasonographic images were more often associated with high risk of hyper-functioning PTGs. CONCLUSION The ultrasound-based ML model for identifying hyper-functioning PTGs in SHPT patients showed good performance and interpretability using high-frequency ultrasonographic images, which may facilitate clinical management.
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Affiliation(s)
- Wenwen Zhou
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yu Zhou
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518055, China
| | - Xiaoer Zhang
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Tongyi Huang
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Rui Zhang
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Di Li
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Xiaoyan Xie
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yi Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518055, China.
| | - Ming Xu
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
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Xie C, Yu X, Tan N, Zhang J, Su W, Ni W, Li C, Zhao Z, Xiang Z, Shao L, Li H, Wu J, Cao Z, Jin J, Jin X. Combined deep learning and radiomics in pretreatment radiation esophagitis prediction for patients with esophageal cancer underwent volumetric modulated arc therapy. Radiother Oncol 2024; 199:110438. [PMID: 39013503 DOI: 10.1016/j.radonc.2024.110438] [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: 02/22/2024] [Revised: 07/06/2024] [Accepted: 07/12/2024] [Indexed: 07/18/2024]
Abstract
PURPOSE To develop a combined radiomics and deep learning (DL) model in predicting radiation esophagitis (RE) of a grade ≥ 2 for patients with esophageal cancer (EC) underwent volumetric modulated arc therapy (VMAT) based on computed tomography (CT) and radiation dose (RD) distribution images. MATERIALS AND METHODS A total of 273 EC patients underwent VMAT were retrospectively reviewed and enrolled from two centers and divided into training (n = 152), internal validation (n = 66), and external validation (n = 55) cohorts, respectively. Radiomic and dosiomic features along with DL features using convolutional neural networks were extracted and screened from CT and RD images to predict RE. The performance of these models was evaluated and compared using the area under curve (AUC) of the receiver operating characteristic curves (ROC). RESULTS There were 5 and 10 radiomic and dosiomic features were screened, respectively. XGBoost achieved a best AUC of 0.703, 0.694 and 0.801, 0.729 with radiomic and dosiomic features in the internal and external validation cohorts, respectively. ResNet34 achieved a best prediction AUC of 0.642, 0.657 and 0.762, 0.737 for radiomics based DL model (DLR) and RD based DL model (DLD) in the internal and external validation cohorts, respectively. Combined model of DLD + Dosiomics + clinical factors achieved a best AUC of 0.913, 0.821 and 0.805 in the training, internal, and external validation cohorts, respectively. CONCLUSION Although the dose was not responsible for the prediction accuracy, the combination of various feature extraction methods was a factor in improving the RE prediction accuracy. Combining DLD with dosiomic features was promising in the pretreatment prediction of RE for EC patients underwent VMAT.
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Affiliation(s)
- Congying Xie
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Xianwen Yu
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang 315000, PR China
| | - Ninghang Tan
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang 315000, PR China
| | - Jicheng Zhang
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Wanyu Su
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang 315000, PR China
| | - Weihua Ni
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang 315000, PR China
| | - Chenyu Li
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Zeshuo Zhao
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Ziqing Xiang
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Li Shao
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Heng Li
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Jianping Wu
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China; Department of Radiotherapy, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People' s Hospital, Quzhou 324000, PR China
| | - Zhuo Cao
- Department of Respiratory, Lishui People's Hospital, Lishui 323000, PR China.
| | - Juebin Jin
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China.
| | - Xiance Jin
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China; School of Basic Medical Science, Wenzhou Medical University, Wenzhou 325000, PR China.
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Foltyn-Dumitru M, Mahmutoglu MA, Brugnara G, Kessler T, Sahm F, Wick W, Heiland S, Bendszus M, Vollmuth P, Schell M. Shape matters: unsupervised exploration of IDH-wildtype glioma imaging survival predictors. Eur Radiol 2024:10.1007/s00330-024-11042-6. [PMID: 39251442 DOI: 10.1007/s00330-024-11042-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 06/15/2024] [Accepted: 08/07/2024] [Indexed: 09/11/2024]
Abstract
OBJECTIVES This study examines clustering based on shape radiomic features and tumor volume to identify IDH-wildtype glioma phenotypes and assess their impact on overall survival (OS). MATERIALS AND METHODS This retrospective study included 436 consecutive patients diagnosed with IDH-wt glioma who underwent preoperative MR imaging. Alongside the total tumor volume, nine distinct shape radiomic features were extracted using the PyRadiomics framework. Different imaging phenotypes were identified using partition around medoids (PAM) clustering on the training dataset (348/436). The prognostic efficacy of these phenotypes in predicting OS was evaluated on the test dataset (88/436). External validation was performed using the public UCSF glioma dataset (n = 397). A decision-tree algorithm was employed to determine the relevance of features associated with cluster affiliation. RESULTS PAM clustering identified two clusters in the training dataset: Cluster 1 (n = 233) had a higher proportion of patients with higher sphericity and elongation, while Cluster 2 (n = 115) had a higher proportion of patients with higher maximum 3D diameter, surface area, axis lengths, and tumor volume (p < 0.001 for each). OS differed significantly between clusters: Cluster 1 showed a median OS of 23.8 compared to 11.4 months of Cluster 2 in the holdout test dataset (p = 0.002). Multivariate Cox regression showed improved performance with cluster affiliation over clinical data alone (C index 0.67 vs 0.59, p = 0.003). Cluster-based models outperformed the models with tumor volume alone (evidence ratio: 5.16-5.37). CONCLUSION Data-driven clustering reveals imaging phenotypes, highlighting the improved prognostic power of combining shape-radiomics with tumor volume, thereby outperforming predictions based on tumor volume alone in high-grade glioma survival outcomes. CLINICAL RELEVANCE STATEMENT Shape-radiomics and volume-based cluster analyses of preoperative MRI scans can reveal imaging phenotypes that improve the prediction of OS in patients with IDH-wild type gliomas, outperforming currently known models based on tumor size alone or clinical parameters. KEY POINTS Shape radiomics and tumor volume clustering in IDH-wildtype gliomas are investigated for enhanced prognostic accuracy. Two distinct phenotypic clusters were identified with different median OSs. Integrating shape radiomics and volume-based clustering enhances OS prediction in IDH-wildtype glioma patients.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Mustafa Ahmed Mahmutoglu
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Kessler
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
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Ebrahimi B. Radiomics analysis of cerebral blood flow suggests a possible link between perfusion homogeneity and poor glioblastoma multiforme prognosis. Biomed Phys Eng Express 2024; 10:065006. [PMID: 39214103 DOI: 10.1088/2057-1976/ad7593] [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: 07/31/2024] [Accepted: 08/30/2024] [Indexed: 09/04/2024]
Abstract
Objectives. This study investigates the association between cerebral blood flow (CBF) and overall survival (OS) in glioblastoma multiforme (GBM) patients receiving chemoradiation. Identifying CBF biomarkers could help predict patient response to this treatment, facilitating the development of personalized therapeutic strategies.Materials and Methods. This retrospective study analyzed CBF data from dynamic susceptibility contrast (DSC) MRI in 30 newly diagnosed GBM patients (WHO grade IV). Radiomics features were extracted from CBF maps, tested for robustness, and correlated with OS. Kaplan-Meier analysis was used to assess the predictive value of radiomic features significantly associated with OS, aiming to stratify patients into groups with distinct post-treatment survival outcomes.Results. While mean relative CBF and CBV failed to serve as independent prognostic markers for OS, the prognostic potential of radiomic features extracted from CBF maps was explored. Ten out of forty-three radiomic features with highest intraclass correlation coefficients (ICC > 0.9), were selected for characterization. While Correlation and Zone Size Variance (ZSV) features showed significant OS correlations, indicating prognostic potential, Kaplan-Meier analysis did not significantly stratify patients based on these features. Visual analysis of the graphs revealed a predominant association between the identified radiomic features and OS under two years. Focusing on this subgroup, Correlation, ZSV, and Gray-Level Nonuniformity (GLN) emerged as significant, suggesting that a lack of heterogeneity in perfusion patterns may be indicative of a poorer outcome. Kaplan-Meier analysis effectively stratified this cohort based on the features mentioned above. Receiver operating characteristic (ROC) analysis further validated their prognostic value, with ZSV demonstrating the highest sensitivity and specificity (0.75 and 0.85, respectively).Conclusion. Our findings underscored radiomics features sensitive to CBF heterogeneity as pivotal predictors for patient stratification. Our results suggest that these markers may have the potential to identify patients who are unlikely to benefit from standard chemoradiation therapy.
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Affiliation(s)
- Behzad Ebrahimi
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States of America
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10
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Kocak B, Keles A, Kose F, Sendur A. Quality of radiomics research: comprehensive analysis of 1574 unique publications from 89 reviews. Eur Radiol 2024:10.1007/s00330-024-11057-z. [PMID: 39237770 DOI: 10.1007/s00330-024-11057-z] [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: 06/18/2024] [Revised: 08/21/2024] [Accepted: 08/24/2024] [Indexed: 09/07/2024]
Abstract
PURPOSE This study aims to comprehensively evaluate the quality of radiomics research by examining unique papers from reviews using the radiomics quality score (RQS). METHODS A literature search was conducted in PubMed (last search date: April 14, 2024). Systematic or non-systematic reviews using the RQS to evaluate radiomic studies were potentially included. Exclusion was applied at two levels: first, at the review level, and second, at the study level (i.e., for the individual articles previously evaluated within the reviews). Score-wise and item-wise analyses were performed, along with trend, multivariable, and subgroup analyses based on baseline study characteristics and validation methods. RESULTS A total of 1574 unique papers (published online between 1999 and 2023) from 89 reviews were included in the final analysis. The median RQS percentage was 31% with an IQR of 25% (25th-75th percentiles, 14-39%). A positive correlation between median RQS percentage and publication year (2014-2023) was found, with Kendall's tau coefficient of 0.908 (p < 0.001), suggesting an improvement in quality over time. The quality of radiomics publications significantly varied according to different subfields of radiology (p < 0.001). Around one-third of the publications (32%) lacked a separate validation set. Papers with internal validation (54%) dominated those with external validation (14%). Higher-quality validation practices were significantly associated with better RQS percentage scores, independent of the validation's effect on the final score. Item-wise analysis revealed significant shortcomings in several areas. CONCLUSION Radiomics research quality is low but improving according to RQS. Significant variation exists across radiology subfields. Critical areas were identified for targeted improvement. CLINICAL RELEVANCE STATEMENT Our study shows that the quality of radiomics research is generally low but improving over time, with item-wise analysis highlighting critical areas needing improvement. It also reveals that the quality of radiomics research differs across subfields and validation methods. KEY POINTS Overall quality of radiomics research remains low and highly variable, although a significant positive trend suggests an improvement in quality over time. Considerable variations exist in the quality of radiomics publications across different subfields of radiology and validation types. The item-wise analysis highlights several critical areas requiring attention, emphasizing the need for targeted improvements.
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Affiliation(s)
- Burak Kocak
- Department of Radiology, Basaksehir Cam and Sakura City Hospital, University of Health Sciences, Istanbul, Turkey.
| | - Ali Keles
- Department of Radiology, Basaksehir Cam and Sakura City Hospital, University of Health Sciences, Istanbul, Turkey
| | - Fadime Kose
- Department of Radiology, Basaksehir Cam and Sakura City Hospital, University of Health Sciences, Istanbul, Turkey
| | - Abdurrezzak Sendur
- Department of Radiology, Basaksehir Cam and Sakura City Hospital, University of Health Sciences, Istanbul, Turkey
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11
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Buchner JA, Kofler F, Mayinger M, Christ SM, Brunner TB, Wittig A, Menze B, Zimmer C, Meyer B, Guckenberger M, Andratschke N, El Shafie RA, Debus J, Rogers S, Riesterer O, Schulze K, Feldmann HJ, Blanck O, Zamboglou C, Ferentinos K, Bilger-Zähringer A, Grosu AL, Wolff R, Piraud M, Eitz KA, Combs SE, Bernhardt D, Rueckert D, Wiestler B, Peeken JC. Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy. Neuro Oncol 2024; 26:1638-1650. [PMID: 38813990 PMCID: PMC11376458 DOI: 10.1093/neuonc/noae098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND Surgical resection is the standard of care for patients with large or symptomatic brain metastases (BMs). Despite improved local control after adjuvant stereotactic radiotherapy, the risk of local failure (LF) persists. Therefore, we aimed to develop and externally validate a pre-therapeutic radiomics-based prediction tool to identify patients at high LF risk. METHODS Data were collected from A Multicenter Analysis of Stereotactic Radiotherapy to the Resection Cavity of BMs (AURORA) retrospective study (training cohort: 253 patients from 2 centers; external test cohort: 99 patients from 5 centers). Radiomic features were extracted from the contrast-enhancing BM (T1-CE MRI sequence) and the surrounding edema (T2-FLAIR sequence). Different combinations of radiomic and clinical features were compared. The final models were trained on the entire training cohort with the best parameter set previously determined by internal 5-fold cross-validation and tested on the external test set. RESULTS The best performance in the external test was achieved by an elastic net regression model trained with a combination of radiomic and clinical features with a concordance index (CI) of 0.77, outperforming any clinical model (best CI: 0.70). The model effectively stratified patients by LF risk in a Kaplan-Meier analysis (P < .001) and demonstrated an incremental net clinical benefit. At 24 months, we found LF in 9% and 74% of the low and high-risk groups, respectively. CONCLUSIONS A combination of clinical and radiomic features predicted freedom from LF better than any clinical feature set alone. Patients at high risk for LF may benefit from stricter follow-up routines or intensified therapy.
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Affiliation(s)
- Josef A Buchner
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Florian Kofler
- Department of Informatics, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Helmholtz AI, Helmholtz Zentrum Munich, Neuherberg, Germany
| | - Michael Mayinger
- Department of Radiation Oncology, University of Zurich, Zurich, Switzerland
| | - Sebastian M Christ
- Department of Radiation Oncology, University of Zurich, Zurich, Switzerland
| | - Thomas B Brunner
- Department of Radiation Oncology, University Hospital Magdeburg, Magdeburg, Germany
| | - Andrea Wittig
- Department of Radiotherapy and Radiation Oncology, University Hospital Jena, Friedrich-Schiller University, Jena, Germany
| | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | | | - Rami A El Shafie
- Department of Radiation Oncology, University Medical Center Göttingen, Göttingen, Germany
- Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen Debus
- Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Susanne Rogers
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - Oliver Riesterer
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - Katrin Schulze
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - Horst J Feldmann
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - Oliver Blanck
- Saphir Radiosurgery Center Frankfurt and Northern Germany, Kiel, Germany
- Department of Radiation Oncology, University Medical Center Schleswig Holstein, Kiel, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
- Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany
| | - Konstantinos Ferentinos
- Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus
| | - Angelika Bilger-Zähringer
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
- Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany
| | - Anca L Grosu
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
- Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany
| | - Robert Wolff
- Saphir Radiosurgery Center Frankfurt and Northern Germany, Kiel, Germany
- Department of Neurosurgery, University Hospital Frankfurt, Frankfurt, Germany
| | - Marie Piraud
- Helmholtz AI, Helmholtz Zentrum Munich, Neuherberg, Germany
| | - Kerstin A Eitz
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Daniel Rueckert
- Institute for Artificial Intelligence and Informatics in Medicine, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
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12
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Zhang Y, Cui Y, Liu H, Chang C, Yin Y, Wang R. Prognostic nomogram combining 18F-FDG PET/CT radiomics and clinical data for stage III NSCLC survival prediction. Sci Rep 2024; 14:20557. [PMID: 39231973 PMCID: PMC11374974 DOI: 10.1038/s41598-024-71003-3] [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: 03/28/2024] [Accepted: 08/23/2024] [Indexed: 09/06/2024] Open
Abstract
The aim of this study was to establish and validate the precision of a novel radiomics approach that integrates 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT) scan data with clinical information to improve the prognostication of survival rates in patients diagnosed with stage III Non-Small Cell Lung Cancer (NSCLC) who are not candidates for surgery. We evaluated pretreatment 18F-FDG PET-CT scans from 156 individuals diagnosed with stage III inoperable NSCLC at Shandong Cancer Hospital. These individuals were divided into two groups: a training set comprising 110 patients and an internal validation set consisting of 46 patients. By employing random forest classifier and cox proportional hazards model , we identified and utilized relevant features to create predictive models and a nomogram. The effectiveness of these models was assessed through the use of the receiver operating characteristics(ROC) curves, Kaplan-Meier (KM) curves, and the application of the nomogram. Our findings showed that the combined model, which integrates both clinical and radiomic data, outperformed those based solely on clinical or radiomic features in predicting 3-year overall survival(OS). Furthermore, calibration plots revealed a high level of agreement between predicted and actual survival times. The research successfully established a predictive radiomics model that integrates 18F-FDG PET/CT imaging with clinical indicators to enhance survival predictions for patients with stage III inoperable NSCLC.
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Affiliation(s)
- Yalin Zhang
- Department of Radiation Oncology, The Third Affiliated Teaching Hospital of Xinjiang Medical University, Affiliated Cancer Hospital, Urumuqi, China
| | - Yongbin Cui
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Huiling Liu
- Department of Radiation Oncology, Binzhou People's Hospital, Binzhou, China
| | - Cheng Chang
- Department of Nuclear Medicine, The Third Affiliated Teaching Hospital of Xinjiang Medical University, Affiliated Cancer Hospital, Urumuqi, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
| | - Ruozheng Wang
- Department of Radiation Oncology, The Third Affiliated Teaching Hospital of Xinjiang Medical University, Affiliated Cancer Hospital, Urumuqi, China.
- Xinjiang Key Laboratory of Oncology, Urumqi, China.
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13
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Yang YF, Zhao E, Shi Y, Zhang H, Yang YY. Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models. Neuroradiology 2024:10.1007/s00234-024-03451-7. [PMID: 39225815 DOI: 10.1007/s00234-024-03451-7] [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: 03/25/2024] [Accepted: 08/09/2024] [Indexed: 09/04/2024]
Abstract
OBJECTIVE Research into the effectiveness and applicability of deep learning, radiomics, and their integrated models based on Magnetic Resonance Imaging (MRI) for preoperative differentiation between Primary Central Nervous System Lymphoma (PCNSL) and Glioblastoma (GBM), along with an exploration of the interpretability of these models. MATERIALS AND METHODS A retrospective analysis was performed on MRI images and clinical data from 261 patients across two medical centers. The data were split into a training set (n = 153, medical center 1) and an external test set (n = 108, medical center 2). Radiomic features were extracted using Pyradiomics to build the Radiomics Model. Deep learning networks, including the transformer-based MobileVIT Model and Convolutional Neural Networks (CNN) based ConvNeXt Model, were trained separately. By applying the "late fusion" theory, the radiomics model and deep learning model were fused to produce the optimal Max-Fusion Model. Additionally, Shapley Additive exPlanations (SHAP) and Grad-CAM were employed for interpretability analysis. RESULTS In the external test set, the Radiomics Model achieved an Area under the receiver operating characteristic curve (AUC) of 0.86, the MobileVIT Model had an AUC of 0.91, the ConvNeXt Model demonstrated an AUC of 0.89, and the Max-Fusion Model showed an AUC of 0.92. The Delong test revealed a significant difference in AUC between the Max-Fusion Model and the Radiomics Model (P = 0.02). CONCLUSION The Max-Fusion Model, combining different models, presents superior performance in distinguishing PCNSL and GBM, highlighting the effectiveness of model fusion for enhanced decision-making in medical applications. CLINICAL RELEVANCE STATEMENT The preoperative non-invasive differentiation between PCNSL and GBM assists clinicians in selecting appropriate treatment regimens and clinical management strategies.
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Affiliation(s)
- Yun-Feng Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Endong Zhao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China
| | - Yutong Shi
- Department of Neurology, Dalian University Affiliated Xinhua Hospital, Dalian, Liaoning, China
| | - Hao Zhang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Yuan-Yuan Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China.
- Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, 100049, China.
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14
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Zhuang Z, Lin J, Wan Z, Weng J, Yuan Z, Xie Y, Liu Z, Xie P, Mao S, Wang Z, Wang X, Huang M, Luo Y, Yu H. Radiogenomic profiling of global DNA methylation associated with molecular phenotypes and immune features in glioma. BMC Med 2024; 22:352. [PMID: 39218882 PMCID: PMC11367996 DOI: 10.1186/s12916-024-03573-y] [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: 04/10/2024] [Accepted: 08/20/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND The radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment. METHODS We apply the machine learning approaches to identify the magnetic resonance imaging (MRI) features that are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecular feature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA). RESULTS The generated MoRad models perform well for profiling each molecular feature with radiomic features, including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models have a remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we find that global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes. CONCLUSIONS Global DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.
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Affiliation(s)
- Zhuokai Zhuang
- Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Institute of Gastroenterology, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, China
- Ministry of Education, Key Laboratory of Human Microbiome and Chronic Diseases (Sun Yat-Sen University), Guangzhou, Guangdong, China
| | - Jinxin Lin
- Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Institute of Gastroenterology, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, China
- Ministry of Education, Key Laboratory of Human Microbiome and Chronic Diseases (Sun Yat-Sen University), Guangzhou, Guangdong, China
| | - Zixiao Wan
- Guangdong Institute of Gastroenterology, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, China
- Ministry of Education, Key Laboratory of Human Microbiome and Chronic Diseases (Sun Yat-Sen University), Guangzhou, Guangdong, China
| | - Jingrong Weng
- Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Institute of Gastroenterology, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, China
| | - Ze Yuan
- Guangdong Institute of Gastroenterology, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, China
- Ministry of Education, Key Laboratory of Human Microbiome and Chronic Diseases (Sun Yat-Sen University), Guangzhou, Guangdong, China
| | - Yumo Xie
- Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Institute of Gastroenterology, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, China
| | - Zongchao Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Cancer Epidemiology, Peking University Cancer Institute, Beijing, 100142, China
| | - Peiyi Xie
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, China
| | - Siyue Mao
- Image and Minimally Invasive Intervention Center, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, and Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zongming Wang
- Department of Neurosurgery and Pituitary Tumor Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiaolin Wang
- Guangdong Institute of Gastroenterology, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, China
- Ministry of Education, Key Laboratory of Human Microbiome and Chronic Diseases (Sun Yat-Sen University), Guangzhou, Guangdong, China
| | - Meijin Huang
- Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Institute of Gastroenterology, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, China
- Ministry of Education, Key Laboratory of Human Microbiome and Chronic Diseases (Sun Yat-Sen University), Guangzhou, Guangdong, China
| | - Yanxin Luo
- Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Institute of Gastroenterology, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, China
- Ministry of Education, Key Laboratory of Human Microbiome and Chronic Diseases (Sun Yat-Sen University), Guangzhou, Guangdong, China
| | - Huichuan Yu
- Guangdong Institute of Gastroenterology, Guangzhou, Guangdong, China.
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, China.
- Ministry of Education, Key Laboratory of Human Microbiome and Chronic Diseases (Sun Yat-Sen University), Guangzhou, Guangdong, China.
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Li Y, Deng J, Ma X, Li W, Wang Z. Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer. Eur Radiol 2024:10.1007/s00330-024-11036-4. [PMID: 39223336 DOI: 10.1007/s00330-024-11036-4] [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: 04/18/2024] [Revised: 06/09/2024] [Accepted: 08/07/2024] [Indexed: 09/04/2024]
Abstract
OBJECTIVES This study evaluates the accuracy of radiomics in predicting lymph node metastasis in non-small cell lung cancer, which is crucial for patient management and prognosis. METHODS Adhering to PRISMA and AMSTAR guidelines, we systematically reviewed literature from March 2012 to December 2023 using databases including PubMed, Web of Science, and Embase. Radiomics studies utilizing computed tomography (CT) and positron emission tomography (PET)/CT imaging were included. The quality of studies was appraised with QUADAS-2 and RQS tools, and the TRIPOD checklist assessed model transparency. Sensitivity, specificity, and AUC values were synthesized to determine diagnostic performance, with subgroup and sensitivity analyses probing heterogeneity and a Fagan plot evaluating clinical applicability. RESULTS Our analysis incorporated 42 cohorts from 22 studies. CT-based radiomics demonstrated a sensitivity of 0.84 (95% CI: 0.79-0.88, p < 0.01) and specificity of 0.82 (95% CI: 0.75-0.87, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.92), indicating no publication bias (p-value = 0.54 > 0.05). PET/CT radiomics showed a sensitivity of 0.82 (95% CI: 0.76-0.86, p < 0.01) and specificity of 0.86 (95% CI: 0.81-0.90, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.93), with a slight publication bias (p-value = 0.03 < 0.05). Despite high clinical utility, subgroup analysis did not clarify heterogeneity sources, suggesting influences from possible factors like lymph node location and small subgroup sizes. CONCLUSIONS Radiomics models show accuracy in predicting lung cancer lymph node metastasis, yet further validation with larger, multi-center studies is necessary. CLINICAL RELEVANCE STATEMENT Radiomics models using CT and PET/CT imaging may improve the prediction of lung cancer lymph node metastasis, aiding personalized treatment strategies. RESEARCH REGISTRATION UNIQUE IDENTIFYING NUMBER (UIN) International Prospective Register of Systematic Reviews (PROSPERO), CRD42023494701. This study has been registered on the PROSPERO platform with a registration date of 18 December 2023. https://www.crd.york.ac.uk/prospero/ KEY POINTS: The study explores radiomics for lung cancer lymph node metastasis detection, impacting surgery and prognosis. Radiomics improves the accuracy of lymph node metastasis prediction in lung cancer. Radiomics can aid in the prediction of lymph node metastasis in lung cancer and personalized treatment.
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Affiliation(s)
- Yuepeng Li
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, China
| | - Junyue Deng
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, China
- Institute of Respiratory Health, West China Hospital, Sichuan University, Chengdu, China
- Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- The Research Units of West China, Chinese Academy of Medical Sciences, West China Hospital, Chengdu, China
| | - Zhoufeng Wang
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, China.
- Institute of Respiratory Health, West China Hospital, Sichuan University, Chengdu, China.
- Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
- The Research Units of West China, Chinese Academy of Medical Sciences, West China Hospital, Chengdu, China.
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16
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Varriano G, Nardone V, Correra S, Mercaldo F, Santone A. An automatic radiomic-based approach for disease localization: A pilot study on COVID-19. Comput Med Imaging Graph 2024; 116:102411. [PMID: 38924800 DOI: 10.1016/j.compmedimag.2024.102411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/15/2024] [Accepted: 06/09/2024] [Indexed: 06/28/2024]
Abstract
Radiomics is an innovative field in Personalized Medicine to help medical specialists in diagnosis and prognosis. Mainly, the application of Radiomics to medical images requires the definition and delimitation of the Region Of Interest (ROI) on the medical image to extract radiomic features. The aim of this preliminary study is to define an approach that automatically detects the specific areas indicative of a particular disease and examines them to minimize diagnostic errors associated with false positives and false negatives. This approach aims to create a nxn grid on the DICOM image sequence and each cell in the matrix is associated with a region from which radiomic features can be extracted. The proposed procedure uses the Model Checking technique and produces as output the medical diagnosis of the patient, i.e., whether the patient under analysis is affected or not by a specific disease. Furthermore, the matrix-based method also localizes where appears the disease marks. To evaluate the performance of the proposed methodology, a case study on COVID-19 disease is used. Both results on disease identification and localization seem very promising. Furthermore, this proposed approach yields better results compared to methods based on the extraction of features using the whole image as a single ROI, as evidenced by improvements in Accuracy and especially Recall. Our approach supports the advancement of knowledge, interoperability and trust in the software tool, fostering collaboration among doctors, staff and Radiomics.
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Affiliation(s)
- Giulia Varriano
- University of Molise, Department of Medicine and Health Sciences "V. Tiberio", Campobasso 86100, Italy.
| | - Vittoria Nardone
- University of Molise, Department of Medicine and Health Sciences "V. Tiberio", Campobasso 86100, Italy.
| | - Simona Correra
- University of Molise, Department of Medicine and Health Sciences "V. Tiberio", Campobasso 86100, Italy.
| | - Francesco Mercaldo
- University of Molise, Department of Medicine and Health Sciences "V. Tiberio", Campobasso 86100, Italy.
| | - Antonella Santone
- University of Molise, Department of Medicine and Health Sciences "V. Tiberio", Campobasso 86100, Italy.
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17
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Hosseini MS, Aghamiri SMR, Fatemi Ardekani A, BagheriMofidi SM. Assessing the stability and discriminative ability of radiomics features in the tumor microenvironment: Leveraging peri-tumoral regions in vestibular schwannoma. Eur J Radiol 2024; 178:111654. [PMID: 39089057 DOI: 10.1016/j.ejrad.2024.111654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 07/20/2024] [Accepted: 07/25/2024] [Indexed: 08/03/2024]
Abstract
PURPOSE The tumor microenvironment (TME) plays a crucial role in tumor progression and treatment response. Radiomics offers a non-invasive approach to studying the TME by extracting quantitative features from medical images. In this study, we present a novel approach to assess the stability and discriminative ability of radiomics features in the TME of vestibular schwannoma (VS). METHODS Magnetic Resonance Imaging (MRI) data from 242 VS patients were analyzed, including contrast-enhanced T1-weighted (ceT1) and high-resolution T2-weighted (hrT2) sequences. Radiomics features were extracted from concentric peri-tumoral regions of varying sizes. The intraclass correlation coefficient (ICC) was used to assess feature stability and discriminative ability, establishing quantile thresholds for ICCmin and ICCmax. RESULTS The identified thresholds for ICCmin and ICCmax were 0.45 and 0.72, respectively. Features were classified into four categories: stable and discriminative (S-D), stable and non-discriminative (S-ND), unstable and discriminative (US-D), and unstable and non-discriminative (US-ND). Different feature groups exhibited varying proportions of S-D features across ceT1 and hrT2 sequences. The similarity of S-D features between ceT1 and hrT2 sequences was evaluated using Jaccard's index, with a value of 0.78 for all feature groups which is ranging from 0.68 (intensity features) to 1.00 (Neighbouring Gray Tone Difference Matrix (NGTDM) features). CONCLUSIONS This study provides a framework for identifying stable and discriminative radiomics features in the TME, which could serve as potential biomarkers or predictors of patient outcomes, ultimately improving the management of VS patients.
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Affiliation(s)
| | | | - Ali Fatemi Ardekani
- Department of Physics, Jackson State University, Jackson, MS, USA; Merit Health Central, Department of Radiation Oncology,Gamma Knife Center, Jackson, MS, USA.
| | - Seyed Mehdi BagheriMofidi
- Department of Biomedical Engineering, Aliabad Katoul Branch Islamic Azad University, Aliabad-e-Katoul, Iran.
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18
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Stefano A. Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research. Comput Biol Med 2024; 179:108827. [PMID: 38964244 DOI: 10.1016/j.compbiomed.2024.108827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/05/2024] [Accepted: 06/29/2024] [Indexed: 07/06/2024]
Abstract
Radiomics, the high-throughput extraction of quantitative imaging features from medical images, holds immense potential for advancing precision medicine in oncology and beyond. While radiomics applied to positron emission tomography (PET) imaging offers unique insights into tumor biology and treatment response, it is imperative to elucidate the challenges and constraints inherent in this domain to facilitate their translation into clinical practice. This review examines the challenges and limitations of applying radiomics to PET imaging, synthesizing findings from the last five years (2019-2023) and highlights the significance of addressing these challenges to realize the full clinical potential of radiomics in oncology and molecular imaging. A comprehensive search was conducted across multiple electronic databases, including PubMed, Scopus, and Web of Science, using keywords relevant to radiomics issues in PET imaging. Only studies published in peer-reviewed journals were eligible for inclusion in this review. Although many studies have highlighted the potential of radiomics in predicting treatment response, assessing tumor heterogeneity, enabling risk stratification, and personalized therapy selection, various challenges regarding the practical implementation of the proposed models still need to be addressed. This review illustrates the challenges and limitations of radiomics in PET imaging across various cancer types, encompassing both phantom and clinical investigations. The analyzed studies highlight the importance of reproducible segmentation methods, standardized pre-processing and post-processing methodologies, and the need to create large multicenter studies registered in a centralized database to promote the continuous validation and clinical integration of radiomics into PET imaging.
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Affiliation(s)
- Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, Italy.
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19
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Cheng DO, Khaw CR, McCabe J, Pennycuick A, Nair A, Moore DA, Janes SM, Jacob J. Predicting histopathological features of aggressiveness in lung cancer using CT radiomics: a systematic review. Clin Radiol 2024; 79:681-689. [PMID: 38853080 DOI: 10.1016/j.crad.2024.04.022] [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: 01/22/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE To examine the accuracy of CT radiomics to predict histopathological features of aggressiveness in lung cancer using a systematic review of test accuracy studies. METHODS Data sources searched included Medline, Embase, Web of Science, and Cochrane Library from up to 3 November 2023. Included studies reported test accuracy of CT radiomics models to detect the presence of: spread through air spaces (STAS), predominant adenocarcinoma pattern, adenocarcinoma grade, lymphovascular invasion (LVI), tumour infiltrating lymphocytes (TIL) and tumour necrosis, in patients with lung cancer. The primary outcome was test accuracy. Two reviewers independently assessed articles for inclusion and assessed methodological quality using the QUality Assessment of Diagnostic Accuracy Studies-2 tool. A single reviewer extracted data, which was checked by a second reviewer. Narrative data synthesis was performed. RESULTS Eleven studies were included in the final analysis. 10/11 studies were in East Asian populations. 4/11 studies investigated STAS, 6/11 investigated adenocarcinoma invasiveness or growth pattern, and 1/11 investigated LVI. No studies investigating TIL or tumour necrosis met inclusion criteria. Studies were of generally mixed to poor methodological quality. Reported accuracies for radiomic models ranged from 0.67 to 0.94. CONCLUSION Due to the high risk of bias and concerns regarding applicability, the evidence is inconclusive as to whether radiomic features can accurately predict prognostically important histopathological features of cancer aggressiveness. Many studies were excluded due to lack of external validation. Rigorously conducted prospective studies with sufficient external validity will be required for radiomic models to play a role in improving lung cancer outcomes.
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Affiliation(s)
- D O Cheng
- University College London, Department of Respiratory Medicine, UK
| | - C R Khaw
- University College London, Department of Respiratory Medicine, UK
| | - J McCabe
- University College London, Department of Respiratory Medicine, UK
| | - A Pennycuick
- University College London, Department of Respiratory Medicine, UK
| | - A Nair
- University College London, Department of Radiology, UK
| | - D A Moore
- University College London, Department of Pathology, UK
| | - S M Janes
- University College London, Department of Respiratory Medicine, UK
| | - J Jacob
- University College London, Department of Respiratory Medicine, UK; University College London, Department of Radiology, UK.
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20
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Fiz F, Rossi N, Langella S, Conci S, Serenari M, Ardito F, Cucchetti A, Gallo T, Zamboni GA, Mosconi C, Boldrini L, Mirarchi M, Cirillo S, Ruzzenente A, Pecorella I, Russolillo N, Borzi M, Vara G, Mele C, Ercolani G, Giuliante F, Cescon M, Guglielmi A, Ferrero A, Sollini M, Chiti A, Torzilli G, Ieva F, Viganò L. Radiomics of Intrahepatic Cholangiocarcinoma and Peritumoral Tissue Predicts Postoperative Survival: Development of a CT-Based Clinical-Radiomic Model. Ann Surg Oncol 2024; 31:5604-5614. [PMID: 38797789 DOI: 10.1245/s10434-024-15457-9] [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: 02/07/2024] [Accepted: 04/28/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND For many tumors, radiomics provided a relevant prognostic contribution. This study tested whether the computed tomography (CT)-based textural features of intrahepatic cholangiocarcinoma (ICC) and peritumoral tissue improve the prediction of survival after resection compared with the standard clinical indices. METHODS All consecutive patients affected by ICC who underwent hepatectomy at six high-volume centers (2009-2019) were considered for the study. The arterial and portal phases of CT performed fewer than 60 days before surgery were analyzed. A manual segmentation of the tumor was performed (Tumor-VOI). A 5-mm volume expansion then was applied to identify the peritumoral tissue (Margin-VOI). RESULTS The study enrolled 215 patients. After a median follow-up period of 28 months, the overall survival (OS) rate was 57.0%, and the progression-free survival (PFS) rate was 34.9% at 3 years. The clinical predictive model of OS had a C-index of 0.681. The addition of radiomic features led to a progressive improvement of performances (C-index of 0.71, including the portal Tumor-VOI, C-index of 0.752 including the portal Tumor- and Margin-VOI, C-index of 0.764, including all VOIs of the portal and arterial phases). The latter model combined clinical variables (CA19-9 and tumor pattern), tumor indices (density, homogeneity), margin data (kurtosis, compacity, shape), and GLRLM indices. The model had performance equivalent to that of the postoperative clinical model including the pathology data (C-index of 0.765). The same results were observed for PFS. CONCLUSIONS The radiomics of ICC and peritumoral tissue extracted from preoperative CT improves the prediction of survival. Both the portal and arterial phases should be considered. Radiomic and clinical data are complementary and achieve a preoperative estimation of prognosis equivalent to that achieved in the postoperative setting.
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Affiliation(s)
- Francesco Fiz
- Nuclear Medicine Unit, Department of Diagnostic Imaging, Ente Ospedaliero "Ospedali Galliera", Genoa, Italy
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital, Tübingen, Germany
| | - Noemi Rossi
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Serena Langella
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, Turin, Italy
| | - Simone Conci
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynaecology and Pediatrics, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Matteo Serenari
- General Surgery and Transplant Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Francesco Ardito
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Alessandro Cucchetti
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy
- Department of General Surgery, Morgagni-Pierantoni Hospital, Forlì, Italy
| | - Teresa Gallo
- Department of Radiology, Mauriziano Umberto I Hospital, Turin, Italy
| | - Giulia A Zamboni
- Department of Radiology, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Cristina Mosconi
- Department of Radiology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
| | - Luca Boldrini
- Department of Radiology, Radiation Oncology and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | | | - Stefano Cirillo
- Department of Radiology, Mauriziano Umberto I Hospital, Turin, Italy
| | - Andrea Ruzzenente
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynaecology and Pediatrics, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Ilaria Pecorella
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Nadia Russolillo
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, Turin, Italy
| | - Martina Borzi
- Department of Radiology, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Giulio Vara
- Department of Radiology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
| | - Caterina Mele
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giorgio Ercolani
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy
- Department of General Surgery, Morgagni-Pierantoni Hospital, Forlì, Italy
| | - Felice Giuliante
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Matteo Cescon
- General Surgery and Transplant Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Alfredo Guglielmi
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynaecology and Pediatrics, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Alessandro Ferrero
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, Turin, Italy
| | - Martina Sollini
- Department of Nuclear Medicine, IRCCS San Raffaele, Milan, Italy
- Faculty of Medicine, Università Vita-Salute San Raffaele, Milan, Italy
| | - Arturo Chiti
- Department of Nuclear Medicine, IRCCS San Raffaele, Milan, Italy
- Faculty of Medicine, Università Vita-Salute San Raffaele, Milan, Italy
| | - Guido Torzilli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Francesca Ieva
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
- CHDS - Center for Health Data Science, Human Technopole, Milan, Italy
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
- Hepatobiliary Unit, Department of Minimally Invasive General and Oncologic Surgery, Humanitas Gavazzeni University Hospital, Bergamo, Italy.
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21
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Horio Y, Ikeda J, Matsumoto K, Okada S, Nagano K, Kusunoki K, Kuwahara R, Kimura K, Kataoka K, Beppu N, Uchino M, Ikeda M, Okadome T, Yamakado K, Ikeuchi H. Machine learning‑based radiomics models accurately predict Crohn's disease‑related anorectal cancer. Oncol Lett 2024; 28:421. [PMID: 39035049 PMCID: PMC11258598 DOI: 10.3892/ol.2024.14553] [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: 02/21/2024] [Accepted: 06/06/2024] [Indexed: 07/23/2024] Open
Abstract
The radiological diagnosis of Crohn's disease (CD)-related anorectal cancer is difficult; it is often found in advanced stages and has a poor prognosis because of the difficulty of curative surgery. However, there are no studies on predicting the diagnosis of CD-related cancer. The present study aimed to develop a predictive model to diagnose CD cancerous lesions more accurately in a way that can be interpreted by clinicians. Patients with CD who developed anorectal CD lesions at Hyogo Medical University (Nishinomiya, Japan) between March 2009 and June 2022 were included in the present study. T2-weighted and T1-weighted magnetic resonance (MR) images were utilized for our analysis. Images of anorectal lesions were segmented using open-source 3D Slicer software, and radiomic features were extracted using PyRadiomics. Six machine learning models were investigated and compared: i) Support vector machine; ii) naive Bayes; iii) random forest; iv) light gradient boosting machine; v) extremely randomized trees; vi) and regularized greedy forest (RGF). SHapley Additive exPlanations (SHAP) values were calculated to assess the extent to which each radiomic feature contributed to the model's predictions compared to baseline, represented as the average of the model's predictions for all test data. The T2-weighted images of 28 patients with anorectal cancer and 40 non-cancer patients were analyzed and the contrast-enhanced T1-weighted images of 22 cancer and 40 non-cancer patients. The model with the highest area under the curve (AUC) was the RGF-based model constructed using T2-weighted image features, achieving an AUC of 0.944 (accuracy, 0.862; recall, 0.830). The SHAP-based model explanation suggested a strong association between the diagnosis of CD-related anorectal cancer and features such as complex lesion texture; greater pixel separation within the same coronal cross-section; larger, randomly distributed clumps of pixels with the same signal intensity; and a more spherical lesion shape on T2-weighted images. The MRI radiomics-based RGF model demonstrated outstanding performance in predicting CD-related anorectal cancer. These results may affect the diagnosis and surveillance strategies of CD-related colorectal cancer.
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Affiliation(s)
- Yuki Horio
- Department of Gastroenterological Surgery, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan
| | - Jota Ikeda
- Department of Radiology, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan
| | - Kentaro Matsumoto
- Department of Science and Engineering, Kwansei Gakuin University, Sanda, Hyogo 669-1330, Japan
| | - Shinichiro Okada
- Department of Science and Engineering, Kwansei Gakuin University, Sanda, Hyogo 669-1330, Japan
| | - Kentaro Nagano
- Department of Gastroenterological Surgery, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan
| | - Kurando Kusunoki
- Department of Gastroenterological Surgery, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan
| | - Ryuichi Kuwahara
- Department of Gastroenterological Surgery, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan
| | - Kei Kimura
- Department of Gastroenterological Surgery, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan
| | - Kozo Kataoka
- Department of Gastroenterological Surgery, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan
| | - Naohito Beppu
- Department of Gastroenterological Surgery, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan
| | - Motoi Uchino
- Department of Gastroenterological Surgery, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan
| | - Masataka Ikeda
- Department of Gastroenterological Surgery, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan
| | - Takeshi Okadome
- Department of Science and Engineering, Kwansei Gakuin University, Sanda, Hyogo 669-1330, Japan
| | - Koichiro Yamakado
- Department of Radiology, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan
| | - Hiroki Ikeuchi
- Department of Gastroenterological Surgery, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan
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22
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Geady C, Abbas-Aghababazadeh F, Kohan A, Schuetze S, Shultz D, Haibe-Kains B. Radiomic-based prediction of lesion-specific systemic treatment response in metastatic disease. Comput Med Imaging Graph 2024; 116:102413. [PMID: 38945043 DOI: 10.1016/j.compmedimag.2024.102413] [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: 08/11/2023] [Revised: 04/08/2024] [Accepted: 06/15/2024] [Indexed: 07/02/2024]
Abstract
Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.
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Affiliation(s)
- Caryn Geady
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Medical Biophysics, University of Toronto, Toronto, Canada
| | | | - Andres Kohan
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Scott Schuetze
- Department of Medicine, University of Michigan, Ann Arbor, MI, USA
| | - David Shultz
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Medical Biophysics, University of Toronto, Toronto, Canada; Department of Medicine, University of Michigan, Ann Arbor, MI, USA; Vector Institute for Artificial Intelligence, Toronto, Canada; Ontario Institute for Cancer Research, Toronto, Canada; Department of Computer Science, University of Toronto, Toronto, Canada; Department of Biostatistics, Dalla Lana School of Public Health, Toronto, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Medical Biophysics, University of Toronto, Toronto, Canada; Vector Institute for Artificial Intelligence, Toronto, Canada; Ontario Institute for Cancer Research, Toronto, Canada; Department of Computer Science, University of Toronto, Toronto, Canada; Department of Biostatistics, Dalla Lana School of Public Health, Toronto, Canada.
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23
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Xu Z, Wang YH, Cheng ZY, Feng YZ, Li XC, Zhou Q, Cai XR. Combined radiomics nomogram of different machine learning models for preoperative distinguishing intraspinal schwannomas and meningiomas: a multicenter and comparative study. Clin Radiol 2024; 79:e1108-e1116. [PMID: 38849236 DOI: 10.1016/j.crad.2024.05.009] [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: 03/09/2024] [Revised: 05/12/2024] [Accepted: 05/14/2024] [Indexed: 06/09/2024]
Abstract
AIMS The objective of our study was to establish and verify a novel combined model based on multiparameter magnetic resonance imaging (MRI) radiomics and clinical features to distinguish intraspinal schwannomas from meningiomas. MATERIALS AND METHODS This research analyzed the preoperative magnetic resonance (MR) images and clinical characteristics of 209 patients with intraspinal tumors who received tumor resection at three institutions. 159 individuals from institutions 1 and 2 were randomly assigned into a training group (n=111) and a test group (n=48) in a 7-3 ratio. A nomogram was constructed using the training cohort and was internally and externally verified in the test cohort and an independent validation cohort (n=50). Model performance was assessed utilizing the area under the curve (AUC) of receiver operating characteristics (ROC), decision curve analysis (DCA), and calibration curves. RESULTS The nomogram exhibited superior predictive efficacy in distinguishing between spinal schwannomas and meningiomas when compared to both the radiomics model and the clinical model. The nomogram yielded AUCs of 0.994, 0.962, and 0.949 in the training, test, and external validation cohorts, respectively, indicating its exceptional differentiating ability. The DCAs demonstrated that the nomogram yielded the best net benefit. The calibration curves indicated that the nomogram got good agreement between the predicted and the actual observation. CONCLUSION This research suggests that the nomogram incorporating clinical and radiomic features may be an effective auxiliary tool for distinguishing between intraspinal schwannomas and meningiomas, and has important clinical significance for clinical decision-making and prognosis prediction.
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Affiliation(s)
- Z Xu
- Medical Imaging Center, First Affiliated Hospital of Jinan University, No.613 West Huangpu Avenue, Tianhe District, Guangzhou 510630, Guangdong, China
| | - Y-H Wang
- Department of Radiology, Third Affiliated Hospital of Southern Medical University, 183 Zhongshan Avenue West, Tianhe District, Guangzhou 510630, China; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, No. 151 Yanjiangxi Road, Guangzhou, Guangdong 510120, China
| | - Z-Y Cheng
- Medical Imaging Center, First Affiliated Hospital of Jinan University, No.613 West Huangpu Avenue, Tianhe District, Guangzhou 510630, Guangdong, China
| | - Y-Z Feng
- Medical Imaging Center, First Affiliated Hospital of Jinan University, No.613 West Huangpu Avenue, Tianhe District, Guangzhou 510630, Guangdong, China
| | - X-C Li
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, No. 151 Yanjiangxi Road, Guangzhou, Guangdong 510120, China.
| | - Q Zhou
- Department of Radiology, Third Affiliated Hospital of Southern Medical University, 183 Zhongshan Avenue West, Tianhe District, Guangzhou 510630, China.
| | - X-R Cai
- Medical Imaging Center, First Affiliated Hospital of Jinan University, No.613 West Huangpu Avenue, Tianhe District, Guangzhou 510630, Guangdong, China.
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Panagiotidis E, Andreou S, Paschali A, Angeioplasti K, Vlontzou E, Kalathas T, Pipintakou A, Fothiadaki A, Makridou A, Chatzimarkou M, Papanastasiou E, Datseris I, Chatzipavlidou V. Towards improved diagnosis: radiomics and quantitative biomarkers in 18 F-PSMA-1007 and 18 F-fluorocholine PET/CT for prostate cancer recurrence. Nucl Med Commun 2024; 45:796-803. [PMID: 38832429 DOI: 10.1097/mnm.0000000000001867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
OBJECTIVE This study compared the radiomic features and quantitative biomarkers of 18 F-PSMA-1007 [prostate-specific membrane antigen (PSMA)] and 18 F-fluorocholine (FCH) PET/computed tomography (CT) in prostate cancer patients with biochemical recurrence (BCR) enrolled in the phase 3, prospective, multicenter BIO-CT-001 trial. METHODS A total of 106 patients with BCR, who had undergone primary definitive treatment for prostate cancer, were recruited to this prospective study. All patients underwent one PSMA and one FCH PET/CT examination in randomized order within 10 days. They were followed up for a minimum of 6 months. Pathology, prostate-specific antigen (PSA), PSA doubling time, PSA velocity, and previous or ongoing treatment were analyzed. Using LifeX software, standardized uptake value (SUV) maximum, SUV mean , PSMA and choline total volume (PSMA-TV/FCH-TV), and total lesion PSMA and choline (TL-PSMA/TL-FCH) of all identified metastatic lesions in both tracers were calculated. RESULTS Of the 286 lesions identified, the majority 140 (49%) were lymph node metastases, 118 (41.2%) were bone metastases and 28 lesions (9.8%) were locoregional recurrences of prostate cancer. The median SUV max value was significantly higher for 18 F-PSMA compared with FCH for all 286 lesions (8.26 vs. 4.99, respectively, P < 0.001). There were statistically significant differences in median SUV mean , TL-PSMA/FCH, and PSMA/FCH-TV between the two radiotracers (4.29 vs. 2.92, 1.97 vs. 1.53, and 7.31 vs. 4.37, respectively, P < 0.001). The correlation between SUV mean /SUV max and PSA level was moderate, both for 18 F-PSMA ( r = 0.44, P < 0.001; r = 0.44, P < 0.001) and FCH ( r = 0.35, P < 0.001; r = 0.41, P < 0.001). TL-PSMA/FCH demonstrated statistically significant positive correlations with both PSA level and PSA velocity for both 18 F-PSMA ( r = 0.56, P < 0.001; r = 0.57, P < 0.001) and FCH ( r = 0.49, P < 0.001; r = 0.51, P < 0.001). While patients who received hormone therapy showed higher median SUV max values for both radiotracers compared with those who did not, the difference was statistically significant only for 18 F-PSMA ( P < 0.05). CONCLUSION Our analysis using both radiomic features and quantitative biomarkers demonstrated the improved performance of 18 F-PSMA-1007 compared with FCH in identifying metastatic lesions in prostate cancer patients with BCR.
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Affiliation(s)
| | - Sotiria Andreou
- Department of Nuclear Medicine - PET/CT, 'Theageneio' Cancer Center, Thessaloniki,
| | - Anna Paschali
- Department of Nuclear Medicine - PET/CT, 'Theageneio' Cancer Center, Thessaloniki,
| | - Kyra Angeioplasti
- Department of Nuclear Medicine - PET/CT, 'Theageneio' Cancer Center, Thessaloniki,
| | - Evaggelia Vlontzou
- Department of Nuclear Medicine - PET/CT, 'Evaggelismos' General Hospital, Athens and
| | - Theodore Kalathas
- Department of Nuclear Medicine - PET/CT, 'Theageneio' Cancer Center, Thessaloniki,
| | - Angeliki Pipintakou
- Department of Nuclear Medicine - PET/CT, 'Theageneio' Cancer Center, Thessaloniki,
| | - Athina Fothiadaki
- Department of Nuclear Medicine - PET/CT, 'Evaggelismos' General Hospital, Athens and
| | - Anna Makridou
- Department of Nuclear Medicine - PET/CT, 'Theageneio' Cancer Center, Thessaloniki,
| | - Michael Chatzimarkou
- Department of Nuclear Medicine - PET/CT, 'Theageneio' Cancer Center, Thessaloniki,
| | - Emmanouil Papanastasiou
- Laboratory of Medical Physics and Digitial Innovation, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ioannis Datseris
- Department of Nuclear Medicine - PET/CT, 'Evaggelismos' General Hospital, Athens and
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25
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Bektaş M, Chia CM, Burchell GL, Daams F, Bonjer HJ, van der Peet DL. Artificial intelligence-aided ultrasound imaging in hepatopancreatobiliary surgery: where are we now? Surg Endosc 2024; 38:4869-4879. [PMID: 39160306 PMCID: PMC11362182 DOI: 10.1007/s00464-024-11130-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 07/28/2024] [Indexed: 08/21/2024]
Abstract
BACKGROUND Artificial intelligence (AI) models have been applied in various medical imaging modalities and surgical disciplines, however the current status and progress of ultrasound-based AI models within hepatopancreatobiliary surgery have not been evaluated in literature. Therefore, this review aimed to provide an overview of ultrasound-based AI models used for hepatopancreatobiliary surgery, evaluating current advancements, validation, and predictive accuracies. METHOD Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using AI models on ultrasound for patients undergoing hepatopancreatobiliary surgery. To be eligible for inclusion, studies needed to apply AI methods on ultrasound imaging for patients undergoing hepatopancreatobiliary surgery. The Probast risk of bias tool was used to evaluate the methodological quality of AI methods. RESULTS AI models have been primarily used within hepatopancreatobiliary surgery, to predict tumor recurrence, differentiate between tumoral tissues, and identify lesions during ultrasound imaging. Most studies have combined radiomics with convolutional neural networks, with AUCs up to 0.98. CONCLUSION Ultrasound-based AI models have demonstrated promising accuracies in predicting early tumoral recurrence and even differentiating between tumoral tissue types during and after hepatopancreatobiliary surgery. However, prospective studies are required to evaluate if these results will remain consistent and externally valid.
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Affiliation(s)
- Mustafa Bektaş
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Catherine M Chia
- Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, The Netherlands
| | - George L Burchell
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Medical Library, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Freek Daams
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands
| | - H Jaap Bonjer
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Donald L van der Peet
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands
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26
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Bang M, Park K, Choi SH, Ahn SS, Kim J, Lee SK, Park YW, Lee SH. Identification of schizophrenia by applying interpretable radiomics modeling with structural magnetic resonance imaging of the cerebellum. Psychiatry Clin Neurosci 2024; 78:527-535. [PMID: 38953397 DOI: 10.1111/pcn.13707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 05/26/2024] [Accepted: 06/12/2024] [Indexed: 07/04/2024]
Abstract
AIMS The cerebellum is involved in higher-order mental processing as well as sensorimotor functions. Although structural abnormalities in the cerebellum have been demonstrated in schizophrenia, neuroimaging techniques are not yet applicable to identify them given the lack of biomarkers. We aimed to develop a robust diagnostic model for schizophrenia using radiomic features from T1-weighted magnetic resonance imaging (T1-MRI) of the cerebellum. METHODS A total of 336 participants (174 schizophrenia; 162 healthy controls [HCs]) were allocated to training (122 schizophrenia; 115 HCs) and test (52 schizophrenia; 47 HCs) cohorts. We obtained 2568 radiomic features from T1-MRI of the cerebellar subregions. After feature selection, a light gradient boosting machine classifier was trained. The discrimination and calibration of the model were evaluated. SHapley Additive exPlanations (SHAP) was applied to determine model interpretability. RESULTS We identified 17 radiomic features to differentiate participants with schizophrenia from HCs. In the test cohort, the radiomics model had an area under the curve, accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.82-0.95), 78.8%, 88.5%, and 75.4%, respectively. The model explanation by SHAP suggested that the second-order size zone non-uniformity feature from the right lobule IX and first-order energy feature from the right lobules V and VI were highly associated with the risk of schizophrenia. CONCLUSION The radiomics model focused on the cerebellum demonstrates robustness in diagnosing schizophrenia. Our results suggest that microcircuit disruption in the posterior cerebellum is a disease-defining feature of schizophrenia, and radiomics modeling has potential for supporting biomarker-based decision-making in clinical practice.
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Affiliation(s)
- Minji Bang
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Kisung Park
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Seoung-Ho Choi
- National Program Excellence in Software at Kwangwoon University, Seoul, Republic of Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jinna Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sang-Hyuk Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
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Villagran M, Driban JB, Lu B, MacKay JW, McAlindon TE, Harkey MS. Radiomic features of the medial meniscus predicts incident destabilizing meniscal tears: Data from the osteoarthritis initiative. J Orthop Res 2024; 42:2080-2087. [PMID: 38747030 PMCID: PMC11336561 DOI: 10.1002/jor.25851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 02/02/2024] [Accepted: 03/30/2024] [Indexed: 08/02/2024]
Abstract
The objective of this study was to determine the optimal meniscal radiomic features to classify people who will develop an incident destabilizing medial meniscal tear. We used magnetic resonance (MR) images from an existing case-control study that includes images from the first 4 years of the Osteoarthritis Initiative (OAI). For this exploratory analysis (n = 215), we limited our study sample to people with (1) intact menisci at the OAI baseline visit, (2) 4-year meniscal status data, and (3) complete meniscal data from each region of interest. Incident destabilizing meniscal tear was defined as progressing from an intact meniscus to a destabilizing tear by the 48-month visit using intermediate-weighted fat-suppressed MR images. One reader manually segmented each participant's anterior and posterior horn of the medial menisci at the OAI baseline visit. Next, 61 different radiomic features were extracted from each medial meniscus horn. We performed a classification and regression tree (CART) analysis to determine the classification rules and important variables that predict incident destabilizing meniscal tear. The CART correctly classified 24 of the 34 cases and 172 out of 181 controls with a sensitivity of 70.6% and a specificity of 95.0%. The CART identified large zone high gray level emphasis (i.e., more coarse texture) from the posterior horn as the most important variable to classify who would develop an incident destabilizing medial meniscal tear. The use of radiomic features provides sensitive and quantitative measures of meniscal alterations, allowing us to intervene and prevent destabilizing meniscal tears.
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Affiliation(s)
- Michelle Villagran
- Department of Chemistry, Wellesley College, Wellesley, Massachusetts, USA
| | - Jeffrey B Driban
- Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, Massachusetts, USA
| | - Bing Lu
- Department of Public Health Sciences, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - James W MacKay
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Timothy E McAlindon
- Division of Rheumatology, Allergy and Immunology, Tufts Medical Center, Boston, Massachusetts, USA
| | - Matthew S Harkey
- Department of Kinesiology, Michigan State University, East Lansing, Michigan, USA
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Doniselli FM, Pascuzzo R, Mazzi F, Padelli F, Moscatelli M, Akinci D'Antonoli T, Cuocolo R, Aquino D, Cuccarini V, Sconfienza LM. Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis. Eur Radiol 2024; 34:5802-5815. [PMID: 38308012 PMCID: PMC11364578 DOI: 10.1007/s00330-024-10594-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 12/04/2023] [Accepted: 12/31/2023] [Indexed: 02/04/2024]
Abstract
OBJECTIVES To evaluate the methodological quality and diagnostic accuracy of MRI-based radiomic studies predicting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in gliomas. METHODS PubMed Medline, EMBASE, and Web of Science were searched to identify MRI-based radiomic studies on MGMT methylation in gliomas published until December 31, 2022. Three raters evaluated the study methodological quality with Radiomics Quality Score (RQS, 16 components) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD, 22 items) scales. Risk of bias and applicability concerns were assessed with QUADAS-2 tool. A meta-analysis was performed to estimate the pooled area under the curve (AUC) and to assess inter-study heterogeneity. RESULTS We included 26 studies, published from 2016. The median RQS total score was 8 out of 36 (22%, range 8-44%). Thirteen studies performed external validation. All studies reported AUC or accuracy, but only 4 (15%) performed calibration and decision curve analysis. No studies performed phantom analysis, cost-effectiveness analysis, and prospective validation. The overall TRIPOD adherence score was between 50% and 70% in 16 studies and below 50% in 10 studies. The pooled AUC was 0.78 (95% CI, 0.73-0.83, I2 = 94.1%) with a high inter-study heterogeneity. Studies with external validation and including only WHO-grade IV gliomas had significantly lower AUC values (0.65; 95% CI, 0.57-0.73, p < 0.01). CONCLUSIONS Study RQS and adherence to TRIPOD guidelines was generally low. Radiomic prediction of MGMT methylation status showed great heterogeneity of results and lower performances in grade IV gliomas, which hinders its current implementation in clinical practice. CLINICAL RELEVANCE STATEMENT MGMT promoter methylation status appears to be variably correlated with MRI radiomic features; radiomic models are not sufficiently robust to be integrated into clinical practice to accurately predict MGMT promoter methylation status in patients with glioma before surgery. KEY POINTS • Adherence to the indications of TRIPOD guidelines was generally low, as was RQS total score. • MGMT promoter methylation status prediction with MRI radiomic features provided heterogeneous diagnostic accuracy results across studies. • Studies that included grade IV glioma only and performed external validation had significantly lower diagnostic accuracy than others.
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Affiliation(s)
- Fabio M Doniselli
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133, Milan, Italy
| | - Riccardo Pascuzzo
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy.
| | - Federica Mazzi
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Francesco Padelli
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Marco Moscatelli
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Rheinstrasse 26, 4410, Liestal, Switzerland
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Via Salvador Allende 43, Baronissi, 84081, Salerno, Italy
| | - Domenico Aquino
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Valeria Cuccarini
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Luca Maria Sconfienza
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133, Milan, Italy
- IRCCS Ospedale Galeazzi-Sant'Ambrogio, Via Cristina Belgioioso 173, 20157, Milan, Italy
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Hou L, Chen K, Zhou C, Tang X, Yu C, Jia H, Xu Q, Zhou S, Yang H. CT-based different regions of interest radiomics analysis for acute radiation pneumonitis in patients with locally advanced NSCLC after chemoradiotherapy. Clin Transl Radiat Oncol 2024; 48:100828. [PMID: 39189001 PMCID: PMC11345682 DOI: 10.1016/j.ctro.2024.100828] [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: 01/05/2024] [Revised: 06/26/2024] [Accepted: 07/27/2024] [Indexed: 08/28/2024] Open
Abstract
Purpose To establish a radiomics model using radiomics features from different region of interests (ROI) based on dosimetry-related regions in enhanced computed tomography (CT) simulated images to predict radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC). Methods Our retrospective study was conducted based on a cohort of 236 NSCLC patients (59 of them with RP≥2) who were treated in 2 institutions and divided into the primary cohort (n = 182,46 of them with RP≥2) and external validation cohort (n = 54,13 of them with RP≥2). Radiomic features extracted from three ROIs were defined as the whole lung (WL), the dose volume histogram (DVH) of the lung V20 (V20_Lung) and the DVH of the V30 of lung minus the planning target volume (PTV) (V30 Lung-PTV). A total of 107 radiomics features were extracted from each ROIs. The U test, correlation coefficient and least absolute shrinkage and selection operator (LASSO) were performed for features selection. Six models based on different classification algorithms were developed to select the best radiomics model (R model).In addition, we built a dosimetry model then combined it with the best R model to create a mixed model (R+D model) The receiver operating characteristic (ROC) curve was delineated to assess the predictive efficacy of the models. Decision curve analysis could benefit from the model proposals through the assessment of clinical utility. Results Among the three ROIs, the best R model constructed from the LightGBM algorithm demonstrated the strongest discriminative ability in the ROI of V30 Lung-PTV. The corresponding area under the curve (AUC) value was 0.930 (95 % confidence interval (CI): 0.829-0.941). The D model, R model and R+D model achieved AUC values of 0.798 (95 %CI: 0.732-0.865), 0.930 (95 %CI: 0.829-0.941) and 0.940 (95 %CI: 0.906-0.974) in primary cohort, and in external validation cohort, the AUC values were 0.793 (95 %CI:0.637-0.949), 0.887 (95 %CI:0.810-0.993), 0.951 (95CI%:0.891-1.000). Decision curve demonstrate that R+D model could benefit for patients through the assessment of clinical utility. Conclusion The radiomics model was able to predict the acute RP more effectively in comparison with the traditional dosimetry model. Especially the radiomics model based on the V30 Lung-PTV region was able to achieve a higher accuracy when compared to the other regions.
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Affiliation(s)
- Liqiao Hou
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy , Department of Radiation Oncology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, NO.150 Ximen Street, Linhai, Taizhou City, 317000, Zhejiang Province, China
| | - Kuifei Chen
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy , Department of Radiation Oncology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, NO.150 Ximen Street, Linhai, Taizhou City, 317000, Zhejiang Province, China
| | - Chao Zhou
- Department of Radiation Oncology, Enze Hospital Affiliated Hospital of Hangzhou Medical College, Zhejiang Province 317000, China
| | - Xingni Tang
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy , Department of Radiation Oncology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, NO.150 Ximen Street, Linhai, Taizhou City, 317000, Zhejiang Province, China
| | - Changhui Yu
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy , Department of Radiation Oncology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, NO.150 Ximen Street, Linhai, Taizhou City, 317000, Zhejiang Province, China
| | - Haijian Jia
- Department of Radiation Oncology, Enze Hospital Affiliated Hospital of Hangzhou Medical College, Zhejiang Province 317000, China
| | - Qianyi Xu
- Department of Radiation Oncology, Thomas Jefferson Health System, 8081 Innovation Park Dr., Fairfax, VA, 22003, USA
| | - Suna Zhou
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy , Department of Radiation Oncology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, NO.150 Ximen Street, Linhai, Taizhou City, 317000, Zhejiang Province, China
| | - Haihua Yang
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy , Department of Radiation Oncology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, NO.150 Ximen Street, Linhai, Taizhou City, 317000, Zhejiang Province, China
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Chen Y, Lin H, Sun J, Pu R, Zhou Y, Sun B. Texture feature differentiation of glioblastoma and solitary brain metastases based on tumor and tumor-brain interface. Acad Radiol 2024:S1076-6332(24)00588-9. [PMID: 39217081 DOI: 10.1016/j.acra.2024.08.025] [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: 07/16/2024] [Revised: 08/04/2024] [Accepted: 08/14/2024] [Indexed: 09/04/2024]
Abstract
RATIONALE AND OBJECTIVES Texture features, derived from both the entire tumor area and the region of the tumor-to-brain interface, are crucial indicators for distinguishing tumor types and their degrees of malignancy. However, the discriminative value of texture features from both regions for identifying glioblastomas and metastatic tumors has not been thoroughly explored. The aim of this study is to develop and validate a diagnostic model that combines texture features from the entire tumor area and a 10 mm tumor-to-brain interface region, in an attempt to identify more stable and effective texture features. METHOD We retrospectively collected enhanced T1-weighted imaging data from 97 patients with glioblastoma(GBM) and single brain metastasis(SBM) between 2010 and 2024. Machine learning is used to establish multiple diagnostic models for discriminating GBM and SBM based on texture features of the entire tumor and 10 mm tumor-to-brain interface regions. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). RESULTS The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Relief and autoencoder (AE), the AUC was highest using the "one-standard error" rule. '10mm_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature. The best models in the training set, test set, and validation set were not the same. In the test set, the Relief19AE model had the highest AUC of 0.869 and an accuracy of 0.857. CONCLUSION The texture feature model that combines the overall tumor and the tumor-brain interface is beneficial for distinguishing glioblastoma from solitary metastatic tumors, and the texture features of the tumor interface exhibit higher heterogeneity.
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Affiliation(s)
- Yini Chen
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China (Y.C., H.L., J.S., R.P., Y.Z., B.S.)
| | - Hongsen Lin
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China (Y.C., H.L., J.S., R.P., Y.Z., B.S.)
| | - Jiayi Sun
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China (Y.C., H.L., J.S., R.P., Y.Z., B.S.); College of Medical Imaging, Dalian Medical University, Dalian, Liaoning, China (J.S.)
| | - Renwang Pu
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China (Y.C., H.L., J.S., R.P., Y.Z., B.S.)
| | - Yujing Zhou
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China (Y.C., H.L., J.S., R.P., Y.Z., B.S.)
| | - Bo Sun
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China (Y.C., H.L., J.S., R.P., Y.Z., B.S.).
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Qi L, Zhu Y, Li J, Zhou M, Liu B, Chen J, Shen J. CT radiomics-based biomarkers can predict response to immunotherapy in hepatocellular carcinoma. Sci Rep 2024; 14:20027. [PMID: 39198563 PMCID: PMC11358293 DOI: 10.1038/s41598-024-70208-w] [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: 03/02/2024] [Accepted: 08/13/2024] [Indexed: 09/01/2024] Open
Abstract
Hepatocellular Carcinoma (HCC) remains a leading cause of cancer deaths. Despite the rise of immunotherapies, many HCC patients don't benefit. There's a clear need for biomarkers to guide treatment decisions. This research aims to identify such biomarkers by combining radiological data and machine learning. We analyzed clinical and CT imaging data of 54 HCC patients undergoing immunotherapy. Radiologic features were examined to develop a model predicting short-term immunotherapy effects. We utilized 9 machine learning and 2 ensemble learning techniques using RapidMiner for model construction. We conducted the validation of the above feature combination using 29 HCC patients who received immunotherapy from another hospital, and tested and validated it using XGBoost combined with a genetic algorithm. In 54 HCC patients, radiomics features varied significantly between those with partial response (PR) and stable disease (SD). Key features in Gray Level Run Length Matrix (GLRLM) and in adjacent tissues' Intensity Direct, Neighborhood Gray Tone Difference Matrix (NGTDM), and Shape correlated with short-term immunotherapy efficacy. Selected feature combinations of 15, 19, and 8/15 features yielded better outcomes. Deep learning, random forest, and naive bayes outperformed other methods, with Bagging being more reliable than Adaboost. In the validation set of 29 HCC patients, the mentioned feature combination also demonstrated favorable performance. Furthermore, we achieved improved results when employing XGBoost in conjunction with a genetic algorithm for testing and validation. The machine learning model built with CT image features derived from GLCM, GLRLM, IntensityDirect, NGTDM, and Shape can accurately forecast the short-term efficacy of immunotherapy for HCC.
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Affiliation(s)
- Liang Qi
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321 Zhongshan Road, Nanjing, 210008, China
| | - Yahui Zhu
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321 Zhongshan Road, Nanjing, 210008, China
| | - Jinxin Li
- Department of Li Ka Shing Faculty of Medicine, The University of Hong Kong, HKSAR, China
| | - Mingzhen Zhou
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Baorui Liu
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321 Zhongshan Road, Nanjing, 210008, China.
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China.
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321 Zhongshan Road, Nanjing, 210008, China.
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China.
| | - Jie Shen
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321 Zhongshan Road, Nanjing, 210008, China.
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China.
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Wang X, Liu S, Yan Z, Yin F, Feng J, Liu H, Liu Y, Li Y. Radiomics Nomograms Based on Multi-sequence MRI for Identifying Cognitive Impairment and Predicting Cognitive Progression in Relapsing-Remitting Multiple Sclerosis. Acad Radiol 2024:S1076-6332(24)00591-9. [PMID: 39198138 DOI: 10.1016/j.acra.2024.08.026] [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: 07/08/2024] [Revised: 08/08/2024] [Accepted: 08/14/2024] [Indexed: 09/01/2024]
Abstract
RATIONALE AND OBJECTIVES To build radiomics nomograms based on multi-sequence MRI to facilitate the identification of cognitive impairment (CI) and prediction of cognitive progression (CP) in patients with relapsing-remitting multiple sclerosis (RRMS). MATERIALS AND METHODS We retrospectively included two RRMS cohorts with multi-sequence MRI and Symbol Digit Modalities Test (SDMT) data: dataset1 (n = 149, for training and validation) and dataset2 (n = 29, for external validation). 80 patients of dataset1 had a 2-year follow-up SDMT. CI and CP were evaluated using SDMT scores at baseline and follow-up. The included DIR sequence aided in identifying cortical lesions. Lesion radiomics and structural features were extracted and selected from multi-sequence MRI, followed by the computation of radiomics and structural scores. The nomogram was developed through multivariate logistic regression, integrating clinical data, radiomics, and structural scores to identify CI in patients. Moreover, a similar method was employed to further construct a nomogram predicting CP in patients. RESULTS The nomogram demonstrated superior performance in identifying patients with CI, with area under the curve (AUC) values of 0.937 (95% Conf. Interval: 0.898-0.975) and 0.876 (0.810-0.943) in internal and external validation sets, compared to models solely based on clinical data, lesion radiomics, and structural features. Furthermore, another nomogram constructed in predicting CP also exhibited outstanding performance, with an AUC value of 0.969 (0.875-1.000) in the validation set. CONCLUSION These nomograms, integrating clinical data, multi-sequence lesions radiomics, and structural features, enable more effective identification of CI and early prediction of CP in RRMS patients, providing important support for clinical decision-making.
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Affiliation(s)
- Xiaohua Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
| | - Shangqing Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
| | - Zichun Yan
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Feiyue Yin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jinzhou Feng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Hao Liu
- Yizhun Medical AI, Beijing 100000, China
| | - Yanbing Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
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Bodalal Z, Hong EK, Trebeschi S, Kurilova I, Landolfi F, Bogveradze N, Castagnoli F, Randon G, Snaebjornsson P, Pietrantonio F, Lee JM, Beets G, Beets-Tan R. Non-invasive CT radiomic biomarkers predict microsatellite stability status in colorectal cancer: a multicenter validation study. Eur Radiol Exp 2024; 8:98. [PMID: 39186200 PMCID: PMC11347521 DOI: 10.1186/s41747-024-00484-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 05/30/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Microsatellite instability (MSI) status is a strong predictor of response to immunotherapy of colorectal cancer. Radiogenomic approaches promise the ability to gain insight into the underlying tumor biology using non-invasive routine clinical images. This study investigates the association between tumor morphology and the status of MSI versus microsatellite stability (MSS), validating a novel radiomic signature on an external multicenter cohort. METHODS Preoperative computed tomography scans with matched MSI status were retrospectively collected for 243 colorectal cancer patients from three hospitals: Seoul National University Hospital (SNUH); Netherlands Cancer Institute (NKI); and Fondazione IRCCS Istituto Nazionale dei Tumori, Milan Italy (INT). Radiologists delineated primary tumors in each scan, from which radiomic features were extracted. Machine learning models trained on SNUH data to identify MSI tumors underwent external validation using NKI and INT images. Performances were compared in terms of area under the receiving operating curve (AUROC). RESULTS We identified a radiomic signature comprising seven radiomic features that were predictive of tumors with MSS or MSI (AUROC 0.69, 95% confidence interval [CI] 0.54-0.84, p = 0.018). Integrating radiomic and clinical data into an algorithm improved predictive performance to an AUROC of 0.78 (95% CI 0.60-0.91, p = 0.002) and enhanced the reliability of the predictions. CONCLUSION Differences in the radiomic morphological phenotype between tumors MSS or MSI could be detected using radiogenomic approaches. Future research involving large-scale multicenter prospective studies that combine various diagnostic data is necessary to refine and validate more robust, potentially tumor-agnostic MSI radiogenomic models. RELEVANCE STATEMENT Noninvasive radiomic signatures derived from computed tomography scans can predict MSI in colorectal cancer, potentially augmenting traditional biopsy-based methods and enhancing personalized treatment strategies. KEY POINTS Noninvasive CT-based radiomics predicted MSI in colorectal cancer, enhancing stratification. A seven-feature radiomic signature differentiated tumors with MSI from those with MSS in multicenter cohorts. Integrating radiomic and clinical data improved the algorithm's predictive performance.
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Affiliation(s)
- Zuhir Bodalal
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Eun Kyoung Hong
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Seoul National University Hospital, Seoul, South Korea
| | - Stefano Trebeschi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Ieva Kurilova
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Federica Landolfi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Radiology Unit, Sant'Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of Radiology, American Hospital Tbilisi, Tbilisi, Georgia
| | - Francesca Castagnoli
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Radiology, Royal Marsden Hospital, London, UK
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, UK
| | - Giovanni Randon
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Petur Snaebjornsson
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Filippo Pietrantonio
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
- Oncology and Hemato-oncology Department, University of Milan, Milan, Italy
| | - Jeong Min Lee
- Seoul National University Hospital, Seoul, South Korea
| | - Geerard Beets
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark.
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Cavinato S, Scaggion A, Paiusco M. Technical note: A software tool to extract complexity metrics from radiotherapy treatment plans. Med Phys 2024. [PMID: 39186793 DOI: 10.1002/mp.17365] [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: 05/10/2024] [Revised: 08/08/2024] [Accepted: 08/09/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Complexity metrics are mathematical quantities designed to quantify aspects of radiotherapy treatment plans that may affect both their deliverability and dosimetric accuracy. Despite numerous studies investigating their utility, there remains a notable absence of shared tools for their extraction. PURPOSE This study introduces UCoMX (Universal Complexity Metrics Extractor), a software package designed for the extraction of complexity metrics from the DICOM-RT plan files of radiotherapy treatments. METHODS UCoMX is developed around two extraction engines: VCoMX (VMAT Complexity Metrics Extractor) for VMAT/IMRT plans, and TCoMX (Tomotherapy Complexity Metrics Extractor) tailored for Helical Tomotherapy plans. The software, built using Matlab, is freely available in both Matlab-based and stand-alone versions. More than 90 complexity metrics, drawn from relevant literature, are implemented in the package: 43 for VMAT/IMRT and 51 for Helical Tomotherapy. RESULTS The package is designed to read DICOM-RT plan files generated by most commercially available Treatment Planning Systems (TPSs), across various treatment units. A reference dataset containing VMAT, IMRT, and Helical Tomotherapy plans is provided to serve as a reference for comparing UCoMX with other in-house systems available at other centers. CONCLUSION UCoMX offers a straightforward solution for extracting complexity metrics from radiotherapy plans. Its versatility is enhanced through different versions, including Matlab-based and stand-alone, and its compatibility with a wide range of commercially available TPSs and treatment units. UCoMX presents a free, user-friendly tool empowering researchers to compute the complexity of treatment plans efficiently.
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Affiliation(s)
- Samuele Cavinato
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Alessandro Scaggion
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Marta Paiusco
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
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Lukhumaidze L, Hogg JC, Bourbeau J, Tan WC, Kirby M. Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning. Acad Radiol 2024:S1076-6332(24)00589-0. [PMID: 39191563 DOI: 10.1016/j.acra.2024.08.030] [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: 04/14/2024] [Revised: 08/03/2024] [Accepted: 08/14/2024] [Indexed: 08/29/2024]
Abstract
RATIONALE AND OBJECTIVES The structural lung features that characterize individuals with preserved ratio impaired spirometry (PRISm) that remain stable overtime are unknown. The objective of this study was to use machine learning models with computed tomography (CT) imaging to classify stable PRISm from stable controls and stable COPD and identify discriminative features. MATERIALS AND METHODS A total of 596 participants that did not transition between control, PRISm and COPD groups at baseline and 3-year follow-up were evaluated: n = 274 with normal lung function (stable control), n = 22 stable PRISm, and n = 300 stable COPD. Investigated features included: quantitative CT (QCT) features (n = 34), such as total lung volume (%TLCCT) and percentage of ground glass and reticulation (%GG+Reticulationtexture), as well as Radiomic (n = 102) features, including varied intensity zone distribution grainy texture (GLDZMZDV). Logistic regression machine learning models were trained using various feature combinations (Base, Base+QCT, Base+Radiomic, Base+QCT+Radiomic). Model performances were evaluated using area under receiver operator curve (AUC) and comparisons between models were made using DeLong test; feature importance was ranked using Shapley Additive Explanations values. RESULTS Machine learning models for all feature combinations achieved AUCs between 0.63-0.84 for stable PRISm vs. stable control, and 0.65-0.92 for stable PRISm vs. stable COPD classification. Models incorporating imaging features outperformed those trained solely on base features (p < 0.05). Compared to stable control and COPD, those with stable PRISm exhibited decreased %TLCCT and increased %GG+Reticulationtexture and GLDZMZDV. CONCLUSION These findings suggest that reduced lung volumes, and elevated high-density and ground glass/reticulation patterns on CT imaging are associated with stable PRISm.
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Affiliation(s)
| | - James C Hogg
- Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada (J.C.H., W.C.T.)
| | - Jean Bourbeau
- Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada (J.B.); Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, Montreal, QC, Canada (J.B.)
| | - Wan C Tan
- Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada (J.C.H., W.C.T.)
| | - Miranda Kirby
- Toronto Metropolitan University, Toronto, ON, Canada (L.L., M.K.).
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Masson-Grehaigne C, Lafon M, Palussière J, Leroy L, Bonhomme B, Jambon E, Italiano A, Cousin S, Crombé A. Single- and multi-site radiomics may improve overall survival prediction for patients with metastatic lung adenocarcinoma. Diagn Interv Imaging 2024:S2211-5684(24)00166-9. [PMID: 39191636 DOI: 10.1016/j.diii.2024.07.005] [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: 06/09/2024] [Accepted: 07/09/2024] [Indexed: 08/29/2024]
Abstract
PURPOSE The purpose of this study was to assess whether single-site and multi-site radiomics could improve the prediction of overall survival (OS) of patients with metastatic lung adenocarcinoma compared to clinicopathological model. MATERIALS AND METHODS Adults with metastatic lung adenocarcinoma, pretreatment whole-body contrast-enhanced computed tomography examinations, and performance status (WHO-PS) ≤ 2 were included in this retrospective single-center study, and randomly assigned to training and testing cohorts. Radiomics features (RFs) were extracted from all measurable lesions with volume ≥ 1 cm3. Radiomics prognostic scores based on the largest tumor (RPSlargest) and the average RF values across all tumors per patient (RPSaverage) were developed in the training cohort using 5-fold cross-validated LASSO-penalized Cox regression. Intra-patient inter-tumor heterogeneity (IPITH) metrics were calculated to quantify the radiophenotypic dissimilarities among all tumors within each patient. A clinicopathological model was built in the training cohort using stepwise Cox regression and enriched with combinations of RPSaverage, RPSlargest and IPITH. Models were compared with the concordance index in the independent testing cohort. RESULTS A total of 300 patients (median age: 63.7 years; 40.7% women; median OS, 16.3 months) with 1359 lesions were included (200 and 100 patients in the training and testing cohorts, respectively). The clinicopathological model included WHO-PS = 2 (hazard ratio [HR] = 3.26; P < 0.0001), EGFR, ALK, ROS1 or RET mutations (HR = 0.57; P = 0.0347), IVB stage (HR = 1.65; P = 0.0211), and liver metastases (HR = 1.47; P = 0.0670). In the testing cohort, RPSaverage, RPSlargest and IPITH were associated with OS (HR = 85.50, P = 0.0038; HR = 18.83, P = 0.0082 and HR = 8.00, P = 0.0327, respectively). The highest concordance index was achieved with the combination of clinicopathological variables and RPSaverage, significantly better than that of the clinicopathological model (concordance index = 0.7150 vs. 0.695, respectively; P = 0.0049) CONCLUSION: Single-site and multi-site radiomics-based scores are associated with OS in patients with metastatic lung adenocarcinoma. RPSaverage improves the clinicopathological model.
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Affiliation(s)
- Cécile Masson-Grehaigne
- Department of Diagnostic and Interventional Oncologic Imaging, Institut Bergonié, 33076 Bordeaux, France; Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France
| | - Mathilde Lafon
- Department of Medical Oncology, Institut Bergonié, 33076 Bordeaux, France
| | - Jean Palussière
- Department of Diagnostic and Interventional Oncologic Imaging, Institut Bergonié, 33076 Bordeaux, France
| | - Laura Leroy
- Department of Medical Oncology, Institut Bergonié, 33076 Bordeaux, France
| | | | - Eva Jambon
- Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France
| | - Antoine Italiano
- Department of Medical Oncology, Institut Bergonié, 33076 Bordeaux, France; SARCOTARGET Team, Bordeaux Institute of Oncology (BRIC) INSERM U1312, Bordeaux 33076, France
| | - Sophie Cousin
- Department of Medical Oncology, Institut Bergonié, 33076 Bordeaux, France
| | - Amandine Crombé
- Department of Diagnostic and Interventional Oncologic Imaging, Institut Bergonié, 33076 Bordeaux, France; Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France; SARCOTARGET Team, Bordeaux Institute of Oncology (BRIC) INSERM U1312, Bordeaux 33076, France.
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Liu YX, Liu QH, Hu QH, Shi JY, Liu GL, Liu H, Shu SC. Ultrasound-Based Deep Learning Radiomics Nomogram for Tumor and Axillary Lymph Node Status Prediction After Neoadjuvant Chemotherapy. Acad Radiol 2024:S1076-6332(24)00470-7. [PMID: 39183131 DOI: 10.1016/j.acra.2024.07.036] [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: 06/05/2024] [Revised: 07/11/2024] [Accepted: 07/19/2024] [Indexed: 08/27/2024]
Abstract
RATIONALE AND OBJECTIVES This study aims to explore the feasibility of the deep learning radiomics nomogram (DLRN) for predicting tumor status and axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) in patients with breast cancer. Additionally, we employ a Cox regression model for survival analysis to validate the effectiveness of the fusion algorithm. MATERIALS AND METHODS A total of 243 patients who underwent NAC were retrospectively included between October 2014 and July 2022. The DLRN integrated clinical characteristics as well as radiomics and deep transfer learning features extracted from ultrasound (US) images. The diagnostic performance of DLRN was evaluated by constructing ROC curves, and the clinical usefulness of models was assessed using decision curve analysis (DCA). A survival model was developed to validate the effectiveness of the fusion algorithm. RESULTS In the training cohort, the DLRN yielded area under the receiver operating characteristic curve values of 0.984 and 0.985 for the tumor and LNM, while 0.892 and 0.870, respectively, in the test cohort. The consistency indices (C-index) of the nomogram were 0.761 and 0.731, respectively, in the training and test cohorts. The Kaplan-Meier survival curves showed that patients in the high-risk group had significantly poorer overall survival than patients in the low-risk group (P < 0.05). CONCLUSION The US-based DLRN model could hold promise as clinical guidance for predicting the status of tumors and LNM after NAC in patients with breast cancer. This fusion model can also predict the prognosis of patients, which could help clinicians make better clinical decisions.
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Affiliation(s)
- Yue-Xia Liu
- Department of Ultrasound, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qing-Hua Liu
- Department of Health Management, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Quan-Hui Hu
- Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jia-Yao Shi
- Department of Ultrasound, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Gui-Lian Liu
- Department of Ultrasound, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Han Liu
- Department of Ultrasound, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Sheng-Chun Shu
- Department of Ultrasound, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
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Imperiale A, Berti V. SDH-related head and neck paragangliomas: Unraveling PET radiomics beyond 18F-FDG. Best Pract Res Clin Endocrinol Metab 2024:101926. [PMID: 39191631 DOI: 10.1016/j.beem.2024.101926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
Radiomics revolutionizes medical imaging by providing quantitative analysis that complements traditional qualitative assessments through advanced computational techniques. In this narrative review we have investigated the impact of succinate dehydrogenase (SDH) pathogenic variants on the radiomic profile of 18F-FDG, 18F-DOPA, and 68Ga-DOTA-peptides PET in paragangliomas, focusing on head and neck localizations (HNPGLs). This influence manifests in uptake intensity and textural heterogeneity, revealing a complex radiomic landscape that may reflect specific tumor behaviors and mutation statuses. By combining radiomic analysis with genetic data, we will gain new insights into the relationship between PET imaging features and underlying molecular changes. In the future, we envision an approach integrating macroscopic indices, such as lesion location, size, and SUV, with advanced computer-based algorithms. This comprehensive analysis could facilitate in vivo predictions of SDH pathogenic variants, thereby encouraging genetic testing, and ultimately improving patient outcomes.
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Affiliation(s)
- Alessio Imperiale
- Nuclear Medicine and Molecular Imaging, ICANS, University Hospitals of Strasbourg, University of Strasbourg, Strasbourg, France; IPHC, UMR 7178, CNRS/Unistra, Strasbourg, France.
| | - Valentina Berti
- Nuclear Medicine Unit, Careggi University Hospital, Florence, Italy; Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', Florence University, Florence, Italy.
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Ahmed TM, Zhu Z, Yasrab M, Blanco A, Kawamoto S, He J, Fishman EK, Chu L, Javed AA. Preoperative Prediction of Lymph Node Metastases in Nonfunctional Pancreatic Neuroendocrine Tumors Using a Combined CT Radiomics-Clinical Model. Ann Surg Oncol 2024:10.1245/s10434-024-16064-4. [PMID: 39179862 DOI: 10.1245/s10434-024-16064-4] [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: 05/10/2024] [Accepted: 08/04/2024] [Indexed: 08/26/2024]
Abstract
BACKGROUND PanNETs are a rare group of pancreatic tumors that display heterogeneous histopathological and clinical behavior. Nodal disease has been established as one of the strongest predictors of patient outcomes in PanNETs. Lack of accurate preoperative assessment of nodal disease is a major limitation in the management of these patients, in particular those with small (< 2 cm) low-grade tumors. The aim of the study was to evaluate the ability of radiomic features (RF) to preoperatively predict the presence of nodal disease in pancreatic neuroendocrine tumors (PanNETs). PATIENTS AND METHODS An institutional database was used to identify patients with nonfunctional PanNETs undergoing resection. Pancreas protocol computed tomography was obtained, manually segmented, and RF were extracted. These were analyzed using the minimum redundancy maximum relevance analysis for hierarchical feature selection. Youden index was used to identify the optimal cutoff for predicting nodal disease. A random forest prediction model was trained using RF and clinicopathological characteristics and validated internally. RESULTS Of the 320 patients included in the study, 92 (28.8%) had nodal disease based on histopathological assessment of the surgical specimen. A radiomic signature based on ten selected RF was developed. Clinicopathological characteristics predictive of nodal disease included tumor grade and size. Upon internal validation the combined radiomics and clinical feature model demonstrated adequate performance (AUC 0.80) in identifying nodal disease. The model accurately identified nodal disease in 85% of patients with small tumors (< 2 cm). CONCLUSIONS Non-invasive preoperative assessment of nodal disease using RF and clinicopathological characteristics is feasible.
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Affiliation(s)
- Taha M Ahmed
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Zhuotun Zhu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Mohammad Yasrab
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Alejandra Blanco
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Jin He
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Linda Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Ammar A Javed
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, NYU Langone Grossman School of Medicine, New York, NY, USA.
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Teng X, Wang Y, Nicol AJ, Ching JCF, Wong EKY, Lam KTC, Zhang J, Lee SWY, Cai J. Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI. Diagnostics (Basel) 2024; 14:1835. [PMID: 39202322 PMCID: PMC11353986 DOI: 10.3390/diagnostics14161835] [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] [Revised: 08/03/2024] [Accepted: 08/16/2024] [Indexed: 09/03/2024] Open
Abstract
Radiomics, which integrates the comprehensive characterization of imaging phenotypes with machine learning algorithms, is increasingly recognized for its potential in the diagnosis and prognosis of oncological conditions. However, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This review aims to address the paucity of discussion regarding the factors that influence the reproducibility and repeatability of radiomic features and their subsequent impact on the application of radiomic models. We provide a synthesis of the literature on the repeatability and reproducibility of CT/MR-based radiomic features, examining sources of variation, the number of reproducible features, and the availability of individual feature repeatability indices. We differentiate sources of variation into random effects, which are challenging to control but can be quantified through simulation methods such as perturbation, and biases, which arise from scanner variability and inter-reader differences and can significantly affect the generalizability of radiomic model performance in diverse settings. Four suggestions for repeatability and reproducibility studies are suggested: (1) detailed reporting of variation sources, (2) transparent disclosure of calculation parameters, (3) careful selection of suitable reliability indices, and (4) comprehensive reporting of reliability metrics. This review underscores the importance of random effects in feature selection and harmonizing biases between development and clinical application settings to facilitate the successful translation of radiomic models from research to clinical practice.
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Affiliation(s)
- Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Yongqiang Wang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Alexander James Nicol
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Jerry Chi Fung Ching
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Edwin Ka Yiu Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Kenneth Tsz Chun Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Shara Wee-Yee Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
- Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518057, China
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Li H, Alves VV, Pednekar A, Manhard MK, Greer J, Trout AT, He L, Dillman JR. Impact of Emerging Deep Learning-Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic Features. J Comput Assist Tomogr 2024:00004728-990000000-00352. [PMID: 39190703 DOI: 10.1097/rct.0000000000001648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
OBJECTIVE This study aims to evaluate, on one MRI vendor's platform, the impact of deep learning (DL)-based reconstruction techniques on MRI radiomic features compared to conventional image reconstruction techniques. METHODS Under IRB approval and informed consent, we prospectively collected undersampled coronal T2-weighted MR images of the abdomen (1.5 T; Philips Healthcare) from 17 pediatric and adult subjects and reconstructed them using a conventional image reconstruction technique (compressed sensitivity encoding [C-SENSE]) and two DL-based reconstruction techniques (SmartSpeed [Philips Healthcare, US FDA cleared] and SmartSpeed with Super Resolution [SmartSpeed-SuperRes, not US FDA cleared to date]). Eight regions of interest (ROIs) across organs/tissues (liver, spleen, kidney, pancreas, fat, and muscle) were manually placed. Eighty-six MRI radiomic features were then extracted. Pearson's correlation coefficients (PCCs) and intraclass correlation coefficients (ICCs) were calculated between (A) C-SENSE versus SmartSpeed, and (B) C-SENSE versus SmartSpeed-SuperRes. To quantify the impact from the perspective of the whole MR image, cross-ROI mean PCCs and ICCs were calculated for individual radiomic features. The impact of image reconstruction on individual radiomic features in different organs/tissues was evaluated using ANOVA analyses. RESULTS According to cross-ROI mean PCCs, 50 out of 86 radiomic features were highly correlated (PCC, ≥0.8) between SmartSpeed and C-SENSE, whereas only 15 radiomic features were highly correlated between SmartSpeed-SuperRes and C-SENSE reconstructions. According to cross-ROI mean ICCs, 58 out of 86 radiomic features had high agreements (ICC ≥0.75) between SmartSpeed and C-SENSE, whereas only 9 radiomic features had high agreements between SmartSpeed-SuperRes and C-SENSE reconstructions. For SmartSpeed reconstruction, the psoas muscle ROI appeared to be impacted most with the lowest median (IQR) correlation of 0.57 (0.25). The circular liver ROI was impacted most by SmartSpeed-SuperRes (PCC, 0.60 [0.22]). ANOVA analyses suggest that the impact of DL reconstruction algorithms on radiomic features varies significantly among different organs/tissues (P < 0.001). CONCLUSIONS MRI radiomic features are significantly altered by DL-based reconstruction compared to a conventional reconstruction technique. The impact of DL reconstruction algorithms on radiomic features varies significantly between different organs/tissues.
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Morelli L, Paganelli C, Marvaso G, Parrella G, Annunziata S, Vicini MG, Zaffaroni M, Pepa M, Summers PE, De Cobelli O, Petralia G, Jereczek-Fossa BA, Baroni G. Addressing intra- and inter-institution variability of a radiomic framework based on Apparent Diffusion Coefficient in prostate cancer. Med Phys 2024. [PMID: 39172115 DOI: 10.1002/mp.17355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 06/27/2024] [Accepted: 08/05/2024] [Indexed: 08/23/2024] Open
Abstract
BACKGROUND Prostate cancer (PCa) is a highly heterogeneous disease, making tailored treatment approaches challenging. Magnetic resonance imaging (MRI), notably diffusion-weighted imaging (DWI) and the derived Apparent Diffusion Coefficient (ADC) maps, plays a crucial role in PCa characterization. In this context, radiomics is a very promising approach able to disclose insights from MRI data. However, the sensitivity of radiomic features to MRI settings, encompassing DWI protocols and multicenter variations, requires the development of robust and generalizable models. PURPOSE To develop a comprehensive radiomics framework for noninvasive PCa characterization using ADC maps, focusing on identifying reliable imaging biomarkers against intra- and inter-institution variations. MATERIALS AND METHODS Two patient cohorts, including an internal cohort (118 PCa patients) used for both training (75%) and hold-out testing (25%), and an external cohort (50 PCa patients) for independent testing, were employed in the study. DWI images were acquired with three different DWI protocols on two different MRI scanners: two DWI protocols acquired on a 1.5-T scanner for the internal cohort, and one DWI protocol acquired on a 3-T scanner for the external cohort. One hundred and seven radiomics features (i.e., shape, first order, texture) were extracted from ADC maps of the whole prostate gland. To address variations in DWI protocols and multicenter variability, a dedicated pipeline, including two-way ANOVA, sequential-feature-selection (SFS), and ComBat features harmonization was implemented. Mann-Whitney U-tests (α = 0.05) were performed to find statistically significant features dividing patients with different tumor characteristics in terms of Gleason score (GS) and T-stage. Support-Vector-Machine models were then developed to predict GS and T-stage, and the performance was assessed through the area under the curve (AUC) of receiver-operating-characteristic curves. RESULTS Downstream of ANOVA, two subsets of 38 and 41 features stable against DWI protocol were identified for GS and T-stage, respectively. Among these, SFS revealed the most predictive features, yielding an AUC of 0.75 (GS) and 0.70 (T-stage) in the hold-out test. Employing ComBat harmonization improved the external-test performance of the GS model, raising AUC from 0.72 to 0.78. CONCLUSION By incorporating stable features with a harmonization procedure and validating the model on an external dataset, model robustness, and generalizability were assessed, highlighting the potential of ADC and radiomics for PCa characterization.
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Affiliation(s)
- Letizia Morelli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Giulia Marvaso
- Department of Radiation Oncology, European Institute of Oncology (IEO), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Giovanni Parrella
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Simone Annunziata
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Maria Giulia Vicini
- Department of Radiation Oncology, European Institute of Oncology (IEO), Milan, Italy
| | - Mattia Zaffaroni
- Department of Radiation Oncology, European Institute of Oncology (IEO), Milan, Italy
| | - Matteo Pepa
- Department of Radiation Oncology, European Institute of Oncology (IEO), Milan, Italy
| | | | - Ottavio De Cobelli
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Department of Urology, European Institute of Oncology (IEO), Milan, Italy
| | - Giuseppe Petralia
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Department of Radiology, European Institute of Oncology (IEO), Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Department of Radiation Oncology, European Institute of Oncology (IEO), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
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Levi R, Mollura M, Savini G, Garoli F, Battaglia M, Ammirabile A, Cappellini LA, Superbi S, Grimaldi M, Barbieri R, Politi LS. A reference framework for standardization and harmonization of CT radiomics features on cadaveric sample. Sci Rep 2024; 14:19259. [PMID: 39164314 PMCID: PMC11336160 DOI: 10.1038/s41598-024-68158-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 07/22/2024] [Indexed: 08/22/2024] Open
Abstract
Radiomics features (RFs) serve as quantitative metrics to characterize shape, density/intensity, and texture patterns in radiological images. Despite their promise, RFs exhibit reproducibility challenges across acquisition settings, thus limiting implementation into clinical practice. In this investigation, we evaluate the effects of different CT scanners and CT acquisition protocols (KV, mA, field-of-view, and reconstruction kernel settings) on RFs extracted from lumbar vertebrae of a cadaveric trunk. Employing univariate and multivariate Generalized Linear Models (GLM), we evaluated the impact of each acquisition parameter on RFs. Our findings indicate that variations in mA had negligible effects on RFs, while alterations in kV resulted in exponential changes in several RFs, notably First Order (94.4%), GLCM (87.5%), and NGTDM (100%). Moreover, we demonstrated that a tailored GLM model was superior to the ComBat algorithm in harmonizing CT images. GLM achieved R2 > 0.90 in 21 RFs (19.6%), contrasting ComBat's mean R2 above 0.90 in only 1 RF (0.9%). This pioneering study unveils the effects of CT acquisition parameters on bone RFs in cadaveric specimens, highlighting significant variations across parameters and scanner datasets. The proposed GLM model presents a robust solution for mitigating these differences, potentially advancing harmonization efforts in Radiomics-based studies across diverse CT protocols and vendors.
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Affiliation(s)
- Riccardo Levi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20072, Milan, Italy
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Maximiliano Mollura
- Department of Electronic, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Giovanni Savini
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Federico Garoli
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20072, Milan, Italy
| | - Massimiliano Battaglia
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20072, Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20072, Milan, Italy
| | - Luca A Cappellini
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20072, Milan, Italy
| | - Simona Superbi
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Marco Grimaldi
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Riccardo Barbieri
- Department of Electronic, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Letterio S Politi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20072, Milan, Italy.
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy.
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Zhu Y, Zheng D, Xu S, Chen J, Wen L, Zhang Z, Ruan H. Intratumoral habitat radiomics based on magnetic resonance imaging for preoperative prediction treatment response to neoadjuvant chemotherapy in nasopharyngeal carcinoma. Jpn J Radiol 2024:10.1007/s11604-024-01639-8. [PMID: 39162780 DOI: 10.1007/s11604-024-01639-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Accepted: 07/27/2024] [Indexed: 08/21/2024]
Abstract
PURPOSE The aim of this study is to determine intratumoral habitat regions from multi-sequences magnetic resonance imaging (MRI) and to assess the value of those regions for prediction of patient response to neoadjuvant chemotherapy (NAC) in nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS Two hundred and ninety seven patients with NPC were enrolled. Multi-sequences MRI data were used to outline three-dimensional volumes of interest (VOI) of the whole tumor. The original imaging data were divided into two groups, which were resampled to an isotropic resolution of 1 × 1 × 1 mm3 (group_1mm) and 3 × 3 × 3 mm3 (group_3mm). Nineteen radiomics features were computed for each voxel of three sequences in group_3mm, within the tumor region to extract local information. Then, k-means clustering was implemented to segment the whole tumor regions in two groups. After radiomics features were extracted and dimension reduction, habitat models were built using Multi-Layer Perceptron (MLP) algorithm. RESULTS Only T stage was included as the clinical model. The habitat3mm model, which included 10 radiomics features, achieved AUCs of 0.752 and 0.724 in the training and validation cohorts, respectively. Given the slightly better outcome of habitat3mm model, nomogram was developed in combination with habitat3mm model and T stage with the AUC of 0.749 and 0.738 in the training and validation cohorts. The decision curve analysis provides further evidence of the nomogram's clinical practicality. CONCLUSIONS A nomogram based on intratumoral habitat predicts the efficacy of NAC in NPC patients, offering the potential to improve both the treatment plan and patient outcomes.
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Affiliation(s)
- Yuemin Zhu
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Dechun Zheng
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China.
| | - Shugui Xu
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Jianwei Chen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Liting Wen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Zhichao Zhang
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Huiping Ruan
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
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Sage A, Miodońska Z, Kręcichwost M, Badura P. Hybridization of Acoustic and Visual Features of Polish Sibilants Produced by Children for Computer Speech Diagnosis. SENSORS (BASEL, SWITZERLAND) 2024; 24:5360. [PMID: 39205053 PMCID: PMC11359356 DOI: 10.3390/s24165360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Revised: 08/14/2024] [Accepted: 08/15/2024] [Indexed: 09/04/2024]
Abstract
Speech disorders are significant barriers to the balanced development of a child. Many children in Poland are affected by lisps (sigmatism)-the incorrect articulation of sibilants. Since speech therapy diagnostics is complex and multifaceted, developing computer-assisted methods is crucial. This paper presents the results of assessing the usefulness of hybrid feature vectors extracted based on multimodal (video and audio) data for the place of articulation assessment in sibilants /s/ and /ʂ/. We used acoustic features and, new in this field, visual parameters describing selected articulators' texture and shape. Analysis using statistical tests indicated the differences between various sibilant realizations in the context of the articulation pattern assessment using hybrid feature vectors. In sound /s/, 35 variables differentiated dental and interdental pronunciation, and 24 were visual (textural and shape). For sibilant /ʂ/, we found 49 statistically significant variables whose distributions differed between speaker groups (alveolar, dental, and postalveolar articulation), and the dominant feature type was noise-band acoustic. Our study suggests hybridizing the acoustic description with video processing provides richer diagnostic information.
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Affiliation(s)
- Agata Sage
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland; (Z.M.); (M.K.); (P.B.)
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Fu S, Chen D, Zhang Y, Yu X, Han L, Yu J, Zheng Y, Zhao L, Xu Y, Tan Y, Yang M. A CT-based radiomics tumor quality and quantity model to predict early recurrence after radical surgery for colorectal liver metastases. Clin Transl Oncol 2024:10.1007/s12094-024-03645-8. [PMID: 39153176 DOI: 10.1007/s12094-024-03645-8] [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: 05/01/2024] [Accepted: 07/29/2024] [Indexed: 08/19/2024]
Abstract
PURPOSE This study aimed to develop a tumor radiomics quality and quantity model (RQQM) based on preoperative enhanced CT to predict early recurrence after radical surgery for colorectal liver metastases (CRLM). METHODS A retrospective analysis was conducted on 282 cases from 3 centers. Clinical risk factors were examined using univariate and multivariate logistic regression (LR) to construct the clinical model. Radiomics features were extracted using the least absolute shrinkage and selection operator (LASSO) for dimensionality reduction. The LR learning algorithm was employed to construct the radiomics model, RQQM (radiomics-TBS), combined model (radiomics-clinical), clinical risk score (CRS) model and tumor burden score (TBS) model. Inter-model comparisons were made using area under the curve (AUC), decision curve analysis (DCA) and calibration curve. Log-rank tests assessed differences in disease-free survival (DFS) and overall survival (OS). RESULTS Clinical features screening identified CRS, KRAS/NRAS/BRAF and liver lobe distribution as risk factors. Radiomics model, RQQM, combined model demonstrated higher AUC values compared to CRS and TBS model in training, internal and external validation cohorts (Delong-test P < 0.05). RQQM outperformed the radiomics model, but was slightly inferior to the combined model. Survival curves revealed statistically significant differences in 1-year DFS and 3-year OS for the RQQM (P < 0.001). CONCLUSIONS RQQM integrates both "quality" (radiomics) and "quantity" (TBS). The radiomics model is superior to the TBS model and has a greater impact on patient prognosis. In the absence of clinical data, RQQM, relying solely on imaging data, shows an advantage in predicting early recurrence after radical surgery for CRLM.
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Affiliation(s)
- Sunya Fu
- Department of Radiology, Ningbo Medical Center LiHuiLi Hospital, 1111 Jiangnan Road, Ningbo, 315040, People's Republic of China
| | - Dawei Chen
- Department of Gastroenterology, Ningbo Medical Center LiHuiLi Hospital, Ningbo, 315040, Zhejiang, People's Republic of China
| | - Yuqin Zhang
- Department of Radiology, Ningbo Medical Center LiHuiLi Hospital, 1111 Jiangnan Road, Ningbo, 315040, People's Republic of China.
| | - Xiao Yu
- Philips Healthcare, Shanghai, 200072, People's Republic of China
| | - Lu Han
- Philips Healthcare, Shanghai, 200072, People's Republic of China
| | - Jiazi Yu
- Department of Colon Anorectal Surgery, Ningbo Medical Center LiHuiLi Hospital, 1111 Jiangnan Road, Ningbo, 315040, Zhejiang, People's Republic of China.
| | - Yupeng Zheng
- Department of Colon Anorectal Surgery, Ningbo Medical Center LiHuiLi Hospital, 1111 Jiangnan Road, Ningbo, 315040, Zhejiang, People's Republic of China
| | - Liang Zhao
- Department of Gastroenterology, Ningbo Medical Center LiHuiLi Hospital, Ningbo, 315040, Zhejiang, People's Republic of China
| | - Yidong Xu
- Department of Colon Anorectal Surgery, Ningbo NO.2 Hospital, Ningbo, 315040, Zhejiang, People's Republic of China
| | - Ying Tan
- Department of Radiology, The Affiliated People's Hospital of Ningbo University, Ningbo, 315040, People's Republic of China
| | - Mian Yang
- Department of Colon Anorectal Surgery, Ningbo Medical Center LiHuiLi Hospital, 1111 Jiangnan Road, Ningbo, 315040, Zhejiang, People's Republic of China
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Chen X, Li M, Su D. Machine learning models for differential diagnosing HER2-low breast cancer: A radiomics approach. Medicine (Baltimore) 2024; 103:e39343. [PMID: 39151526 PMCID: PMC11332746 DOI: 10.1097/md.0000000000039343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 03/26/2024] [Accepted: 07/26/2024] [Indexed: 08/19/2024] Open
Abstract
To develop machine learning models based on preoperative dynamic enhanced magnetic resonance imaging (DCE-MRI) radiomics and to explore their potential prognostic value in the differential diagnosis of human epidermal growth factor receptor 2 (HER2)-low from HER2-positive breast cancer (BC). A total of 233 patients with pathologically confirmed invasive breast cancer admitted to our hospital between January 2018 and December 2022 were included in this retrospective analysis. Of these, 103 cases were diagnosed as HER2-positive and 130 cases were HER2 low-expression BC. The Synthetic Minority Oversampling Technique is employed to address the class imbalance problem. Patients were randomly split into a training set (163 cases) and a validation set (70 cases) in a 7:3 ratio. Radiomics features from DCE-MRI second-phase imaging were extracted. Z-score normalization was used to standardize the radiomics features, and Pearson's correlation coefficient and recursive feature elimination were used to explore the significant features. Prediction models were constructed using 6 machine learning algorithms: logistic regression, random forest, support vector machine, AdaBoost, decision tree, and auto-encoder. Receiver operating characteristic curves were constructed, and predictive models were evaluated according to the area under the curve (AUC), accuracy, sensitivity, and specificity. In the training set, the AUC, accuracy, sensitivity, and specificity of all models were 1.000. However, in the validation set, the auto-encoder model's AUC, accuracy, sensitivity, and specificity were 0.994, 0.976, 0.972, and 0.978, respectively. The remaining models' AUC, accuracy, sensitivity, and specificity were 1.000. The DeLong test showed no statistically significant differences between the machine learning models in the training and validation sets (Z = 0, P = 1). Our study investigated the feasibility of using DCE-MRI-based radiomics features to predict HER2-low BC. Certain radiomics features showed associations with HER2-low BC and may have predictive value. Machine learning prediction models developed using these radiomics features could be beneficial for distinguishing between HER2-low and HER2-positive BC. These noninvasive preoperative models have the potential to assist in clinical decision-making for HER2-low breast cancer, thereby advancing personalized clinical precision.
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Affiliation(s)
- Xianfei Chen
- Department of Radiology, The First Affiliated Hospital, Hainan Medical University, Haikou, China
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Minghao Li
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Danke Su
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
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Jian L, Chen X, Hu P, Li H, Fang C, Wang J, Wu N, Yu X. Predicting progression-free survival in patients with epithelial ovarian cancer using an interpretable random forest model. Heliyon 2024; 10:e35344. [PMID: 39166005 PMCID: PMC11334804 DOI: 10.1016/j.heliyon.2024.e35344] [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: 02/18/2024] [Revised: 07/26/2024] [Accepted: 07/26/2024] [Indexed: 08/22/2024] Open
Abstract
Prognostic models play a crucial role in providing personalised risk assessment, guiding treatment decisions, and facilitating the counselling of patients with cancer. However, previous imaging-based artificial intelligence models of epithelial ovarian cancer lacked interpretability. In this study, we aimed to develop an interpretable machine-learning model to predict progression-free survival in patients with epithelial ovarian cancer using clinical variables and radiomics features. A total of 102 patients with epithelial ovarian cancer who underwent contrast-enhanced computed tomography scans were enrolled in this retrospective study. Pre-surgery clinical data, including age, performance status, body mass index, tumour stage, venous blood cancer antigen-125 (CA125) level, white blood cell count, neutrophil count, red blood cell count, haemoglobin level, and platelet count, were obtained from medical records. The volume of interest for each tumour was manually delineated slice-by-slice along the boundary. A total of 2074 radiomic features were extracted from the pre- and post-contrast computed tomography images. Optimal radiomic features were selected using the Least Absolute Shrinkage and Selection Operator logistic regression. Multivariate Cox analysis was performed to identify independent predictors of three-year progression-free survival. The random forest algorithm developed radiomic and combined models using four-fold cross-validation. Finally, the Shapley additive explanation algorithm was applied to interpret the predictions of the combined model. Multivariate Cox analysis identified CA-125 levels (P = 0.015), tumour stage (P = 0.019), and Radscore (P < 0.001) as independent predictors of progression-free survival. The combined model based on these factors achieved an area under the curve of 0.812 (95 % confidence interval: 0.802-0.822) in the training cohort and 0.772 (95 % confidence interval: 0.727-0.817) in the validation cohort. The most impactful features on the model output were Radscore, followed by tumour stage and CA-125. In conclusion, the Shapley additive explanation-based interpretation of the prognostic model enables clinicians to understand the reasoning behind predictions better.
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Affiliation(s)
- Lian Jian
- Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China
| | - Xiaoyan Chen
- Department of Pathology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China
| | - Pingsheng Hu
- Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China
| | - Handong Li
- Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China
| | - Chao Fang
- Department of Clinical Pharmaceutical Research Institution, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China
| | - Jing Wang
- Department of Clinical Pharmaceutical Research Institution, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China
| | - Nayiyuan Wu
- Central Laboratory, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China
| | - Xiaoping Yu
- Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China
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Chen SQ, Wei L, He K, Xiao YW, Zhang ZT, Dai JK, Shu T, Sun XY, Wu D, Luo Y, Gui YF, Xiao XL. A radiomics nomogram based on multiparametric MRI for diagnosing focal cortical dysplasia and initially identifying laterality. BMC Med Imaging 2024; 24:216. [PMID: 39148028 PMCID: PMC11325615 DOI: 10.1186/s12880-024-01374-6] [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: 11/26/2022] [Accepted: 07/22/2024] [Indexed: 08/17/2024] Open
Abstract
BACKGROUND Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation. The diagnosis of FCD is challenging. We generated a radiomics nomogram based on multiparametric magnetic resonance imaging (MRI) to diagnose FCD and identify laterality early. METHODS Forty-three patients treated between July 2017 and May 2022 with histopathologically confirmed FCD were retrospectively enrolled. The contralateral unaffected hemispheres were included as the control group. Therefore, 86 ROIs were finally included. Using January 2021 as the time cutoff, those admitted after January 2021 were included in the hold-out set (n = 20). The remaining patients were separated randomly (8:2 ratio) into training (n = 55) and validation (n = 11) sets. All preoperative and postoperative MR images, including T1-weighted (T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and combined (T1w + T2w + FLAIR) images, were included. The least absolute shrinkage and selection operator (LASSO) was used to select features. Multivariable logistic regression analysis was used to develop the diagnosis model. The performance of the radiomic nomogram was evaluated with an area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration and clinical utility. RESULTS The model-based radiomics features that were selected from combined sequences (T1w + T2w + FLAIR) had the highest performances in all models and showed better diagnostic performance than inexperienced radiologists in the training (AUCs: 0.847 VS. 0.664, p = 0.008), validation (AUC: 0.857 VS. 0.521, p = 0.155), and hold-out sets (AUCs: 0.828 VS. 0.571, p = 0.080). The positive values of NRI (0.402, 0.607, 0.424) and IDI (0.158, 0.264, 0.264) in the three sets indicated that the diagnostic performance of Model-Combined improved significantly. The radiomics nomogram fit well in calibration curves (p > 0.05), and decision curve analysis further confirmed the clinical usefulness of the nomogram. Additionally, the contrast (the radiomics feature) of the FCD lesions not only played a crucial role in the classifier but also had a significant correlation (r = -0.319, p < 0.05) with the duration of FCD. CONCLUSION The radiomics nomogram generated by logistic regression model-based multiparametric MRI represents an important advancement in FCD diagnosis and treatment.
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Affiliation(s)
- Shi-Qi Chen
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Liang Wei
- Department of Pediatrics, The Affiliated Hospital of Jinggangshan University, Jinggangshan, Jiangxi Province, China
| | - Keng He
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Ya-Wen Xiao
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Zhao-Tao Zhang
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Jian-Kun Dai
- GE Healthcare, MR Research China, Beijing, China
| | - Ting Shu
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Xiao-Yu Sun
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Di Wu
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Yi Luo
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Yi-Fei Gui
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Xin-Lan Xiao
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China.
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50
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Yuan J, Wu M, Qiu L, Xu W, Fei Y, Zhu Y, Shi K, Li Y, Luo J, Ding Z, Sun X, Zhou S. Tumor habitat-based MRI features assessing early response in locally advanced nasopharyngeal carcinoma. Oral Oncol 2024; 158:106980. [PMID: 39151333 DOI: 10.1016/j.oraloncology.2024.106980] [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: 06/05/2024] [Revised: 07/08/2024] [Accepted: 08/02/2024] [Indexed: 08/19/2024]
Abstract
OBJECTIVE The early response to concurrent chemoradiotherapy in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) is closely correlated with prognosis. In this study, we aimed to predict early response using a combined model that combines sub-regional radiomics features from multi-sequence MRI with clinically relevant factors. METHODS A total of 104 patients with LA-NPC were randomly divided into training and test cohorts at a ratio of 3:1. Radiomic features were extracted from subregions within the tumor area using the K-means clustering method, and feature selection was performed using LASSO regression. Four models were established: a radiomics model, a clinical model, an Intratumor Heterogeneity (ITH) score-based model and a combined model that integrates the ITH score with clinical factors. The predictive performance of these models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS Among the models, the combined model incorporating the ITH score and clinical factors exhibited the highest predictive performance in the test cohort (AUC=0.838). Additionally, the models based on ITH score showed superior prognostic value in both the training cohort (AUC=0.888) and the test cohort (AUC=0.833). CONCLUSION The combined model that integrates the ITH score with clinical factors exhibited superior performance in predicting early response following concurrent chemoradiotherapy in patients with LA-NPC.
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Affiliation(s)
- Jinling Yuan
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Mengxing Wu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Lei Qiu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Weilin Xu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Yinjiao Fei
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Yuchen Zhu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Kexin Shi
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Yurong Li
- Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China
| | - Jinyan Luo
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Zhou Ding
- Department of Radiation Oncology, Lianshui County People's Hospital, Huai'an 223400, Jiangsu, China.
| | - Xinchen Sun
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
| | - Shu Zhou
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
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