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Mao Y, Jiang LP, Wang JL, Diao YH, Chen FQ, Zhang WP, Chen L, Liu ZX. Multi-feature Fusion Network on Gray Scale Ultrasonography: Effective Differentiation of Adenolymphoma and Pleomorphic Adenoma. Acad Radiol 2024:S1076-6332(24)00308-8. [PMID: 38871552 DOI: 10.1016/j.acra.2024.05.023] [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: 04/02/2024] [Revised: 05/12/2024] [Accepted: 05/13/2024] [Indexed: 06/15/2024]
Abstract
RATIONALE AND OBJECTIVES to develop a deep learning radiomics graph network (DLRN) that integrates deep learning features extracted from gray scale ultrasonography, radiomics features and clinical features, for distinguishing parotid pleomorphic adenoma (PA) from adenolymphoma (AL) MATERIALS AND METHODS: A total of 287 patients (162 in training cohort, 70 in internal validation cohort and 55 in external validation cohort) from two centers with histologically confirmed PA or AL were enrolled. Deep transfer learning features and radiomics features extracted from gray scale ultrasound images were input to machine learning classifiers including logistic regression (LR), support vector machines (SVM), KNN, RandomForest (RF), ExtraTrees, XGBoost, LightGBM, and MLP to construct deep transfer learning radiomics (DTL) models and Rad models respectively. Deep learning radiomics (DLR) models were constructed by integrating the two features and DLR signatures were generated. Clinical features were further combined with the signatures to develop a DLRN model. The performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test. RESULTS In the internal validation cohort and external validation cohort, comparing to Clinic (AUC=0.767 and 0.777), Rad (AUC=0.841 and 0.748), DTL (AUC=0.740 and 0.825) and DLR (AUC=0.863 and 0.859), the DLRN model showed greatest discriminatory ability (AUC=0.908 and 0.908) showed optimal discriminatory ability. CONCLUSION The DLRN model built based on gray scale ultrasonography significantly improved the diagnostic performance for benign salivary gland tumors. It can provide clinicians with a non-invasive and accurate diagnostic approach, which holds important clinical significance and value. Ensemble of multiple models helped alleviate overfitting on the small dataset compared to using Resnet50 alone.
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Affiliation(s)
- Yi Mao
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Li-Ping Jiang
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Jing-Ling Wang
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Yu-Hong Diao
- Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
| | - Fang-Qun Chen
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Wei-Ping Zhang
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Li Chen
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Zhi-Xing Liu
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China; Department of Ultrasonography, GanJiang New District Peoples Hospital, Nanchang, China.
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Zhou Z, Xia T, Zhang T, Du M, Zhong J, Huang Y, Xuan K, Xu G, Wan Z, Ju S, Xu J. Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography. Abdom Radiol (NY) 2024; 49:611-624. [PMID: 38051358 DOI: 10.1007/s00261-023-04102-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: 08/16/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 12/07/2023]
Abstract
PURPOSE Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. METHODS A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). RESULTS Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). CONCLUSION The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.
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Affiliation(s)
- Zhenghao Zhou
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Tianyi Xia
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China
| | - Teng Zhang
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Mingyang Du
- Cerebrovascular Disease Treatment Center, Nanjing Brain Hospital Affiliated to Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Jiarui Zhong
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China
| | - Yunzhi Huang
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Kai Xuan
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Geyang Xu
- Information School, University of Washington, Seattle, WA, 98195, USA
| | - Zhuo Wan
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Shenghong Ju
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China.
| | - Jun Xu
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
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Mundt P, Hertel A, Tharmaseelan H, Nörenberg D, Papavassiliu T, Schoenberg SO, Froelich MF, Ayx I. Analysis of Epicardial Adipose Tissue Texture in Relation to Coronary Artery Calcification in PCCT: The EAT Signature! Diagnostics (Basel) 2024; 14:277. [PMID: 38337793 PMCID: PMC10854976 DOI: 10.3390/diagnostics14030277] [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: 01/03/2024] [Revised: 01/25/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
(1) Background: Epicardial adipose tissue influences cardiac biology in physiological and pathological terms. As it is suspected to be linked to coronary artery calcification, identifying improved methods of diagnostics for these patients is important. The use of radiomics and the new Photon-Counting computed tomography (PCCT) may offer a feasible step toward improved diagnostics in these patients. (2) Methods: In this retrospective single-centre study epicardial adipose tissue was segmented manually on axial unenhanced images. Patients were divided into three groups, depending on the severity of coronary artery calcification. Features were extracted using pyradiomics. Mean and standard deviation were calculated with the Pearson correlation coefficient for feature correlation. Random Forest classification was applied for feature selection and ANOVA was performed for group comparison. (3) Results: A total of 53 patients (32 male, 21 female, mean age 57, range from 21 to 80 years) were enrolled in this study and scanned on the novel PCCT. "Original_glrlm_LongRunEmphasis", "original_glrlm_RunVariance", "original_glszm_HighGrayLevelZoneEmphasis", and "original_glszm_SizeZoneNonUniformity" were found to show significant differences between patients with coronary artery calcification (Agatston score 1-99/≥100) and those without. (4) Conclusions: Four texture features of epicardial adipose tissue are associated with coronary artery calcification and may reflect inflammatory reactions of epicardial adipose tissue, offering a potential imaging biomarker for atherosclerosis detection.
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Affiliation(s)
- Peter Mundt
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, 68167 Mannheim, Germany; (P.M.); (A.H.); (H.T.); (D.N.); (S.O.S.); (M.F.F.)
| | - Alexander Hertel
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, 68167 Mannheim, Germany; (P.M.); (A.H.); (H.T.); (D.N.); (S.O.S.); (M.F.F.)
| | - Hishan Tharmaseelan
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, 68167 Mannheim, Germany; (P.M.); (A.H.); (H.T.); (D.N.); (S.O.S.); (M.F.F.)
| | - Dominik Nörenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, 68167 Mannheim, Germany; (P.M.); (A.H.); (H.T.); (D.N.); (S.O.S.); (M.F.F.)
| | - Theano Papavassiliu
- First Department of Internal Medicine-Cardiology, University Medical Centre Mannheim, and DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, 68167 Mannheim, Germany
| | - Stefan O. Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, 68167 Mannheim, Germany; (P.M.); (A.H.); (H.T.); (D.N.); (S.O.S.); (M.F.F.)
| | - Matthias F. Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, 68167 Mannheim, Germany; (P.M.); (A.H.); (H.T.); (D.N.); (S.O.S.); (M.F.F.)
| | - Isabelle Ayx
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, 68167 Mannheim, Germany; (P.M.); (A.H.); (H.T.); (D.N.); (S.O.S.); (M.F.F.)
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Lu HJ, Shen CY, Chiu YW, Lin WL, Peng CY, Tseng HC, Hsin CH, Chuang CY, Chen CC, Wu MF, Huang WS, Shen WC. Radiomic biomarkers for platinum-refractory head and neck cancer in the era of immunotherapy. Oral Dis 2024. [PMID: 38178608 DOI: 10.1111/odi.14854] [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: 06/16/2023] [Revised: 11/17/2023] [Accepted: 12/20/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVE Immune checkpoint inhibitors (ICI) are recommended as the first-line therapy for platinum-refractory head and neck squamous cell carcinoma (HNSCC), a disease with a poor prognosis. However, biomarkers in this situation are rare. The objective was to identify radiomic features-associated biomarkers to guide the prognosis and treatment opinions in the era of ICI. METHODS A total of 31 platinum-refractory HNSCC patients were retrospectively enrolled. Of these, 65.5% (20/31) received ICI-based therapy and 35.5% (11/31) did not. Radiomic features of the primary site at the onset of recurrent metastatic (R/M) status were extracted. Prognostic and predictive radiomic biomarkers were analysed. RESULTS The median overall survival from R/M status (R/M OS) was 9.6 months. Grey-level co-occurrence matrix-associated texture features were the most important in identifying the patients with or without 9-month R/M death. A radiomic risk-stratification model was established and equally separated the patients into high-, intermittent- and lower-risk groups (1-year R/M death rate, 100.0% vs. 70.8% vs. 27.1%, p = 0.001). Short-run high grey-level emphasis (SRHGE) was more suitable than programmed death ligand 1 (PD-L1) expression in selecting whether patients received ICI-based therapy. CONCLUSIONS Radiomic features were effective prognostic and predictive biomarkers. Future studies are warranted.
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Affiliation(s)
- Hsueh-Ju Lu
- Division of Hematology and Oncology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Chao-Yu Shen
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Yu-Wei Chiu
- Department of Stomatology, Chung Shan Medical University Hospital, Taichung, Taiwan
- College of Oral Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Wea-Lung Lin
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Pathology, Chung Shan Medical University and Hospital, Taichung, Taiwan
| | - Chih-Yu Peng
- Department of Stomatology, Chung Shan Medical University Hospital, Taichung, Taiwan
- College of Oral Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Hsien-Chun Tseng
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Radiation Oncology, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Chung-Han Hsin
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Otolaryngology, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Chun-Yi Chuang
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Otolaryngology, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Chun-Chia Chen
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Division of Plastic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Ming-Fang Wu
- Division of Hematology and Oncology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Wei-Shiou Huang
- Division of Hematology and Oncology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Wei-Chih Shen
- Department of Medical Informatics, Chung Shan Medical University, Taichung, Taiwan
- Artificial Intelligence Center, Chung Shan Medical University Hospital, Taichung, Taiwan
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Hussain S, Lafarga-Osuna Y, Ali M, Naseem U, Ahmed M, Tamez-Peña JG. Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review. BMC Bioinformatics 2023; 24:401. [PMID: 37884877 PMCID: PMC10605943 DOI: 10.1186/s12859-023-05515-6] [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: 04/07/2023] [Accepted: 10/02/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images. OBJECTIVE AND METHODS This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers. RESULTS A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images. CONCLUSION Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.
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Affiliation(s)
- Sadam Hussain
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico.
| | - Yareth Lafarga-Osuna
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
| | - Mansoor Ali
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
| | - Usman Naseem
- College of Science and Engineering, James Cook University, Cairns, Australia
| | - Masroor Ahmed
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
| | - Jose Gerardo Tamez-Peña
- School of Medicine and Health Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
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Jha AK, Mithun S, Sherkhane UB, Dwivedi P, Puts S, Osong B, Traverso A, Purandare N, Wee L, Rangarajan V, Dekker A. Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:569-582. [PMID: 37720353 PMCID: PMC10501896 DOI: 10.37349/etat.2023.00153] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/20/2023] [Indexed: 09/19/2023] Open
Abstract
Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.
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Affiliation(s)
- Ashish Kumar Jha
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Sneha Mithun
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Umeshkumar B. Sherkhane
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
| | - Pooj Dwivedi
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
- Department of Nuclear Medicine, Advance Center for Treatment, Research, Education in Cancer, Kharghar, Navi-Mumbai 410210, Maharashtra, India
| | - Senders Puts
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Biche Osong
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Nilendu Purandare
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
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Mireștean CC, Iancu RI, Iancu DPT. Image Guided Radiotherapy (IGRT) and Delta (Δ) Radiomics-An Urgent Alliance for the Front Line of the War against Head and Neck Cancers. Diagnostics (Basel) 2023; 13:2045. [PMID: 37370940 DOI: 10.3390/diagnostics13122045] [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: 03/15/2023] [Revised: 05/24/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
The identification of a biomarker that is response predictive could offer a solution for the stratification of the treatment of head and neck cancers (HNC) in the context of high recurrence rates, especially those associated with loco-regional failure. Delta (Δ) radiomics, a concept based on the variation of parameters extracted from medical imaging using artificial intelligence (AI) algorithms, demonstrates its potential as a predictive biomarker of treatment response in HNC. The concept of image-guided radiotherapy (IGRT), including computer tomography simulation (CT) and position control imaging with cone-beam-computed tomography (CBCT), now offers new perspectives for radiomics applied in radiotherapy. The use of Δ features of texture, shape, and size, both from the primary tumor and from the tumor-involved lymph nodes, demonstrates the best predictive accuracy. If, in the case of treatment response, promising Δ radiomics results could be obtained, even after 24 h from the start of treatment, for radiation-induced xerostomia, the evaluation of Δ radiomics in the middle of treatment could be recommended. The fused models (clinical and Δ radiomics) seem to offer benefits, both in comparison to the clinical model and to the radiomic model. The selection of patients who benefit from induction chemotherapy is underestimated in Δ radiomic studies and may be an unexplored territory with major potential. The advantage offered by "in house" simulation CT and CBCT favors the rapid implementation of Δ radiomics studies in radiotherapy departments. Positron emission tomography (PET)-CT Δ radiomics could guide the new concepts of dose escalation on radio-resistant sub-volumes based on radiobiological criteria, but also guide the "next level" of HNC adaptive radiotherapy (ART).
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Affiliation(s)
- Camil Ciprian Mireștean
- Department of Oncology and Radiotherapy, University of Medicine and Pharmacy Craiova, 200349 Craiova, Romania
- Department of Surgery, Railways Clinical Hospital Iasi, 700506 Iași, Romania
| | - Roxana Irina Iancu
- Oral Pathology Department, "Gr. T. Popa" Faculty of Dental Medicine, University of Medicine and Pharmacy, 700115 Iași, Romania
- Department of Clinical Laboratory, "St. Spiridon" Emergency Universitary Hospital, 700111 Iași, Romania
| | - Dragoș Petru Teodor Iancu
- Oncology and Radiotherapy Department, Faculty of Medicine, "Gr. T. Popa" University of Medicine and Pharmacy, 700115 Iași, Romania
- Department of Radiation Oncology, Regional Institute of Oncology, 700483 Iași, Romania
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Uncertainty Assessment for Deep Learning Radiotherapy Applications. Semin Radiat Oncol 2022; 32:304-318. [DOI: 10.1016/j.semradonc.2022.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Mireștean CC, Iancu RI, Iancu DPT. Capecitabine—A “Permanent Mission” in Head and Neck Cancers “War Council”? J Clin Med 2022; 11:jcm11195582. [PMID: 36233450 PMCID: PMC9573684 DOI: 10.3390/jcm11195582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 09/09/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022] Open
Abstract
Capecitabine, an oral pro-drug that is metabolized to 5-FU, has been used in clinical practice for more than 20 years, being part of the therapeutic standard for digestive and breast cancers. The use of capecitabine has been evaluated in many trials including cases diagnosed in recurrent or metastatic settings. Induction regimens or a combination with radiation therapy were evaluated in head and neck cancers, but 5-FU still remained the fluoropyrimidine used as a part of the current therapeutic standard. Quantifications of levels or ratios for enzymes are involved in the capecitabine metabolism to 5-FU but are also involved in its conversion and elimination that may lead to discontinuation, dose reduction or escalation of treatment in order to obtain the best therapeutic ratio. These strategies based on biomarkers may be relevant in the context of the implementation of precision oncology. In particular for head and neck cancers, the identification of biomarkers to select possible cases of severe toxicity requiring discontinuation of treatment, including “multi-omics” approaches, evaluate not only serological biomarkers, but also miRNAs, imaging and radiomics which will ensure capecitabine a role in both induction and concomitant or even adjuvant and palliative settings. An approach including routine testing of dihydropyrimidine dehydrogenase (DPD) or even the thymidine phosphorylase (TP)/DPD ratio and the inclusion of miRNAs, imaging and radiomics parameters in multi-omics models will help implement “precision chemotherapy” in HNC, a concept supported by the importance of avoiding interruptions or treatment delays in this type of cancer. The chemosensitivity and prognostic features of HPV-OPC cancers open new horizons for the use of capecitabine in heavily pretreated metastatic cases. Vorinostat and lapatinib are agents that can be associated with capecitabine in future clinical trials to increase the therapeutic ratio.
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Affiliation(s)
- Camil Ciprian Mireștean
- Department of Medical Oncology and Radiotherapy, University of Medicine and Pharmacy Craiova, 200349 Craiova, Romania
- Department of Surgery, Railways Clinical Hospital, 700506 Iasi, Romania
| | - Roxana Irina Iancu
- Oral Pathology Department, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Clinical Laboratory, St. Spiridon Emergency Hospital, 700111 Iasi, Romania
- Correspondence: ; Tel.: +40-232-301-603
| | - Dragoș Petru Teodor Iancu
- Department of Medical Oncology and Radiotherapy, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Radiation Oncology, Regional Institute of Oncology, 700483 Iasi, Romania
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