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Chakrabarty N, Mahajan A. Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review. Clin Oncol (R Coll Radiol) 2024; 36:498-513. [PMID: 37806795 DOI: 10.1016/j.clon.2023.09.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/09/2023] [Accepted: 09/21/2023] [Indexed: 10/10/2023]
Abstract
The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains and the use of transfer learning. Here we discuss the multifaceted role of deep learning in cancer care, ranging from risk stratification, the screening and diagnosis of cancer, to the prediction of genomic mutations, treatment response and survival outcome prediction, through the use of convolutional neural networks. Another role of artificial intelligence is in the generation of automated radiology reports, which is a boon in high-volume centres to minimise report turnaround time. Although a validated and deployable deep-learning model for clinical use is still in its infancy, there is ongoing research to overcome the barriers for its universal implementation and we also delve into this aspect. We also briefly describe the role of radiomics in oncoimaging. Artificial intelligence can provide answers pertaining to cancer management at baseline imaging, saving cost and time. Imaging biobanks, which are repositories of anonymised images, are also briefly described. We also discuss the commercialisation and ethical issues pertaining to artificial intelligence. The latest generation generalist artificial intelligence model is also briefly described at the end of the article. We believe this article will not only enrich knowledge, but also promote research acumen in the minds of readers to take oncoimaging to another level using artificial intelligence and also work towards clinical translation of such research.
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Affiliation(s)
- N Chakrabarty
- Department of Radiodiagnosis, Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Parel, Mumbai, Maharashtra, India.
| | - A Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK.
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de Oliveira LAP, Lopes DLG, Gomes JPP, da Silveira RV, Nozaki DVA, Santos LF, Castellano G, de Castro Lopes SLP, Costa ALF. Enhanced Diagnostic Precision: Assessing Tumor Differentiation in Head and Neck Squamous Cell Carcinoma Using Multi-Slice Spiral CT Texture Analysis. J Clin Med 2024; 13:4038. [PMID: 39064078 PMCID: PMC11277332 DOI: 10.3390/jcm13144038] [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: 05/13/2024] [Revised: 06/28/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
This study explores the efficacy of texture analysis by using preoperative multi-slice spiral computed tomography (MSCT) to non-invasively determine the grade of cellular differentiation in head and neck squamous cell carcinoma (HNSCC). In a retrospective study, MSCT scans of patients with HNSCC were analyzed and classified based on its histological grade as moderately differentiated, well-differentiated, or poorly differentiated. The location of the tumor was categorized as either in the bone or in soft tissues. Segmentation of the lesion areas was conducted, followed by texture analysis. Eleven GLCM parameters across five different distances were calculated. Median values and correlations of texture parameters were examined in relation to tumor differentiation grade by using Spearman's correlation coefficient and Kruskal-Wallis and Dunn tests. Forty-six patients were included, predominantly female (87%), with a mean age of 66.7 years. Texture analysis revealed significant parameter correlations with histopathological grades of tumor differentiation. The study identified no significant age correlation with tumor differentiation, which underscores the potential of texture analysis as an age-independent biomarker. The strong correlations between texture parameters and histopathological grades support the integration of this technique into the clinical decision-making process.
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Affiliation(s)
- Lays Assolini Pinheiro de Oliveira
- Department of Anesthesiology, Oncology and Radiology, Faculty of Medical Sciences, University of Campinas (UNICAMP), Campinas 13084-971, SP, Brazil;
- Postgraduate Program in Dentistry, Dentomaxillofacial Radiology and Imaging Laboratory, Department of Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 01506-000, SP, Brazil;
| | - Diana Lorena Garcia Lopes
- Postgraduate Program in Dentistry, Dentomaxillofacial Radiology and Imaging Laboratory, Department of Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 01506-000, SP, Brazil;
| | - João Pedro Perez Gomes
- Department of Stomatology, Division of Oral Radiology, School of Dentistry, University of São Paulo (USP), São Paulo 05508-000, SP, Brazil;
| | - Rafael Vinicius da Silveira
- Institute of Physics Gleb Wataghin, Universidade Estadual de Campinas (UNICAMP), Campinas 13084-971, SP, Brazil; (R.V.d.S.); (G.C.)
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas 13083-970, SP, Brazil
| | | | - Lana Ferreira Santos
- Department of Diagnosis and Surgery, São José dos Campos School of Dentistry, São Paulo State University (UNESP), São José dos Campos, São Paulo 12245-000, SP, Brazil; (L.F.S.); (S.L.P.d.C.L.)
| | - Gabriela Castellano
- Institute of Physics Gleb Wataghin, Universidade Estadual de Campinas (UNICAMP), Campinas 13084-971, SP, Brazil; (R.V.d.S.); (G.C.)
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas 13083-970, SP, Brazil
| | - Sérgio Lúcio Pereira de Castro Lopes
- Department of Diagnosis and Surgery, São José dos Campos School of Dentistry, São Paulo State University (UNESP), São José dos Campos, São Paulo 12245-000, SP, Brazil; (L.F.S.); (S.L.P.d.C.L.)
| | - Andre Luiz Ferreira Costa
- Department of Anesthesiology, Oncology and Radiology, Faculty of Medical Sciences, University of Campinas (UNICAMP), Campinas 13084-971, SP, Brazil;
- Postgraduate Program in Dentistry, Dentomaxillofacial Radiology and Imaging Laboratory, Department of Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 01506-000, SP, Brazil;
- School of Dentistry of Paulista Association of Dentists (FAOA), São Paulo 02010-000, SP, Brazil;
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Bicci E, Calamandrei L, Di Finizio A, Pietragalla M, Paolucci S, Busoni S, Mungai F, Nardi C, Bonasera L, Miele V. Predicting Response to Exclusive Combined Radio-Chemotherapy in Naso-Oropharyngeal Cancer: The Role of Texture Analysis. Diagnostics (Basel) 2024; 14:1036. [PMID: 38786334 PMCID: PMC11120575 DOI: 10.3390/diagnostics14101036] [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: 03/31/2024] [Revised: 04/28/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
The aim of this work is to identify MRI texture features able to predict the response to radio-chemotherapy (RT-CHT) in patients with naso-oropharyngeal carcinoma (NPC-OPC) before treatment in order to help clinical decision making. Textural features were derived from ADC maps and post-gadolinium T1-images on a single MRI machine for 37 patients with NPC-OPC. Patients were divided into two groups (responders/non-responders) according to results from MRI scans and 18F-FDG-PET/CT performed at follow-up 3-4 and 12 months after therapy and biopsy. Pre-RT-CHT lesions were segmented, and radiomic features were extracted. A non-parametric Mann-Whitney test was performed. A p-value < 0.05 was considered significant. Receiver operating characteristic curves and area-under-the-curve values were generated; a 95% confidence interval (CI) was reported. A radiomic model was constructed using the LASSO algorithm. After feature selection on MRI T1 post-contrast sequences, six features were statistically significant: gldm_DependenceEntropy and DependenceNonUniformity, glrlm_RunEntropy and RunLengthNonUniformity, and glszm_SizeZoneNonUniformity and ZoneEntropy, with significant cut-off values between responder and non-responder group. With the LASSO algorithm, the radiomic model showed an AUC of 0.89 and 95% CI: 0.78-0.99. In ADC, five features were selected with an AUC of 0.84 and 95% CI: 0.68-1. Texture analysis on post-gadolinium T1-images and ADC maps could potentially predict response to therapy in patients with NPC-OPC who will undergo exclusive treatment with RT-CHT, being, therefore, a useful tool in therapeutical-clinical decision making.
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Affiliation(s)
- Eleonora Bicci
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy; (F.M.); (L.B.); (V.M.)
| | - Leonardo Calamandrei
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy; (L.C.); (A.D.F.) (C.N.)
| | - Antonio Di Finizio
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy; (L.C.); (A.D.F.) (C.N.)
| | - Michele Pietragalla
- Department of Radiology, Ospedale San Jacopo, Via Ciliegiole 97, 51100 Pistoia, Italy;
| | - Sebastiano Paolucci
- Department of Health Physics, L.Go Brambilla, Careggi University Hospital, 50134 Florence, Italy; (S.P.); (S.B.)
| | - Simone Busoni
- Department of Health Physics, L.Go Brambilla, Careggi University Hospital, 50134 Florence, Italy; (S.P.); (S.B.)
| | - Francesco Mungai
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy; (F.M.); (L.B.); (V.M.)
| | - Cosimo Nardi
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy; (L.C.); (A.D.F.) (C.N.)
| | - Luigi Bonasera
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy; (F.M.); (L.B.); (V.M.)
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy; (F.M.); (L.B.); (V.M.)
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Ansari G, Mirza-Aghazadeh-Attari M, Mosier KM, Fakhry C, Yousem DM. Radiomics Features in Predicting Human Papillomavirus Status in Oropharyngeal Squamous Cell Carcinoma: A Systematic Review, Quality Appraisal, and Meta-Analysis. Diagnostics (Basel) 2024; 14:737. [PMID: 38611650 PMCID: PMC11011663 DOI: 10.3390/diagnostics14070737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/18/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
We sought to determine the diagnostic accuracy of radiomics features in predicting HPV status in oropharyngeal squamous cell carcinoma (SCC) compared to routine paraclinical measures used in clinical practice. Twenty-six articles were included in the systematic review, and thirteen were used for the meta-analysis. The overall sensitivity of the included studies was 0.78, the overall specificity was 0.76, and the overall area under the ROC curve was 0.84. The diagnostic odds ratio (DOR) equaled 12 (8, 17). Subgroup analysis showed no significant difference between radiomics features extracted from CT or MR images. Overall, the studies were of low quality in regard to radiomics quality score, although most had a low risk of bias based on the QUADAS-2 tool. Radiomics features showed good overall sensitivity and specificity in determining HPV status in OPSCC, though the low quality of the included studies poses problems for generalizability.
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Affiliation(s)
- Golnoosh Ansari
- Department of Radiology, Northwestern Hospital, Northwestern School of Medicine, Chicago, IL 60611, USA;
| | - Mohammad Mirza-Aghazadeh-Attari
- Division of Interventional Radiology, Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Kristine M. Mosier
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Carole Fakhry
- Department of Otolaryngology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA;
| | - David M. Yousem
- Division of Neuroradiology, Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA;
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Li Y, Li J, Meng M, Duan S, Shi H, Hang J. Development and Validation of a Radiomics Nomogram for Liver Metastases Originating from Gastric and Colorectal Cancer. Diagnostics (Basel) 2023; 13:2937. [PMID: 37761304 PMCID: PMC10528017 DOI: 10.3390/diagnostics13182937] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/04/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
The origin of metastatic liver tumours (arising from gastric or colorectal sources) is closely linked to treatment choices and survival prospects. However, in some instances, the primary lesion remains elusive even after an exhaustive diagnostic investigation. Consequently, we have devised and validated a radiomics nomogram for ascertaining the primary origin of liver metastases stemming from gastric cancer (GCLMs) and colorectal cancer (CCLMs). This retrospective study encompassed patients diagnosed with either GCLMs or CCLMs, comprising a total of 277 GCLM cases and 278 CCLM cases. Radiomic characteristics were derived from venous phase computed tomography (CT) scans, and a radiomics signature (RS) was computed. Multivariable regression analysis demonstrated that gender (OR = 3.457; 95% CI: 2.102-5.684; p < 0.001), haemoglobin levels (OR = 0.976; 95% CI: 0.967-0.986; p < 0.001), carcinoembryonic antigen (CEA) levels (OR = 0.500; 95% CI: 0.307-0.814; p = 0.005), and RS (OR = 2.147; 95% CI: 1.127-4.091; p = 0.020) exhibited independent associations with GCLMs as compared to CCLMs. The nomogram, combining RS with clinical variables, demonstrated strong discriminatory power in both the training (AUC = 0.71) and validation (AUC = 0.78) cohorts. The calibration curve, decision curve analysis, and clinical impact curves revealed the clinical utility of this nomogram and substantiated its enhanced diagnostic performance.
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Affiliation(s)
- Yuying Li
- Department of Radiology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China; (Y.L.); (J.L.); (M.M.)
- Graduate College, Dalian Medical University, Dalian 116000, China
| | - Jingjing Li
- Department of Radiology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China; (Y.L.); (J.L.); (M.M.)
- Graduate College, Dalian Medical University, Dalian 116000, China
| | - Mingzhu Meng
- Department of Radiology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China; (Y.L.); (J.L.); (M.M.)
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai 201100, China;
| | - Haifeng Shi
- Department of Radiology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China; (Y.L.); (J.L.); (M.M.)
| | - Junjie Hang
- Department of Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
- Department of Oncology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China
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Bicci E, Nardi C, Calamandrei L, Barcali E, Pietragalla M, Calistri L, Desideri I, Mungai F, Bonasera L, Miele V. Magnetic resonance imaging in naso-oropharyngeal carcinoma: role of texture analysis in the assessment of response to radiochemotherapy, a preliminary study. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01653-2. [PMID: 37336860 DOI: 10.1007/s11547-023-01653-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/25/2023] [Indexed: 06/21/2023]
Abstract
OBJECTIVE Identifying MRI texture parameters able to distinguish inflammation, fibrosis, and residual cancer in patients with naso-oropharynx carcinoma after radiochemotherapy (RT-CHT). MATERIAL AND METHODS In this single-centre, observational, retrospective study, texture analysis was performed on ADC maps and post-gadolinium T1 images of patients with histological diagnosis of naso-oropharyngeal carcinoma treated with RT-CHT. An initial cohort of 99 patients was selected; 57 of them were later excluded. The final cohort of 42 patients was divided into 3 groups (inflammation, fibrosis, and residual cancer) according to MRI, 18F-FDG-PET/CT performed 3-4 months after RT-CHT, and biopsy. Pre-RT-CHT lesions and the corresponding anatomic area post-RT-CHT were segmented with 3D slicer software from which 107 textural features were derived. T-Student and Wilcoxon signed-rank tests were performed, and features with p-value < 0.01 were considered statistically significant. Cut-off values-obtained by ROC curves-to discriminate post-RT-CHT non-tumoural changes from residual cancer were calculated for the parameters statistically associated to the diseased status at follow-up. RESULTS Two features-Energy and Grey Level Non-Uniformity-were statistically significant on T1 images in the comparison between 'positive' (residual cancer) and 'negative' patients (inflammation and fibrosis). Energy was also found to be statistically significant in both patients with fibrosis and residual cancer. Grey Level Non-Uniformity was significant in the differentiation between residual cancer and inflammation. Five features were statistically significant on ADC maps in the differentiation between 'positive' and 'negative' patients. The reduction in values of such features between pre- and post-RT-CHT was correlated with a good response to therapy. CONCLUSIONS Texture analysis on post-gadolinium T1 images and ADC maps can differentiate residual cancer from fibrosis and inflammation in early follow-up of naso-oropharyngeal carcinoma treated with RT-CHT.
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Affiliation(s)
- Eleonora Bicci
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Cosimo Nardi
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Leonardo Calamandrei
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Eleonora Barcali
- Department of Information Engineering, University of Florence, 50139, Florence, Italy
| | - Michele Pietragalla
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Linda Calistri
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Isacco Desideri
- Radiation Oncology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Francesco Mungai
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Luigi Bonasera
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Vittorio Miele
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
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Leijenaar RTH, Walsh S, Aliboni L, Sanchez VL, Leech M, Joyce R, Gillham C, Kridelka F, Hustinx R, Danthine D, Occhipinti M, Vos W, Guiot J, Lambin P, Lovinfosse P. External validation of a radiomic signature to predict p16 (HPV) status from standard CT images of anal cancer patients. Sci Rep 2023; 13:7198. [PMID: 37137947 PMCID: PMC10156720 DOI: 10.1038/s41598-023-34162-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 04/25/2023] [Indexed: 05/05/2023] Open
Abstract
The paper deals with the evaluation of the performance of an existing and previously validated CT based radiomic signature, developed in oropharyngeal cancer to predict human papillomavirus (HPV) status, in the context of anal cancer. For the validation in anal cancer, a dataset of 59 patients coming from two different centers was collected. The primary endpoint was HPV status according to p16 immunohistochemistry. Predefined statistical tests were performed to evaluate the performance of the model. The AUC obtained here in anal cancer is 0.68 [95% CI (0.32-1.00)] with F1 score of 0.78. This signature is TRIPOD level 4 (57%) with an RQS of 61%. This study provides proof of concept that this radiomic signature has the potential to identify a clinically relevant molecular phenotype (i.e., the HPV-ness) across multiple cancers and demonstrates potential for this radiomic signature as a CT imaging biomarker of p16 status.
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Affiliation(s)
| | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | | | - Michelle Leech
- Applied Radiation Therapy, Discipline of Radiation Therapy, Trinity St. James's Cancer Institute, Trinity College, Dublin, Ireland
| | - Ronan Joyce
- Department of Radiation Oncology, St. Luke's Radiation Oncology Network and St James's Hospital, Dublin, Ireland
| | - Charles Gillham
- Department of Radiation Oncology, St. Luke's Radiation Oncology Network and St James's Hospital, Dublin, Ireland
| | - Frédéric Kridelka
- Department of Obstetrics and Gynecology, University Hospital of Liège, Liège, Belgium
| | - Roland Hustinx
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Denis Danthine
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | | | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Julien Guiot
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Pierre Lovinfosse
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium.
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Ricardo ALF, da Silva GA, Ogawa CM, Nussi AD, De Rosa CS, Martins JS, de Castro Lopes SLP, Appenzeller S, Braz-Silva PH, Costa ALF. Magnetic resonance imaging texture analysis for quantitative evaluation of the mandibular condyle in juvenile idiopathic arthritis. Oral Radiol 2023; 39:329-340. [PMID: 35948783 DOI: 10.1007/s11282-022-00641-y] [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: 05/04/2022] [Accepted: 07/13/2022] [Indexed: 10/15/2022]
Abstract
OBJECTIVES Juvenile idiopathic arthritis (JIA) is a chronic inflammatory disease that affects the joints and other organs, including the development of the former in a growing child. This study aimed to evaluate the feasibility of texture analysis (TA) based on magnetic resonance imaging (MRI) to provide biomarkers that serve to identify patients likely to progress to temporomandibular joint damage by associating JIA with age, gender and disease onset age. METHODS The radiological database was retrospectively reviewed. A total of 45 patients were first divided into control group (23) and JIA group (22). TA was performed using grey-level co-occurrence matrix (GLCM) parameters, in which 11 textural parameters were calculated using MaZda software. These 11 parameters were ranked based on the p value obtained with ANOVA and then correlated with age, gender and disease onset age. RESULTS Significant differences in texture parameters of condyle were demonstrated between JIA group and control group (p < 0.05). There was a progressive loss of uniformity in the grayscale pixels of MRI with an increasing age in JIA group. CONCLUSIONS MRI TA of the condyle can make it possible to detect the alterations in bone marrow of patients with JIA and promising tool which may help the image analysis.
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Affiliation(s)
- Ana Lúcia Franco Ricardo
- Postgraduate Program in Dentistry, Cruzeiro Do Sul University (UNICSUL), São Paulo, 01506-000, Brazil
| | - Gabriel Araújo da Silva
- Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), Campinas, Brazil
| | - Celso Massahiro Ogawa
- Postgraduate Program in Dentistry, Cruzeiro Do Sul University (UNICSUL), São Paulo, 01506-000, Brazil
| | - Amanda D Nussi
- Postgraduate Program in Dentistry, Cruzeiro Do Sul University (UNICSUL), São Paulo, 01506-000, Brazil
| | | | - Jaqueline Serra Martins
- Rheumatology Department, Faculty of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Sérgio Lúcio Pereira de Castro Lopes
- Department of Diagnosis and Surgery, São José Dos Campos School of Dentistry, São Paulo State University (UNESP), São José dos Campos, SP, Brazil
| | - Simone Appenzeller
- Rheumatology Department, Faculty of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | | | - Andre Luiz Ferreira Costa
- Postgraduate Program in Dentistry, Cruzeiro Do Sul University (UNICSUL), São Paulo, 01506-000, Brazil.
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Choi BK, Park S, Lee G, Chang D, Jeon S, Choi J. Can CT texture analysis parameters be used as imaging biomarkers for prediction of malignancy in canine splenic tumors? Vet Radiol Ultrasound 2023; 64:224-232. [PMID: 36285434 DOI: 10.1111/vru.13175] [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: 02/02/2022] [Revised: 08/16/2022] [Accepted: 08/16/2022] [Indexed: 11/29/2022] Open
Abstract
Splenic hemangiosarcoma has morphological similarities to benign nodular hyperplasia. Computed tomography (CT) texture analysis can analyze the texture of images that the naive human eye cannot detect. Recently, there have been attempts to incorporate CT texture analysis with artificial intelligence in human medicine. This retrospective, analytical design study aimed to assess the feasibility of CT texture analysis in splenic masses and investigate predictive biomarkers of splenic hemangiosarcoma in dogs. Parameters for dogs with hemangiosarcoma and nodular hyperplasia were compared, and an independent parameter that could differentiate between them was selected. Discriminant analysis was performed to assess the ability to discriminate the two splenic masses and compare the relative importance of the parameters. A total of 23 dogs were sampled, including 16 splenic nodular hyperplasia and seven hemangiosarcoma. In each dog, total 38 radiomic parameters were extracted from first-, second-, and higher-order matrices. Thirteen parameters had significant differences between hemangiosarcoma and nodular hyperplasia. Skewness in the first-order matrix and GLRLM_LGRE and GLZLM_ZLNU in the second, higher-order matrix were determined as independent parameters. A discriminant equation consisting of skewness, GLZLM_LGZE, and GLZLM_ZLNU was derived, and the cross-validation verification result showed an accuracy of 95.7%. Skewness was the most influential parameter for the discrimination of the two masses. The study results supported using CT texture analysis to help differentiate hemangiosarcoma from nodular hyperplasia in dogs. This new diagnostic approach can be used for developing future machine learning-based texture analysis tools.
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Affiliation(s)
- Bo-Kwon Choi
- College of Veterinary Medicine, Chonnam National University, Gwangju, Republic of Korea
| | - Seungjo Park
- College of Veterinary Medicine, Chonnam National University, Gwangju, Republic of Korea
| | - Gahyun Lee
- Haemaru Referral Animal Hospital, Seongnam, Republic of Korea
| | - Dongwoo Chang
- College of Veterinary Medicine, Chungbuk National University, Cheongju, Republic of Korea
| | - Sunghoon Jeon
- Haemaru Referral Animal Hospital, Seongnam, Republic of Korea
| | - Jihye Choi
- Department of Veterinary Medical Imaging, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, Republic of Korea
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Bicci E, Cozzi D, Cavigli E, Ruzga R, Bertelli E, Danti G, Bettarini S, Tortoli P, Mazzoni LN, Busoni S, Miele V. Reproducibility of CT radiomic features in lung neuroendocrine tumours (NETs) patients: analysis in a heterogeneous population. LA RADIOLOGIA MEDICA 2023; 128:203-211. [PMID: 36637739 PMCID: PMC9938819 DOI: 10.1007/s11547-023-01592-y] [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: 09/30/2022] [Accepted: 01/04/2023] [Indexed: 01/14/2023]
Abstract
BACKGROUND The aim is to find a correlation between texture features extracted from neuroendocrine (NET) lung cancer subtypes, both Ki-67 index and the presence of lymph-nodal mediastinal metastases detected while using different computer tomography (CT) scanners. METHODS Sixty patients with a confirmed pulmonary NET histological diagnosis, a known Ki-67 status and metastases, were included. After subdivision of primary lesions in baseline acquisition and venous phase, 107 radiomic features of first and higher orders were extracted. Spearman's correlation matrix with Ward's hierarchical clustering was applied to confirm the absence of bias due to the database heterogeneity. Nonparametric tests were conducted to identify statistically significant features in the distinction between patient groups (Ki-67 < 3-Group 1; 3 ≤ Ki-67 ≤ 20-Group 2; and Ki-67 > 20-Group 3, and presence of metastases). RESULTS No bias arising from sample heterogeneity was found. Regarding Ki-67 groups statistical tests, seven statistically significant features (p value < 0.05) were found in post-contrast enhanced CT; three in baseline acquisitions. In metastasis classes distinction, three features (first-order class) were statistically significant in post-contrast acquisitions and 15 features (second-order class) in baseline acquisitions, including the three features distinguishing between Ki-67 groups in baseline images (MCC, ClusterProminence and Strength). CONCLUSIONS Some radiomic features can be used as a valid and reproducible tool for predicting Ki-67 class and hence the subtype of lung NET in baseline and post-contrast enhanced CT images. In particular, in baseline examination three features can establish both tumour class and aggressiveness.
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Affiliation(s)
- Eleonora Bicci
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Diletta Cozzi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Edoardo Cavigli
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Ron Ruzga
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Elena Bertelli
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Ginevra Danti
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Silvia Bettarini
- Department of Health Physics, L.Go Brambilla, Careggi University Hospital, 50134 Florence, Italy
| | - Paolo Tortoli
- Department of Health Physics, L.Go Brambilla, Careggi University Hospital, 50134 Florence, Italy
| | - Lorenzo Nicola Mazzoni
- Department of Health Physics, AUSL Toscana Centro, Via Ciliegiole 97, 51100 Pistoia, Italy
| | - Simone Busoni
- Department of Health Physics, L.Go Brambilla, Careggi University Hospital, 50134 Florence, Italy
| | - Vittorio Miele
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
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Costa ALF, Fardim KAC, Ribeiro IT, Jardini MAN, Braz-Silva PH, Orhan K, de Castro Lopes SLP. Cone-beam computed tomography texture analysis can help differentiate odontogenic and non-odontogenic maxillary sinusitis. Imaging Sci Dent 2023; 53:43-51. [PMID: 37006790 PMCID: PMC10060763 DOI: 10.5624/isd.20220166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 12/02/2022] [Accepted: 12/14/2022] [Indexed: 01/12/2023] Open
Abstract
Purpose This study aimed to assess texture analysis (TA) of cone-beam computed tomography (CBCT) images as a quantitative tool for the differential diagnosis of odontogenic and non-odontogenic maxillary sinusitis (OS and NOS, respectively). Materials and Methods CBCT images of 40 patients diagnosed with OS (N=20) and NOS (N=20) were evaluated. The gray level co-occurrence (GLCM) matrix parameters, and gray level run length matrix texture (GLRLM) parameters were extracted using manually placed regions of interest on lesion images. Seven texture parameters were calculated using GLCM and 4 parameters using GLRLM. The Mann-Whitney test was used for comparisons between the groups, and the Levene test was performed to confirm the homogeneity of variance (α=5%). Results The results showed statistically significant differences (P<0.05) between the OS and NOS patients regarding 3 TA parameters. NOS patients presented higher values for contrast, while OS patients presented higher values for correlation and inverse difference moment. Greater textural homogeneity was observed in the OS patients than in the NOS patients, with statistically significant differences in standard deviations between the groups for correlation, sum of squares, sum of entropy, and entropy. Conclusion TA enabled quantitative differentiation between OS and NOS on CBCT images by using the parameters of contrast, correlation, and inverse difference moment.
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Affiliation(s)
| | - Karolina Aparecida Castilho Fardim
- Department of Diagnosis and Surgery, São José dos Campos School of Dentistry of the São Paulo State University, São José dos Campos, SP, Brazil
| | - Isabela Teixeira Ribeiro
- Department of Diagnosis and Surgery, São José dos Campos School of Dentistry of the São Paulo State University, São José dos Campos, SP, Brazil
| | - Maria Aparecida Neves Jardini
- Department of Diagnosis and Surgery, São José dos Campos School of Dentistry of the São Paulo State University, São José dos Campos, SP, Brazil
| | - Paulo Henrique Braz-Silva
- Division of General Pathology, School of Dentistry, University of São Paulo, São Paulo, SP, Brazil
- Laboratory of Virology, Institute of Tropical Medicine of São Paulo, School of Medicine, University of São Paulo, São Paulo, SP, Brazil
| | - Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
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12
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Nussi AD, de Castro Lopes SLP, De Rosa CS, Gomes JPP, Ogawa CM, Braz-Silva PH, Costa ALF. In vivo study of cone beam computed tomography texture analysis of mandibular condyle and its correlation with gender and age. Oral Radiol 2023; 39:191-197. [PMID: 35585223 DOI: 10.1007/s11282-022-00620-3] [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: 03/19/2022] [Accepted: 04/24/2022] [Indexed: 01/07/2023]
Abstract
OBJECTIVE Texture analysis is an image processing method that aims to assess the distribution of gray-level intensity and spatial organization of the pixels in the image. The purpose of this study was to investigate whether the texture analysis applied to cone beam computed tomography (CBCT) images could detect variation in the condyle trabecular bone of individuals from different age groups and genders. METHODS The sample consisted of imaging exams from 63 individuals divided into three groups according to age groups of 03-13, 14-24 and 25-34. For texture analysis, the MaZda® software was used to extract the following parameters: second angular momentum, contrast, correlation, sum of squares, inverse difference moment, sum entropy and entropy. Statistical analysis was performed using Mann-Whitney test for gender and Kruskal-Wallis test for age (P = 5%). RESULTS No statistically significant differences were found between age groups for any of the parameters. Males had lower values for the parameter correlation than those of females (P < 0.05). CONCLUSION Texture analysis proved to be useful to discriminate mandibular condyle trabecular bone between genders.
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Affiliation(s)
- Amanda Drumstas Nussi
- Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), Rua Galvão Bueno, 868, Liberdade, São Paulo, SP, 01506-000, Brazil
| | - Sérgio Lucio Pereira de Castro Lopes
- Department of Diagnosis and Surgery, Science and Technology Institute, São Paulo State University (UNESP), São José dos Campos, São Paulo, Brazil
| | - Catharina Simioni De Rosa
- Division of General Pathology, Department of Stomatology, School of Dentistry, University of São Paulo (USP), São Paulo, SP, Brazil
| | - João Pedro Perez Gomes
- Division of General Pathology, Department of Stomatology, School of Dentistry, University of São Paulo (USP), São Paulo, SP, Brazil
| | - Celso Massahiro Ogawa
- Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), Rua Galvão Bueno, 868, Liberdade, São Paulo, SP, 01506-000, Brazil
| | - Paulo Henrique Braz-Silva
- Division of General Pathology, Department of Stomatology, School of Dentistry, University of São Paulo (USP), São Paulo, SP, Brazil
- Laboratory of Virology, Institute of Tropical Medicine of São Paulo, School of Medicine, University of São Paulo, São Paulo, SP, Brazil
| | - Andre Luiz Ferreira Costa
- Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), Rua Galvão Bueno, 868, Liberdade, São Paulo, SP, 01506-000, Brazil.
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Shah D, Gehani A, Mahajan A, Chakrabarty N. Advanced Techniques in Head and Neck Cancer Imaging: Guide to Precision Cancer Management. Crit Rev Oncog 2023; 28:45-62. [PMID: 37830215 DOI: 10.1615/critrevoncog.2023047799] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Precision treatment requires precision imaging. With the advent of various advanced techniques in head and neck cancer treatment, imaging has become an integral part of the multidisciplinary approach to head and neck cancer care from diagnosis to staging and also plays a vital role in response evaluation in various tumors. Conventional anatomic imaging (CT scan, MRI, ultrasound) remains basic and focuses on defining the anatomical extent of the disease and its spread. Accurate assessment of the biological behavior of tumors, including tumor cellularity, growth, and response evaluation, is evolving with recent advances in molecular, functional, and hybrid/multiplex imaging. Integration of these various advanced diagnostic imaging and nonimaging methods aids understanding of cancer pathophysiology and provides a more comprehensive evaluation in this era of precision treatment. Here we discuss the current status of various advanced imaging techniques and their applications in head and neck cancer imaging.
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Affiliation(s)
- Diva Shah
- Senior Consultant Radiologist, Department of Radiodiagnosis, HCG Cancer Centre, Ahmedabad, 380060, Gujarat, India
| | - Anisha Gehani
- Department of Radiology and Imaging Sciences, Tata Medical Centre, New Town, WB 700160, India
| | - Abhishek Mahajan
- Department of Radiology, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, L7 8YA, United Kingdom
| | - Nivedita Chakrabarty
- Department of Radiodiagnosis, Tata Memorial Hospital, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), 400012, Mumbai, India
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Fujima N, Shimizu Y, Yoneyama M, Nakagawa J, Kameda H, Harada T, Hamada S, Suzuki T, Tsushima N, Kano S, Homma A, Kudo K. Amide proton transfer imaging for the determination of human papillomavirus status in patients with oropharyngeal squamous cell carcinoma. Medicine (Baltimore) 2022; 101:e29457. [PMID: 35839055 PMCID: PMC11132395 DOI: 10.1097/md.0000000000029457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/22/2022] [Indexed: 10/14/2022] Open
Abstract
The aim of this study was to investigate the utility of amide proton transfer (APT) imaging for the determination of human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (SCC). Thirty-one patients with oropharyngeal SCC were retrospectively evaluated. All patients underwent amide proton transfer imaging using a 3T magnetic resonance (MR) unit. Patients were divided into HPV-positive and -negative groups depending on the pathological findings in their primary tumor. In APT imaging, the primary tumor was delineated with a polygonal region of interest (ROI). Signal information in the ROI was used to calculate the mean, standard deviation (SD) and coefficient of variant (CV) of the APT signals (APT mean, APT SD, and APT CV, respectively). The value of APT CV in the HPV-positive group (0.43 ± 0.04) was significantly lower than that in the HPV-negative group (0.48 ± 0.04) (P = .01). There was no significant difference in APT mean (P = .82) or APT SD (P = .13) between the HPV-positive and -negative groups. Receiver operating characteristic (ROC) curve analysis of APT CV had a sensitivity of 0.75, specificity of 0.8, positive predictive value of 0.75, negative predictive value of 0.8, accuracy of 0.77 and area under the curve (AUC) of 0.8. The APT signal in the HPV-negative group was considered heterogeneous compared to the HPV-positive group. This information might be useful for the determination of HPV status in patients with oropharyngeal SCC.
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Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Yukie Shimizu
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
- Department of Advanced Diagnostic Imaging Development, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | | | - Junichi Nakagawa
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Hiroyuki Kameda
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Taisuke Harada
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Seijiro Hamada
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Takayoshi Suzuki
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Nayuta Tsushima
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Satoshi Kano
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Akihiro Homma
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
- Department of Advanced Diagnostic Imaging Development, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
- The Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, Sapporo, Japan
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Role of Texture Analysis in Oropharyngeal Carcinoma: A Systematic Review of the Literature. Cancers (Basel) 2022; 14:cancers14102445. [PMID: 35626048 PMCID: PMC9139172 DOI: 10.3390/cancers14102445] [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: 03/29/2022] [Revised: 05/02/2022] [Accepted: 05/10/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary The incidence of squamous cell carcinomas of the oropharynx has rapidly increased in the last two decades due to human papilloma virus infection (HPV). HPV-positive and HPV-negative squamous cell tumours differ in radiological imaging, treatment, and prognosis; therefore, differential diagnosis is mandatory. Radiomics with texture analysis is an innovative technique that has been used increasingly in recent years to characterise the tissue heterogeneity of certain structures such as neoplasms or organs by measuring the spatial distribution of pixel values on radiological imaging. This review delineates the application of texture analysis in oropharyngeal tumours and explores how radiomics may potentially improve clinical decision-making. Abstract Human papilloma virus infection (HPV) is associated with the development of lingual and palatine tonsil carcinomas. Diagnosing, differentiating HPV-positive from HPV-negative cancers, and assessing the presence of lymph node metastases or recurrences by the visual interpretation of images is not easy. Texture analysis can provide structural information not perceptible to human eyes. A systematic literature search was performed on 16 February 2022 for studies with a focus on texture analysis in oropharyngeal cancers. We conducted the research on PubMed, Scopus, and Web of Science platforms. Studies were screened for inclusion according to the preferred reporting items for systematic reviews. Twenty-six studies were included in our review. Nineteen articles related specifically to the oropharynx and seven articles analysed the head and neck area with sections dedicated to the oropharynx. Six, thirteen, and seven articles used MRI, CT, and PET, respectively, as the imaging techniques by which texture analysis was performed. Regarding oropharyngeal tumours, this review delineates the applications of texture analysis in (1) the diagnosis, prognosis, and assessment of disease recurrence or persistence after therapy, (2) early differentiation of HPV-positive versus HPV-negative cancers, (3) the detection of cancers not visualised by imaging alone, and (4) the assessment of lymph node metastases from unknown primary carcinomas.
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The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment. Neuroradiology 2022; 64:1639-1647. [PMID: 35459957 PMCID: PMC9271107 DOI: 10.1007/s00234-022-02959-0] [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: 12/08/2021] [Accepted: 04/07/2022] [Indexed: 11/19/2022]
Abstract
Purpose
Human papillomavirus (HPV) status assessment is crucial for decision making in oropharyngeal cancer patients. In last years, several articles have been published investigating the possible role of radiomics in distinguishing HPV-positive from HPV-negative neoplasms. Aim of this review was to perform a systematic quality assessment of radiomic studies published on this topic. Methods Radiomics studies on HPV status prediction in oropharyngeal cancer patients were selected. The Radiomic Quality Score (RQS) was assessed by three readers to evaluate their methodological quality. In addition, possible correlations between RQS% and journal type, year of publication, impact factor, and journal rank were investigated. Results After the literature search, 19 articles were selected whose RQS median was 33% (range 0–42%). Overall, 16/19 studies included a well-documented imaging protocol, 13/19 demonstrated phenotypic differences, and all were compared with the current gold standard. No study included a public protocol, phantom study, or imaging at multiple time points. More than half (13/19) included feature selection and only 2 were comprehensive of non-radiomic features. Mean RQS was significantly higher in clinical journals. Conclusion Radiomics has been proposed for oropharyngeal cancer HPV status assessment, with promising results. However, these are supported by low methodological quality investigations. Further studies with higher methodological quality, appropriate standardization, and greater attention to validation are necessary prior to clinical adoption. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-022-02959-0.
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Wang H, Zhou Y, Wang X, Zhang Y, Ma C, Liu B, Kong Q, Yue N, Xu Z, Nie K. Reproducibility and Repeatability of CBCT-Derived Radiomics Features. Front Oncol 2021; 11:773512. [PMID: 34869015 PMCID: PMC8637922 DOI: 10.3389/fonc.2021.773512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/27/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE This study was conducted in order to determine the reproducibility and repeatability of cone-beam computed tomography (CBCT) radiomics features. METHODS The first-, second-, and fifth-day CBCT images from 10 head and neck (H&N) cancer patients and 10 pelvic cancer patients were retrospectively collected for this study. Eighteen common radiomics features were extracted from the longitudinal CBCT images using two radiomics packages. The reproducibility of CBCT-derived radiomics features was assessed using the first-day image as input and compared across the two software packages. The site-specific intraclass correlation coefficient (ICC) was used to quantitatively assess the agreement between packages. The repeatability of CBCT-based radiomics features was evaluated by comparing the following days of CBCT to the first-day image and quantified using site-specific concordance correlation coefficient (CCC). Furthermore, the correlation with volume for all the features was assessed with linear regression and R 2 as correlation parameters. RESULTS The first-order histogram-based features such as skewness and entropy showed good agreement computed in either software package (ICCs ≥ 0.80), while the kurtosis measurements were consistent in H&N patients between the two software tools but not in pelvic cases. The ICCs for GLCM-based features showed good agreement (ICCs ≥ 0.80) between packages in both H&N and pelvic groups except for the GLCM-correction. The GLRLM-based texture features were overall less consistent as calculated by the two different software packages compared with the GLCM-based features. The CCC values of all first-order and second-order GLCM features (except GLCM-energy) were all above 0.80 from the 2-day part test-retest set, while the CCC values all dropped below the cutoff after 5-day treatment scans. All first-order histogram-based and GLCM-texture-based features were not highly correlated with volume, while two GLRLM features, in both H&N and pelvic cohorts, showed R 2 ≥0.8, meaning a high correlation with volume. CONCLUSION The reproducibility and repeatability of CBCT-based radiomics features were assessed and compared for the first time on both H&N and pelvic sites. There were overlaps of stable features in both disease sites, yet the overall stability of radiomics features may be disease-/protocol-specific and a function of time between scans.
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Affiliation(s)
- Hao Wang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
- Institute of Modern Physics, Fudan University, Shanghai, China
| | - Yongkang Zhou
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Yin Zhang
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Chi Ma
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Bo Liu
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Qing Kong
- Institute of Modern Physics, Fudan University, Shanghai, China
| | - Ning Yue
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Zhiyong Xu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Ke Nie
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
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Bagher Ebadian H, Siddiqui F, Ghanem A, Zhu S, Lu M, Movsas B, Chetty IJ. Radiomics outperforms clinical factors in characterizing human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinomas. Biomed Phys Eng Express 2021; 8. [PMID: 34781281 DOI: 10.1088/2057-1976/ac39ab] [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: 08/05/2021] [Accepted: 11/15/2021] [Indexed: 11/11/2022]
Abstract
Purpose:To utilize radiomic features extracted from CT images to characterize Human Papilloma Virus (HPV) for patients with oropharyngeal cancer squamous cell carcinoma (OPSCC).Methods:One hundred twenty-eight OPSCC patients with known HPV-status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16-protein testing) were retrospectively studied. Radiomic features (11 feature-categories) were extracted in 3D from contrast-enhanced (CE)-CT images of gross-tumor-volumes using 'in-house' software ('ROdiomiX') developed and validated following the image-biomarker-standardization-initiative (IBSI) guidelines. Six clinical factors were investigated: Age-at-Diagnosis, Gender, Total-Charlson, Alcohol-Use, Smoking-History, and T-Stage. A Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) technique combined with a Generalized-Linear-Model (Lasso-GLM) were applied to perform regularization in the radiomic and clinical feature spaces to identify the ranking of optimal feature subsets with most representative information for prediction of HPV. Lasso-GLM models/classifiers based on clinical factors only, radiomics only, and combined clinical and radiomics (ensemble/integrated) were constructed using random-permutation-sampling. Tests of significance (One-way ANOVA), average Area-Under-Receiver-Operating-Characteristic (AUC), and Positive and Negative Predictive values (PPV and NPV) were computed to estimate the generalization-error and prediction performance of the classifiers.Results:Five clinical factors, including T-stage, smoking status, and age, and 14 radiomic features, including tumor morphology, and intensity contrast were found to be statistically significant discriminators between HPV positive and negative cohorts. Performances for prediction of HPV for the 3 classifiers were: Radiomics-Lasso-GLM: AUC/PPV/NPV=0.789/0.755/0.805; Clinical-Lasso-GLM: 0.676/0.747/0.672, and Integrated/Ensemble-Lasso-GLM: 0.895/0.874/0.844. Results imply that the radiomics-based classifier enabled better characterization and performance prediction of HPV relative to clinical factors, and that the combination of both radiomics and clinical factors yields even higher accuracy characterization and predictive performance.Conclusion:Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results in support of the role of radiomic features towards characterization of HPV in patients with OPSCC.
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Affiliation(s)
- Hassan Bagher Ebadian
- Department of Radiation Oncology , Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Detroit, Michigan, 48202, UNITED STATES
| | - Farzan Siddiqui
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Ahmed Ghanem
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Simeng Zhu
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Mei Lu
- Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Benjamin Movsas
- Dept of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, 48202, UNITED STATES
| | - Indrin J Chetty
- Dept of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI 48202-2689, USA, Detroit, Michigan, 48202, UNITED STATES
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Zhang T, Dong X, Zhou Y, Liu M, Hang J, Wu L. Development and validation of a radiomics nomogram to discriminate advanced pancreatic cancer with liver metastases or other metastatic patterns. Cancer Biomark 2021; 32:541-550. [PMID: 34334383 DOI: 10.3233/cbm-210190] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Patients with advanced pancreatic cancer (APC) and liver metastases have much poorer prognoses than patients with other metastatic patterns. OBJECTIVE This study aimed to develop and validate a radiomics model to discriminate patients with pancreatic cancer and liver metastases from those with other metastatic patterns. METHODS We evaluated 77 patients who had APC and performed texture analysis on the region of interest. 58 patients and 19 patients were allocated randomly into the training and validation cohorts with almost the same proportion of liver metastases. An independentsamples t-test was used for feature selection in the training cohort. Random forest classifier was used to construct models based on these features and a radiomics signature (RS) was derived. A nomogram was constructed based on RS and CA19-9, and was validated with calibration plot and decision curve. The prognostic value of RS was evaluated by Kaplan-Meier methods. RESULTS The constructed nomogram demonstrated good discrimination in the training (AUC = 0.93) and validation (AUC = 0.81) cohorts. In both cohorts, patients with RS > 0.61 had much poorer overall survival than patients with RS < 0.61. CONCLUSIONS This study presents a radiomics nomogram incorporating RS and CA19-9 to discriminate patients who have APC with liver metastases from patients with other metastatic patterns.
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Affiliation(s)
- Tianliang Zhang
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Xiao Dong
- Department of Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Yang Zhou
- Changzhou No. 2 People's Hospital, Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
| | - Muhan Liu
- Changzhou No. 2 People's Hospital, Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
| | - Junjie Hang
- Changzhou No. 2 People's Hospital, Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
| | - Lixia Wu
- Department of Oncology, Shanghai JingAn District ZhaBei Central Hospital, Shanghai, China
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Bruixola G, Remacha E, Jiménez-Pastor A, Dualde D, Viala A, Montón JV, Ibarrola-Villava M, Alberich-Bayarri Á, Cervantes A. Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges. Cancer Treat Rev 2021; 99:102263. [PMID: 34343892 DOI: 10.1016/j.ctrv.2021.102263] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 07/06/2021] [Accepted: 07/23/2021] [Indexed: 12/12/2022]
Abstract
The application of imaging biomarkers in oncology is still in its infancy, but with the expansion of radiomics and radiogenomics a revolution is expected in this field. This may be of special interest in head and neck cancer, since it can promote precision medicine and personalization of treatment by overcoming several intrinsic obstacles in this pathology. Our goal is to provide the medical oncologist with the basis to approach these disciplines and appreciate their main uses in clinical research and clinical practice in the medium term. Aligned with this objective we analyzed the most relevant studies in the field, also highlighting novel opportunities and current challenges.
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Affiliation(s)
- Gema Bruixola
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Elena Remacha
- Quantitative Imaging Biomarkers in Medicine (QUIBIM SL), Valencia, Spain
| | - Ana Jiménez-Pastor
- Quantitative Imaging Biomarkers in Medicine (QUIBIM SL), Valencia, Spain
| | - Delfina Dualde
- Department of Radiology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Alba Viala
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Jose Vicente Montón
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Maider Ibarrola-Villava
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain; CIBERONC, Instituto de Salud Carlos III, Madrid, Spain
| | | | - Andrés Cervantes
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain; CIBERONC, Instituto de Salud Carlos III, Madrid, Spain.
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21
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Ahn Y, Choi YJ, Sung YS, Pfeuffer J, Suh CH, Chung SR, Baek JH, Lee JH. Histogram analysis of arterial spin labeling perfusion data to determine the human papillomavirus status of oropharyngeal squamous cell carcinomas. Neuroradiology 2021; 63:1345-1352. [PMID: 34185105 DOI: 10.1007/s00234-021-02751-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 06/09/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE To evaluate the correlation between histogram parameters derived from pseudo-continuous arterial spin labeling (PCASL) and human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). METHODS This study included a total of 58 patients (HPV-positive: n = 45; -negative: n = 13) from a prospective cohort of consecutive patients aged ≥ 18 years, who were newly diagnosed with oropharyngeal squamous cell carcinoma. All patients were required to have undergone pre-treatment MRI with PCASL to measure regional perfusion. The region of interest was drawn by two radiologists, encompassing the entire tumor volume on all corresponding slices. Differences in the histogram parameters derived from tumor blood flow (TBF) in ASL were assessed for HPV-positive and -negative patients. Receiver operating characteristic curve analysis was performed to determine the best differentiating parameters, and a leave-one-out cross-validation was used. RESULTS Patients with HPV-positive OPSCC showed a significantly lower overall standard deviation and 95th percentile value of tumor blood flow (P < .007). The standard deviation of TBF was the single best predictive parameter. Leave-one-out cross-validation tests revealed that the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.745, 75.9%, 75.6%, and 76.9%, respectively. CONCLUSION PCASL revealed differences in perfusion parameters according to HPV status in patients with OPSCC, reflecting their distinct histopathology.
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Affiliation(s)
- Yura Ahn
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Young Jun Choi
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea.
| | - Yu Sub Sung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Josef Pfeuffer
- Siemens Healthcare, MR Application Development, Erlangen, Germany
| | - Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Sae Rom Chung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Jung Hwan Baek
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Jeong Hyun Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
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22
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Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma. Sci Rep 2021; 11:4679. [PMID: 33633160 PMCID: PMC7907098 DOI: 10.1038/s41598-021-84048-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 02/11/2021] [Indexed: 01/06/2023] Open
Abstract
The purpose of this study was to examine differences in texture features between olfactory neuroblastoma (ONB) and sinonasal squamous cell carcinoma (SCC) on contrast-enhanced CT (CECT) images, and to evaluate the predictive accuracy of texture analysis compared to radiologists’ interpretations. Forty-three patients with pathologically-diagnosed primary nasal and paranasal tumor (17 ONB and 26 SCC) were included. We extracted 42 texture features from tumor regions on CECT images obtained before treatment. In univariate analysis, each texture features were compared, with adjustment for multiple comparisons. In multivariate analysis, the elastic net was used to select useful texture features and to construct a texture-based prediction model with leave-one-out cross-validation. The prediction accuracy was compared with two radiologists’ visual interpretations. In univariate analysis, significant differences were observed for 28 of 42 texture features between ONB and SCC, with areas under the receiver operating characteristic curve between 0.68 and 0.91 (median: 0.80). In multivariate analysis, the elastic net model selected 18 texture features that contributed to differentiation. It tended to show slightly higher predictive accuracy than radiologists’ interpretations (86% and 74%, respectively; P = 0.096). In conclusion, several texture features contributed to differentiation of ONB from SCC, and the texture-based prediction model was considered useful.
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23
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Hang J, Xu K, Yin R, Shao Y, Liu M, Shi H, Wang X, Wu L. Role of CT texture features for predicting outcome of pancreatic cancer patients with liver metastases. J Cancer 2021; 12:2351-2358. [PMID: 33758611 DOI: 10.7150/jca.49569] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 01/13/2021] [Indexed: 12/13/2022] Open
Abstract
Objective: The purpose of this study was to evaluate the prognostic value of computed tomography (CT) texture features of pancreatic cancer with liver metastases. Methods: We included 39 patients with metastatic pancreatic cancer (MPC) with liver metastases and performed texture analysis on primary tumors and metastases. The correlations between texture parameters were assessed using Pearson's correlation. Univariate Cox proportional hazards model was used to assess the correlations between clinicopathological characteristics, texture features and overall survival (OS). The univariate Cox regression model revealed four texture features potentially correlated with OS (P<0.1). A radiomics score (RS) was determined using a sequential combination of four texture features with potential prognostic value that were weighted according to their β-coefficients. Furthermore, all variables with P<0.1 were included in the multivariate analysis. A nomogram,which was developed to predict OS according to independent prognostic factors, was internally validated using the C-index and calibration plots. Kaplan-Meier analysis and the log-rank test were performed to stratify OS according to the RS and nomogram total points (NTP). Results: Few significant correlations were found between texture features of primary tumors and those of liver metastases. However, texture features within primary tumors or liver metastases were significantly associated. Multivariate analysis showed that Eastern Cooperative Oncology Group performance status (ECOG PS), chemotherapy, Carbohydrate antigen 19-9 (CA19-9), and the RS were independent prognostic factors (P<0.05). The nomogram incorporating these factors showed good discriminative ability (C-index = 0.754). RS and NTP stratified patients into two potential risk groups (P<0.01). Conclusion: The RS derived from significant texture features of primary tumors and metastases shows promise as a prognostic biomarker of OS of patients with MPC. A nomogram based on the RS and other independent prognostic clinicopathological factors accurately predicts OS.
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Affiliation(s)
- Junjie Hang
- Department of Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Xinglong Road 19, Changzhou 213000, China
| | - Kequn Xu
- Department of Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Xinglong Road 19, Changzhou 213000, China
| | - Ruohan Yin
- Department of Medical Imaging, Changzhou No.2 People's Hospital, Nanjing Medical University, Xinglong Road 19, Changzhou 213000, China
| | - Yueting Shao
- Department of Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Xinglong Road 19, Changzhou 213000, China
| | - Muhan Liu
- Department of Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Xinglong Road 19, Changzhou 213000, China
| | - Haifeng Shi
- Department of Medical Imaging, Changzhou No.2 People's Hospital, Nanjing Medical University, Xinglong Road 19, Changzhou 213000, China
| | - Xiaoyong Wang
- Department of Gastroenterology, Changzhou No.2 People's Hospital, Nanjing Medical University, Xinglong Road 19, Changzhou 213000, China
| | - Lixia Wu
- Department of Oncology, Shanghai JingAn District ZhaBei Central Hospital, Zhonghuaxin Road 619, Shanghai 200040, China
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24
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Nardi C, Tomei M, Pietragalla M, Calistri L, Landini N, Bonomo P, Mannelli G, Mungai F, Bonasera L, Colagrande S. Texture analysis in the characterization of parotid salivary gland lesions: A study on MR diffusion weighted imaging. Eur J Radiol 2021; 136:109529. [PMID: 33453571 DOI: 10.1016/j.ejrad.2021.109529] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 11/02/2020] [Accepted: 01/05/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE Parotid lesions show overlaps of morphological findings, apparent diffusion coefficient (ADC) values and types of time/intensity curve. This research aimed to evaluate the role of diffusion weighted imaging texture analysis in differentiating between benign and malignant parotid lesions and in characterizing pleomorphic adenoma (PA), Warthin tumor (WT), epithelial malignancy (EM), and lymphoma (LY). METHODS Texture analysis of 54 parotid lesions (19 PA, 14 WT, 14 EM, and 7 LY) was performed on ADC map images. An ANOVA test was used to estimate both the difference between benign and malignant lesions and the texture feature differences among PA, WT, EM, and LY. A P-value≤0.01 was considered to be statistically significant. A cut-off value defined by ROC curve analysis was found for each statistically significant texture parameter. The diagnostic accuracy was obtained for each texture parameter with AUC ≥ 0.5. The agreement between each texture parameter and histology was calculated using the Cohen's kappa coefficient. RESULTS The mean kappa values were 0.61, 0.34, 0.26, 0.17, and 0.48 for LY, EM, WT, PA, and benign vs. malignant lesions respectively. Long zone emphasis cut-off values >1.870 indicated EM with an accuracy of 81 % and values >2.630 revealed LY with an accuracy of 93 %. Long run emphasis values >1.050 and >1.070 indicated EM and LY with a diagnostic accuracy of 79% and 93% respectively. CONCLUSIONS Long zone emphasis and long run emphasis texture parameters allowed the identification of LY and the differentiation between benign and malignant lesions. WT and PA were not accurately recognized.
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Affiliation(s)
- Cosimo Nardi
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Maddalena Tomei
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Michele Pietragalla
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Linda Calistri
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Nicholas Landini
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy; Department of Radiology, Ca' Foncello General Hospital.Piazzale Ospedale 1, 31100, Treviso, Italy.
| | - Pierluigi Bonomo
- Radiation Oncology, University of Florence - Azienda Ospedaliero-Universitaria Careggi. Largo Brambilla 3, 50134, Florence, Italy.
| | - Giuditta Mannelli
- Department of Experimental and Clinical Medicine, Head and Neck Oncology and Robotic Surgery, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Palagi 1, 50134, Florence, Italy.
| | - Francesco Mungai
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Luigi Bonasera
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Stefano Colagrande
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
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25
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Suh CH, Lee KH, Choi YJ, Chung SR, Baek JH, Lee JH, Yun J, Ham S, Kim N. Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status. Sci Rep 2020; 10:17525. [PMID: 33067484 PMCID: PMC7568530 DOI: 10.1038/s41598-020-74479-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 08/23/2020] [Indexed: 02/06/2023] Open
Abstract
We investigated the ability of machine-learning classifiers on radiomics from pre-treatment multiparametric magnetic resonance imaging (MRI) to accurately predict human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). This retrospective study collected data of 60 patients (48 HPV-positive and 12 HPV-negative) with newly diagnosed histopathologically proved OPSCC, who underwent head and neck MRIs consisting of axial T1WI, T2WI, CE-T1WI, and apparent diffusion coefficient (ADC) maps from diffusion-weighted imaging (DWI). The median age was 59 years (the range being 35 to 85 years), and 83.3% of patients were male. The imaging data were randomised into a training set (32 HPV-positive and 8 HPV-negative OPSCC) and a test set (16 HPV-positive and 4 HPV-negative OPSCC) in each fold. 1618 quantitative features were extracted from manually delineated regions-of-interest of primary tumour and one definite lymph node in each sequence. After feature selection by using the least absolute shrinkage and selection operator (LASSO), three different machine-learning classifiers (logistic regression, random forest, and XG boost) were trained and compared in the setting of various combinations between four sequences. The highest diagnostic accuracies were achieved when using all sequences, and the difference was significant only when the combination did not include the ADC map. Using all sequences, logistic regression and the random forest classifier yielded higher accuracy compared with the that of the XG boost classifier, with mean area under curve (AUC) values of 0.77, 0.76, and 0.71, respectively. The machine-learning classifier of non-invasive and quantitative radiomics signature could guide the classification of the HPV status.
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Affiliation(s)
- Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Kyung Hwa Lee
- Department of Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.,Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Young Jun Choi
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea.
| | - Sae Rom Chung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Jung Hwan Baek
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Jeong Hyun Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Jihye Yun
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Sungwon Ham
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea. .,Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea.
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26
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Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis. Heliyon 2020; 6:e05194. [PMID: 33088959 PMCID: PMC7560585 DOI: 10.1016/j.heliyon.2020.e05194] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/25/2020] [Accepted: 10/05/2020] [Indexed: 12/13/2022] Open
Abstract
Objective This study aimed to investigate the use of texture analysis for characterization of radicular cysts and periapical granulomas and to assess its efficacy to differentiate between both lesions with histological diagnosis. Methods Cone beam computed tomography (CBCT) images were obtained from 19 patients with 25 periapical lesions (14 radicular cysts and 11 periapical granulomas) confirmed by biopsy. Regions of interest were created in the lesions from which 11 texture parameters were calculated. Spearman's correlation analysis was performed and adjusted with Benjamini-Hochberg false discovery rate procedure (FDR <0.005). Results The texture parameters used to differentiate the lesions were assessed by using a receiver operating characteristic analysis. Five texture parameters were predictive of lesion differentiation for eight positions: angular second moment; sum of squares; sum of average; contrast; correlation. Conclusion Texture analysis of CBCT scans distinguishes radicular cysts from periapical granulomas and can be a promising diagnostic tool for periapical lesions. Clinical significance Texture analysis can be used in diagnostic and treatment monitoring to provide supplementary information.
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27
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Machine learning–based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation. Eur Radiol 2020; 30:6858-6866. [DOI: 10.1007/s00330-020-07011-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 04/29/2020] [Accepted: 06/04/2020] [Indexed: 01/11/2023]
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28
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PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma. Eur J Nucl Med Mol Imaging 2020; 47:2978-2991. [DOI: 10.1007/s00259-020-04839-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Accepted: 04/24/2020] [Indexed: 01/02/2023]
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29
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Haider SP, Burtness B, Yarbrough WG, Payabvash S. Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas. CANCERS OF THE HEAD & NECK 2020; 5:6. [PMID: 32391171 PMCID: PMC7197186 DOI: 10.1186/s41199-020-00053-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 03/09/2020] [Indexed: 12/15/2022]
Abstract
Recent advancements in computational power, machine learning, and artificial intelligence technology have enabled automated evaluation of medical images to generate quantitative diagnostic and prognostic biomarkers. Such objective biomarkers are readily available and have the potential to improve personalized treatment, precision medicine, and patient selection for clinical trials. In this article, we explore the merits of the most recent addition to the “-omics” concept for the broader field of head and neck cancer – “Radiomics”. This review discusses radiomics studies focused on (molecular) characterization, classification, prognostication and treatment guidance for head and neck squamous cell carcinomas (HNSCC). We review the underlying hypothesis, general concept and typical workflow of radiomic analysis, and elaborate on current and future challenges to be addressed before routine clinical application.
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Affiliation(s)
- Stefan P Haider
- 1Department of Radiology and Biomedical Imaging, Division of Neuroradiology, Yale School of Medicine, New Haven, CT USA.,2Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians University of Munich, Munich, Germany
| | - Barbara Burtness
- 3Department of Internal Medicine, Division of Medical Oncology, Yale School of Medicine, New Haven, CT USA
| | - Wendell G Yarbrough
- 4Department of Otolaryngology/Head and Neck Surgery, University of North Carolina School of Medicine, Chapel Hill, NC USA
| | - Seyedmehdi Payabvash
- 1Department of Radiology and Biomedical Imaging, Division of Neuroradiology, Yale School of Medicine, New Haven, CT USA
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30
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The role of diffusion-weighted and dynamic contrast enhancement perfusion-weighted imaging in the evaluation of salivary glands neoplasms. Radiol Med 2020; 125:851-863. [PMID: 32266692 DOI: 10.1007/s11547-020-01182-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 03/23/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVES To evaluate the association of magnetic resonance diffusion-weighted imaging (DwI) and dynamic contrast-enhanced perfusion-weighted imaging (DCE-PwI) with a temporal resolution of 5 s, wash-in < 120 s, and wash-out ratio > 30% in the evaluation of salivary glands neoplasms. METHODS DwI and DCE-PwI of 92 salivary glands neoplasms were assessed. The apparent diffusion coefficient (ADC) was calculated by drawing three regions of interest with an average area of 0.30-0.40 cm2 on three contiguous axial sections. The time/intensity curve was generated from DCE-PwI images by drawing a region of interest that included at least 50% of the largest lesion section. Vessels, calcifications, and necrotic/haemorrhagic or cystic areas within solid components were excluded. The association of ADC ≥ 1.4 × 10-3 mm2/s with type A curves (progressive wash-in) and ADC 0.9-1.4 × 10-3 mm2/s with type C curves (rapid wash-in/slow wash-out) were tested as parameters of benignity and malignancy, respectively. Type B curve (rapid wash-in/rapid wash-out) was not used as a reference parameter. RESULTS ADC ≥ 1.4 × 10-3 mm2/s and type A curves were observed only in benign neoplasms. ADC of 0.9-1.4 × 10-3 mm2/s and type C curves association showed specificity of 94.9% and positive predictive value of 81.8% for epithelial malignancies. The association of ADC < 0.9 × 10-3 mm2/s with type B and C curves showed diagnostic accuracy of 94.6% and 100% for Warthin tumour and lymphoma, respectively. CONCLUSIONS ADC ≥ 1.4 × 10-3 mm2/s and type A curves association was indicative of benignity. Lymphomas exhibited ADC < 0.7 × 10-3 mm2/s and type C curves. The association of ADC < 0.9 × 10-3 mm2/s and type B and C curves had accuracy 94.6% and 88.5% for Warthin tumour and epithelial malignancies, respectively.
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31
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Fujima N, Andreu-Arasa VC, Meibom SK, Mercier GA, Truong MT, Sakai O. Prediction of the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma by FDG-PET imaging dataset using deep learning analysis: A hypothesis-generating study. Eur J Radiol 2020; 126:108936. [PMID: 32171912 DOI: 10.1016/j.ejrad.2020.108936] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 02/22/2020] [Accepted: 03/02/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE To assess the diagnostic accuracy of imaging-based deep learning analysis to differentiate between human papillomavirus (HPV) positive and negative oropharyngeal squamous cell carcinomas (OPSCCs) using FDG-PET images. METHODS One hundred and twenty patients with OPSCC who underwent pretreatment FDG-PET/CT were included and divided into the training 90 patients and validation 30 patients cohorts. In the training session, 2160 FDG-PET images were analyzed after data augmentation process by a deep learning technique to create a diagnostic model to discriminate between HPV-positive and HPV-negative OPSCCs. Validation cohort data were subsequently analyzed for confirmation of diagnostic accuracy in determining HPV status by the deep learning-based diagnosis model. In addition, two radiologists evaluated the validation cohort image-data to determine the HPV status based on each tumor's imaging findings. RESULTS In deep learning analysis with training session, the diagnostic model using training dataset was successfully created. In the validation session, the deep learning diagnostic model revealed sensitivity of 0.83, specificity of 0.83, positive predictive value of 0.88, negative predictive value of 0.77, and diagnostic accuracy of 0.83, while the visual assessment by two radiologists revealed 0.78, 0.5, 0.7, 0.6, and 0.67 (reader 1), and 0.56, 0.67, 0.71, 0.5, and 0.6 (reader 2), respectively. Chi square test showed a significant difference between deep learning- and radiologist-based diagnostic accuracy (reader 1: P = 0.016, reader 2: P = 0.008). CONCLUSIONS Deep learning diagnostic model with FDG-PET imaging data can be useful as one of supportive tools to determine the HPV status in patients with OPSCC.
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Affiliation(s)
- Noriyuki Fujima
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States; Research Center for Cooperative Projects, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan
| | - V Carlota Andreu-Arasa
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Sara K Meibom
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Gustavo A Mercier
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Minh Tam Truong
- Department of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Osamu Sakai
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States; Department of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States; Department of Otolaryngology-Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States.
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Gonçalves BC, Araújo EC, Nussi AD, Bechara N, Sarmento D, Oliveira MS, Santamaria MP, Costa ALF, Lopes S. Texture analysis of cone‐beam computed tomography images assists the detection of furcal lesion. J Periodontol 2020; 91:1159-1166. [DOI: 10.1002/jper.19-0477] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 12/12/2019] [Accepted: 12/20/2019] [Indexed: 12/13/2022]
Affiliation(s)
- Bianca C. Gonçalves
- Department of Diagnosis and Surgery São José dos Campos School of Dentistry São Paulo State University (UNESP) São José dos Campos Sao Paulo Sao Paulo Brazil
| | - Elaine C. Araújo
- Department of Diagnosis and Surgery São José dos Campos School of Dentistry São Paulo State University (UNESP) São José dos Campos Sao Paulo Sao Paulo Brazil
| | - Amanda D. Nussi
- Postgraduate Program in Dentistry Cruzeiro do Sul University (UNICSUL) Sao Paulo Sao Paulo Brazil
| | - Naira Bechara
- Department of Diagnosis and Surgery São José dos Campos School of Dentistry São Paulo State University (UNESP) São José dos Campos Sao Paulo Sao Paulo Brazil
| | - Dmitry Sarmento
- Department of Stomatology School of Dentistry University of São Paulo Sao Paulo Sao Paulo Brazil
| | - Marcia S. Oliveira
- Department of Physics Institute of Exact Sciences and Technology Paulista University (UNIP) Sao Paulo Sao Paulo Brazil
| | - Mauro P. Santamaria
- Department of Diagnosis and Surgery São José dos Campos School of Dentistry São Paulo State University (UNESP) São José dos Campos Sao Paulo Sao Paulo Brazil
| | - Andre Luiz F. Costa
- Postgraduate Program in Dentistry Cruzeiro do Sul University (UNICSUL) Sao Paulo Sao Paulo Brazil
| | - Sérgio Lopes
- Department of Diagnosis and Surgery São José dos Campos School of Dentistry São Paulo State University (UNESP) São José dos Campos Sao Paulo Sao Paulo Brazil
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Moan JM, Amdal CD, Malinen E, Svestad JG, Bogsrud TV, Dale E. The prognostic role of 18F-fluorodeoxyglucose PET in head and neck cancer depends on HPV status. Radiother Oncol 2019; 140:54-61. [DOI: 10.1016/j.radonc.2019.05.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 05/13/2019] [Accepted: 05/15/2019] [Indexed: 11/25/2022]
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