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Wang B, Liu J, Xie J, Zhang X, Wang Z, Cao Z, Wen D, Wan Hasan WZ, Harun Ramli HR, Dong X. Systematic review and meta-analysis of the prognostic value of 18F-Fluorodeoxyglucose ( 18F-FDG) positron emission tomography (PET) and/or computed tomography (CT)-based radiomics in head and neck cancer. Clin Radiol 2024:S0009-9260(24)00282-4. [PMID: 38944542 DOI: 10.1016/j.crad.2024.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 05/16/2024] [Accepted: 05/24/2024] [Indexed: 07/01/2024]
Abstract
AIM Radiomics involves the extraction of quantitative data from medical images to facilitate the diagnosis, prognosis, and staging of tumors. This study provides a comprehensive overview of the efficacy of radiomics in prognostic applications for head and neck cancer (HNC) in recent years. It undertakes a systematic review of prognostic models specific to HNC and conducts a meta-analysis to evaluate their predictive performance. MATERIALS AND METHODS This study adhered rigorously to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for literature searches. The literature databases, including PubMed, Embase, Cochrane, and Scopus were systematically searched individually. The methodological quality of the incorporated studies underwent assessment utilizing the radiomics quality score (RQS) tool. A random-effects meta-analysis employing the Harrell concordance index (C-index) was conducted to evaluate the performance of all radiomics models. RESULTS Among the 388 studies retrieved, 24 studies encompassing a total of 6,978 cases were incorporated into the systematic review. Furthermore, eight studies, focusing on overall survival as an endpoint, were included in the meta-analysis. The meta-analysis revealed that the estimated random effect of the C-index for all studies utilizing radiomics alone was 0.77 (0.71-0.82), with a substantial degree of heterogeneity indicated by an I2 of 80.17%. CONCLUSIONS Based on this review, prognostic modeling utilizing radiomics has demonstrated enhanced efficacy for head and neck cancers; however, there remains room for improvement in this approach. In the future, advancements are warranted in the integration of clinical parameters and multimodal features, balancing multicenter data, as well as in feature screening and model construction within this field.
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Affiliation(s)
- B Wang
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia; Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
| | - J Liu
- Department of Nursing, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia; Department of Nursing, Chengde Central Hospital, Chengde city, Hebei Province, China.
| | - J Xie
- Department of Automatic, Tsinghua University, Beijing, China.
| | - X Zhang
- Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
| | - Z Wang
- Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
| | - Z Cao
- Department of Radiology, The Affiliated Hospital of Chengde Medical University, Chengde City, Hebei Province, China.
| | - D Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China.
| | - W Z Wan Hasan
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
| | - H R Harun Ramli
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
| | - X Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China; Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde City, Hebei, China; Hebei International Research Center of Medical Engineering, Chengde Medical University, Hebei, China.
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2
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Caldarella C, De Risi M, Massaccesi M, Miccichè F, Bussu F, Galli J, Rufini V, Leccisotti L. Role of 18F-FDG PET/CT in Head and Neck Squamous Cell Carcinoma: Current Evidence and Innovative Applications. Cancers (Basel) 2024; 16:1905. [PMID: 38791983 PMCID: PMC11119768 DOI: 10.3390/cancers16101905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 05/08/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
This article provides an overview of the use of 18F-FDG PET/CT in various clinical scenarios of head-neck squamous cell carcinoma, ranging from initial staging to treatment-response assessment, and post-therapy follow-up, with a focus on the current evidence, debated issues, and innovative applications. Methodological aspects and the most frequent pitfalls in head-neck imaging interpretation are described. In the initial work-up, 18F-FDG PET/CT is recommended in patients with metastatic cervical lymphadenectomy and occult primary tumor; moreover, it is a well-established imaging tool for detecting cervical nodal involvement, distant metastases, and synchronous primary tumors. Various 18F-FDG pre-treatment parameters show prognostic value in terms of disease progression and overall survival. In this scenario, an emerging role is played by radiomics and machine learning. For radiation-treatment planning, 18F-FDG PET/CT provides an accurate delineation of target volumes and treatment adaptation. Due to its high negative predictive value, 18F-FDG PET/CT, performed at least 12 weeks after the completion of chemoradiotherapy, can prevent unnecessary neck dissections. In addition to radiomics and machine learning, emerging applications include PET/MRI, which combines the high soft-tissue contrast of MRI with the metabolic information of PET, and the use of PET radiopharmaceuticals other than 18F-FDG, which can answer specific clinical needs.
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Affiliation(s)
- Carmelo Caldarella
- Nuclear Medicine Unit, Department of Radiology and Oncologic Radiotherapy, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.C.); (M.D.R.); (L.L.)
| | - Marina De Risi
- Nuclear Medicine Unit, Department of Radiology and Oncologic Radiotherapy, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.C.); (M.D.R.); (L.L.)
| | - Mariangela Massaccesi
- Radiation Oncology Unit, Department of Radiology and Oncologic Radiotherapy, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Francesco Miccichè
- Radiation Oncology Unit, Ospedale Isola Tiberina—Gemelli Isola, 00186 Rome, Italy;
| | - Francesco Bussu
- Otorhinolaryngology Operative Unit, Azienda Ospedaliero Universitaria Sassari, 07100 Sassari, Italy;
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Jacopo Galli
- Otorhinolaryngology Unit, Department of Neurosciences, Sensory Organs and Thorax, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
- Section of Otolaryngology, Department of Head-Neck and Sensory Organs, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Vittoria Rufini
- Nuclear Medicine Unit, Department of Radiology and Oncologic Radiotherapy, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.C.); (M.D.R.); (L.L.)
- Section of Nuclear Medicine, Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Lucia Leccisotti
- Nuclear Medicine Unit, Department of Radiology and Oncologic Radiotherapy, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.C.); (M.D.R.); (L.L.)
- Section of Nuclear Medicine, Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
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3
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Prior O, Macarro C, Navarro V, Monreal C, Ligero M, Garcia-Ruiz A, Serna G, Simonetti S, Braña I, Vieito M, Escobar M, Capdevila J, Byrne AT, Dienstmann R, Toledo R, Nuciforo P, Garralda E, Grussu F, Bernatowicz K, Perez-Lopez R. Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer. Radiol Artif Intell 2024; 6:e230118. [PMID: 38294307 PMCID: PMC10982821 DOI: 10.1148/ryai.230118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 10/18/2023] [Accepted: 01/07/2024] [Indexed: 02/01/2024]
Abstract
Purpose To identify precise three-dimensional radiomics features in CT images that enable computation of stable and biologically meaningful habitats with machine learning for cancer heterogeneity assessment. Materials and Methods This retrospective study included 2436 liver or lung lesions from 605 CT scans (November 2010-December 2021) in 331 patients with cancer (mean age, 64.5 years ± 10.1 [SD]; 185 male patients). Three-dimensional radiomics were computed from original and perturbed (simulated retest) images with different combinations of feature computation kernel radius and bin size. The lower 95% confidence limit (LCL) of the intraclass correlation coefficient (ICC) was used to measure repeatability and reproducibility. Precise features were identified by combining repeatability and reproducibility results (LCL of ICC ≥ 0.50). Habitats were obtained with Gaussian mixture models in original and perturbed data using precise radiomics features and compared with habitats obtained using all features. The Dice similarity coefficient (DSC) was used to assess habitat stability. Biologic correlates of CT habitats were explored in a case study, with a cohort of 13 patients with CT, multiparametric MRI, and tumor biopsies. Results Three-dimensional radiomics showed poor repeatability (LCL of ICC: median [IQR], 0.442 [0.312-0.516]) and poor reproducibility against kernel radius (LCL of ICC: median [IQR], 0.440 [0.33-0.526]) but excellent reproducibility against bin size (LCL of ICC: median [IQR], 0.929 [0.853-0.988]). Twenty-six radiomics features were precise, differing in lung and liver lesions. Habitats obtained with precise features (DSC: median [IQR], 0.601 [0.494-0.712] and 0.651 [0.52-0.784] for lung and liver lesions, respectively) were more stable than those obtained with all features (DSC: median [IQR], 0.532 [0.424-0.637] and 0.587 [0.465-0.703] for lung and liver lesions, respectively; P < .001). In the case study, CT habitats correlated quantitatively and qualitatively with heterogeneity observed in multiparametric MRI habitats and histology. Conclusion Precise three-dimensional radiomics features were identified on CT images that enabled tumor heterogeneity assessment through stable tumor habitat computation. Keywords: CT, Diffusion-weighted Imaging, Dynamic Contrast-enhanced MRI, MRI, Radiomics, Unsupervised Learning, Oncology, Liver, Lung Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Sagreiya in this issue.
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Affiliation(s)
- Olivia Prior
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Carlos Macarro
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Víctor Navarro
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Camilo Monreal
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Marta Ligero
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Alonso Garcia-Ruiz
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Garazi Serna
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Sara Simonetti
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Irene Braña
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Maria Vieito
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Manuel Escobar
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Jaume Capdevila
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Annette T. Byrne
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Rodrigo Dienstmann
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Rodrigo Toledo
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Paolo Nuciforo
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Elena Garralda
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Francesco Grussu
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
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Hu Y, Jiang T, Wang H, Song J, Yang Z, Wang Y, Su J, Jin M, Chang S, Deng K, Jiang W. Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC. Phys Med 2024; 117:103200. [PMID: 38160516 DOI: 10.1016/j.ejmp.2023.103200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 08/18/2023] [Accepted: 12/21/2023] [Indexed: 01/03/2024] Open
Abstract
PURPOSE To develop and externally validate subregional radiomics for predicting therapeutic response to anti-PD1 therapy in non-small-cell lung cancer (NSCLC). METHODS Sixty-six patients from center 1 served as training and internal validation cohorts. Thirty patients from center 2 and thirty patients from center 3 served as external validation 1 and external validation 2 cohorts, respectively. The lesions identified on CT scans were subdivided into two phenotypically consistent subregions by automatic clustering on the patient-level and population-level (denoted as marginal S1 and inner S2). Handcrafted and deep learning-based features were extracted separately from the entire tumor region and subregions, then selected using the intraclass correlation coefficient and least absolute shrinkage and selection operator regression (LASSO). Radiomics signatures (RSs) were built integrating the selected features and correlation coefficients using a logistic regression method. Area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the RSs. RESULTS RSs derived from S1 outperformed those from S2 and the whole tumor region for both handcrafted and deep learning features. The Fusion-RS incorporating the two feature types achieved the best prediction performance in training (AUC = 0.947, 95 % Confidence Interval [CI] 0.905-0.989, SPE = 0.895, SEN = 0.878), internal validation (AUC = 0.875, 95 % CI: 0.782-0.969, SPE = 0.724, SEN = 0.952), external validation 1 (AUC = 0.836, 95 % CI: 0.694-0.977, SPE = 1.000, SEN = 0.533) and external validation 2 (AUC = 0.783, 95 % CI: 0.613-0.953, SPE = 0.765, SEN = 0.692) cohorts. CONCLUSIONS Subregional radiomics analysis can be useful for predicting therapeutic response to anti-PD1 therapy. The developed Fusion-RS may be considered as a potential non-invasive tool for individual treatment managements.
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Affiliation(s)
- Yue Hu
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Tao Jiang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Huan Wang
- Radiation Oncology Department Of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Jiangdian Song
- School of Medical Informatics, China Medical University, Liaoning 110122, PR China
| | - Zhiguang Yang
- Department of Radiology, Shengjing Hospital, Shenyang 110004, PR China
| | - Yan Wang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Juan Su
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Meiqi Jin
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Shijie Chang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China.
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Anhui 230036, PR China.
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China.
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Yuan S, Chen X, Liu Y, Zhu J, Men K, Dai J. Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma. Radiat Oncol 2023; 18:182. [PMID: 37936196 PMCID: PMC10629140 DOI: 10.1186/s13014-023-02349-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 09/11/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND Although magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis studies based on deep learning have significantly progressed, the similarity between synthetic CT (sCT) and real CT (rCT) has only been evaluated in image quality metrics (IQMs). To evaluate the similarity between synthetic CT (sCT) and real CT (rCT) comprehensively, we comprehensively evaluated IQMs and radiomic features for the first time. METHODS This study enrolled 127 patients with nasopharyngeal carcinoma who underwent CT and MRI scans. Supervised-learning (Unet) and unsupervised-learning (CycleGAN) methods were applied to build MRI-to-CT synthesis models. The regions of interest (ROIs) included nasopharynx gross tumor volume (GTVnx), brainstem, parotid glands, and temporal lobes. The peak signal-to-noise ratio (PSNR), mean absolute error (MAE), root mean square error (RMSE), and structural similarity (SSIM) were used to evaluate image quality. Additionally, 837 radiomic features were extracted for each ROI, and the correlation was evaluated using the concordance correlation coefficient (CCC). RESULTS The MAE, RMSE, SSIM, and PSNR of the body were 91.99, 187.12, 0.97, and 51.15 for Unet and 108.30, 211.63, 0.96, and 49.84 for CycleGAN. For the metrics, Unet was superior to CycleGAN (P < 0.05). For the radiomic features, the percentage of four levels (i.e., excellent, good, moderate, and poor, respectively) were as follows: GTVnx, 8.5%, 14.6%, 26.5%, and 50.4% for Unet and 12.3%, 25%, 38.4%, and 24.4% for CycleGAN; other ROIs, 5.44% ± 3.27%, 5.56% ± 2.92%, 21.38% ± 6.91%, and 67.58% ± 8.96% for Unet and 5.16% ± 1.69%, 3.5% ± 1.52%, 12.68% ± 7.51%, and 78.62% ± 8.57% for CycleGAN. CONCLUSIONS Unet-sCT was superior to CycleGAN-sCT for the IQMs. However, neither exhibited absolute superiority in radiomic features, and both were far less similar to rCT. Therefore, further work is required to improve the radiomic similarity for MRI-to-CT synthesis. TRIAL REGISTRATION This study was a retrospective study, so it was free from registration.
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Affiliation(s)
- Siqi Yuan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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6
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Xu H, Lv W, Zhang H, Yuan Q, Wang Q, Wu Y, Lu L. Multimodality radiomics analysis based on [ 18F]FDG PET/CT imaging and multisequence MRI: application to nasopharyngeal carcinoma prognosis. Eur Radiol 2023; 33:6677-6688. [PMID: 37060444 DOI: 10.1007/s00330-023-09606-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 01/02/2023] [Accepted: 02/13/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVES To determine whether radiomics models developed from 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT combined with multisequence MRI could contribute to predicting the progression-free survival (PFS) of nasopharyngeal carcinoma (NPC) patients. METHODS One hundred thirty-two NPC patients who underwent both PET/CT and MRI scanning were retrospectively enrolled (88 vs. 44 for training vs. testing). For each modality/sequence (i.e., PET, CT, T1, T1C, and T2), 1906 radiomics features were extracted from the primary tumor volume. Univariate Cox model and correlation analysis were used for feature selection. A multivariate Cox model was used to establish radiomics signature. Prognostic performances of 5 individual modality models and 12 multimodality models (3 integrations × 4 fusion strategies) were assessed by the concordance index (C-index) and log-rank test. A clinical-radiomics nomogram was built to explore the clinical utilities of radiomics signature, which was evaluated by discrimination, calibration curve, and decision curve analysis (DCA). RESULTS The radiomics signatures of individual modalities showed limited prognostic efficacy with a C-index of 0.539-0.664 in the testing cohort. Different fusion strategies exhibited a slight difference in predictive performance. The PET/CT and MRI integrated model achieved the best performance with a C-index of 0.745 (95% CI, 0.619-0.865) in the testing cohort (log-rank test, p < 0.05). Clinical-radiomics nomogram further improved the prognosis, which also showed satisfactory discrimination, calibration, and net benefit. CONCLUSIONS Multimodality radiomics analysis by combining PET/CT with multisequence MRI could potentially improve the efficacy of PFS prediction for NPC patients. KEY POINTS • Individual modality radiomics models showed limited performance in prognosis evaluation for NPC patients. • Combined PET, CT and multisequence MRI radiomics signature could improve the prognostic efficacy. • Multilevel fusion strategies exhibit comparable performance but feature-level fusion deserves more attention.
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Affiliation(s)
- Hui Xu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Provincial Key Laboratory of Medial Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Pazhou Lab, Guangzhou, 510330, China
| | - Wenbing Lv
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Provincial Key Laboratory of Medial Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Pazhou Lab, Guangzhou, 510330, China
| | - Hao Zhang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Qingyu Yuan
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Quanshi Wang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Yuankui Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
- Guangdong Provincial Key Laboratory of Medial Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, Guangdong, China.
- Pazhou Lab, Guangzhou, 510330, China.
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Lucia F, Lovinfosse P, Schick U, Le Pennec R, Pradier O, Salaun PY, Hustinx R, Bourbonne V. Radiotherapy modification based on artificial intelligence and radiomics applied to ( 18F)-fluorodeoxyglucose positron emission tomography/computed tomography. Cancer Radiother 2023; 27:542-547. [PMID: 37481344 DOI: 10.1016/j.canrad.2023.06.001] [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: 05/30/2023] [Revised: 06/06/2023] [Accepted: 06/07/2023] [Indexed: 07/24/2023]
Abstract
Over the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (18F)-FDG PET/CT in the management of cancer treated by radiation therapy.
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Affiliation(s)
- F Lucia
- Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France; Division of Nuclear Medicine and Oncological Imaging, centre hospitalier universitaire de Liège, Liège, Belgium.
| | - P Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, centre hospitalier universitaire de Liège, Liège, Belgium
| | - U Schick
- Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
| | - R Le Pennec
- Service de médecine nucléaire, CHU de Brest, Inserm UMR 1304 (Getbo), université de Bretagne Occidentale, Brest, France
| | - O Pradier
- Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
| | - P-Y Salaun
- Service de médecine nucléaire, CHU de Brest, Inserm UMR 1304 (Getbo), université de Bretagne Occidentale, Brest, France
| | - R Hustinx
- Division of Nuclear Medicine and Oncological Imaging, centre hospitalier universitaire de Liège, Liège, Belgium
| | - V Bourbonne
- Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
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Philip MM, Welch A, McKiddie F, Nath M. A systematic review and meta-analysis of predictive and prognostic models for outcome prediction using positron emission tomography radiomics in head and neck squamous cell carcinoma patients. Cancer Med 2023; 12:16181-16194. [PMID: 37353996 PMCID: PMC10469753 DOI: 10.1002/cam4.6278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/07/2023] [Accepted: 06/11/2023] [Indexed: 06/25/2023] Open
Abstract
BACKGROUND Positron emission tomography (PET) images of head and neck squamous cell carcinoma (HNSCC) patients can assess the functional and biochemical processes at cellular levels. Therefore, PET radiomics-based prediction and prognostic models have the potentials to understand tumour heterogeneity and assist clinicians with diagnosis, prognosis and management of the disease. We conducted a systematic review of published modelling information to evaluate the usefulness of PET radiomics in the prediction and prognosis of HNSCC patients. METHODS We searched bibliographic databases (MEDLINE, Embase, Web of Science) from 2010 to 2021 and considered 31 studies with pre-defined inclusion criteria. We followed the CHARMS checklist for data extraction and performed quality assessment using the PROBAST tool. We conducted a meta-analysis to estimate the accuracy of the prediction and prognostic models using the diagnostic odds ratio (DOR) and average C-statistic, respectively. RESULTS Manual segmentation method followed by 40% of the maximum standardised uptake value (SUVmax ) thresholding is a commonly used approach. The area under the receiver operating curves of externally validated prediction models ranged between 0.60-0.87, 0.65-0.86 and 0.62-0.75 for overall survival, distant metastasis and recurrence, respectively. Most studies highlighted an overall high risk of bias (outcome definition, statistical methodologies and external validation of models) and high unclear concern in terms of applicability. The meta-analysis showed the estimated pooled DOR of 6.75 (95% CI: 4.45, 10.23) for prediction models and the C-statistic of 0.71 (95% CI: 0.67, 0.74) for prognostic models. CONCLUSIONS Both prediction and prognostic models using clinical variables and PET radiomics demonstrated reliable accuracy for detecting adverse outcomes in HNSCC, suggesting the prospect of PET radiomics in clinical settings for diagnosis, prognosis and management of HNSCC patients. Future studies of prediction and prognostic models should emphasise the quality of reporting, external model validation, generalisability to real clinical scenarios and enhanced reproducibility of results.
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Affiliation(s)
| | - Andy Welch
- Institute of Education in Healthcare and Medical Sciences, University of AberdeenAberdeenUK
| | | | - Mintu Nath
- Institute of Applied Health Sciences, University of AberdeenAberdeenUK
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Long ZC, Ding XC, Zhang XB, Sun PP, Hao FR, Li ZR, Hu M. The Efficacy of Pretreatment 18F-FDG PET-CT-Based Deep Learning Network Structure to Predict Survival in Nasopharyngeal Carcinoma. Clin Med Insights Oncol 2023; 17:11795549231171793. [PMID: 37251551 PMCID: PMC10214083 DOI: 10.1177/11795549231171793] [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: 11/28/2022] [Accepted: 04/10/2023] [Indexed: 05/31/2023] Open
Abstract
Background Previous studies have shown that the 5-year survival rates of patients with nasopharyngeal carcinoma (NPC) were still not ideal despite great improvement in NPC treatments. To achieve individualized treatment of NPC, we have been looking for novel models to predict the prognosis of patients with NPC. The objective of this study was to use a novel deep learning network structural model to predict the prognosis of patients with NPC and to compare it with the traditional PET-CT model combining metabolic parameters and clinical factors. Methods A total of 173 patients were admitted to 2 institutions between July 2014 and April 2020 for the retrospective study; each received a PET-CT scan before treatment. The least absolute shrinkage and selection operator (LASSO) was employed to select some features, including SUVpeak-P, T3, age, stage II, MTV-P, N1, stage III and pathological type, which were associated with overall survival (OS) of patients. We constructed 2 survival prediction models: an improved optimized adaptive multimodal task (a 3D Coordinate Attention Convolutional Autoencoder and an uncertainty-based jointly Optimizing Cox Model, CACA-UOCM for short) and a clinical model. The predictive power of these models was assessed using the Harrell Consistency Index (C index). Overall survival of patients with NPC was compared by Kaplan-Meier and Log-rank tests. Results The results showed that CACA-UOCM model could estimate OS (C index, 0.779 for training, 0.774 for validation, and 0.819 for testing) and divide patients into low and high mortality risk groups, which were significantly associated with OS (P < .001). However, the C-index of the model based only on clinical variables was only 0.42. Conclusions The deep learning network model based on 18F-FDG PET/CT can serve as a reliable and powerful predictive tool for NPC and provide therapeutic strategies for individual treatment.
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Affiliation(s)
- Zi-Chan Long
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xing-Chen Ding
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xian-Bin Zhang
- Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Peng-Peng Sun
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Fu-Rong Hao
- Department of Radiation Oncology, Weifang People's Hospital, Weifang, China
| | | | - Man Hu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Lv W, Zhou Z, Peng J, Peng L, Lin G, Wu H, Xu H, Lu L. Functional-structural sub-region graph convolutional network (FSGCN): Application to the prognosis of head and neck cancer with PET/CT imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107341. [PMID: 36682111 DOI: 10.1016/j.cmpb.2023.107341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 12/14/2022] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate risk stratification is crucial for enabling personalized treatment for head and neck cancer (HNC). Current PET/CT image-based prognostic methods include radiomics analysis and convolutional neural network (CNN), while extracting radiomics or deep features in grid Euclidean space has inherent limitations for risk stratification. Here, we propose a functional-structural sub-region graph convolutional network (FSGCN) for accurate risk stratification of HNC. METHODS This study collected 642 patients from 8 different centers in The Cancer Imaging Archive (TCIA), 507 patients from 5 centers were used for training, and 135 patients from 3 centers were used for testing. The tumor was first clustered into multiple sub-regions by using PET and CT voxel information, and radiomics features were extracted from each sub-region to characterize its functional and structural information, a graph was then constructed to format the relationship/difference among different sub-regions in non-Euclidean space for each patient, followed by a residual gated graph convolutional network, the prognostic score was finally generated to predict the progression-free survival (PFS). RESULTS In the testing cohort, compared with radiomics or FSGCN or clinical model alone, the model PETCTFea_CTROI + Cli that integrates FSGCN prognostic score and clinical parameter achieved the highest C-index and AUC of 0.767 (95% CI: 0.759-0.774) and 0.781 (95% CI: 0.774-0.788), respectively for PFS prediction. Besides, it also showed good prognostic performance on the secondary endpoints OS, RFS, and MFS in the testing cohort, with C-index of 0.786 (95% CI: 0.778-0.795), 0.775 (95% CI: 0.767-0.782) and 0.781 (95% CI: 0.772-0.789), respectively. CONCLUSIONS The proposed FSGCN can better capture the metabolic or anatomic difference/interaction among sub-regions of the whole tumor imaged with PET/CT. Extensive multi-center experiments demonstrated its capability and generalization of prognosis prediction in HNC over conventional radiomics analysis.
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Affiliation(s)
- Wenbing Lv
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Department of Electronic Engineering, Information School, Yunnan University, Kunming 650091, China
| | - Zidong Zhou
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
| | - Junyi Peng
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
| | - Lihong Peng
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
| | - Guoyu Lin
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
| | - Huiqin Wu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
| | - Hui Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Pazhou Lab, Guangzhou 510515, China.
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Wang B, Liu J, Zhang X, Wang Z, Cao Z, Lu L, Lv W, Wang A, Li S, Wu X, Dong X. Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer. EJNMMI Res 2023; 13:14. [PMID: 36779997 PMCID: PMC9925656 DOI: 10.1186/s13550-023-00959-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 01/26/2023] [Indexed: 02/14/2023] Open
Abstract
OBJECTIVES By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. MATERIALS AND METHODS A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan-Meier curves (log-rank analysis) were used to evaluate and compare these models. RESULTS The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812-0.934), 0.759 (95% CI 0.663-0.855) and 0.835 (95% CI 0.745-0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). CONCLUSION Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC.
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Affiliation(s)
- Bingzhen Wang
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Jinghua Liu
- Department of Nursing, Chengde Central Hospital, Chengde, Hebei China ,grid.11142.370000 0001 2231 800XDepartment of Nursing, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia
| | - Xiaolei Zhang
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Zhongxiao Wang
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Zhendong Cao
- grid.413851.a0000 0000 8977 8425Department of Radiology, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei China
| | - Lijun Lu
- grid.284723.80000 0000 8877 7471School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong China
| | - Wenbing Lv
- grid.440773.30000 0000 9342 2456Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan China
| | - Aihui Wang
- grid.413851.a0000 0000 8977 8425Department of Nuclear Medicine, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei China
| | - Shuyan Li
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Xiaotian Wu
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Xianling Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China. .,Hebei International Research Center of Medical-Engineering, Chengde Medical University, Chengde, Hebei, China.
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12
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Li S, Wan X, Deng YQ, Hua HL, Li SL, Chen XX, Zeng ML, Zha Y, Tao ZZ. Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued. Cancer Imaging 2023; 23:14. [PMID: 36759889 PMCID: PMC9912633 DOI: 10.1186/s40644-023-00530-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 02/01/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC. METHODS A total of 381 NPC patients who were divided into high- and low-risk groups according to progression-free survival were retrospectively included. Deeplab v3 and U-Net were trained to build segmentation models for the automatic segmentation of the tumor and suspicious lymph nodes. Five datasets were constructed by expanding 5, 10, 20, 40, and 60 pixels outward from the edge of the automatically segmented region. Inception-Resnet-V2, ECA-ResNet50t, EfficientNet-B3, and EfficientNet-B0 were trained with the original, segmented, and the five new constructed datasets to establish the classification models. The receiver operating characteristic curve was used to evaluate the performance of each model. RESULTS The Dice coefficients of Deeplab v3 and U-Net were 0.741(95%CI:0.722-0.760) and 0.737(95%CI:0.720-0.754), respectively. The average areas under the curve (aAUCs) of deep learning models for classification trained with the original and segmented images and with images expanded by 5, 10, 20, 40, and 60 pixels were 0.717 ± 0.043, 0.739 ± 0.016, 0.760 ± 0.010, 0.768 ± 0.018, 0.802 ± 0.013, 0.782 ± 0.039, and 0.753 ± 0.014, respectively. The models trained with the images expanded by 20 pixels obtained the best performance. CONCLUSIONS The peritumoral region NPC contains information related to prognosis, and the incorporation of this region could improve the performance of deep learning models for prognosis prediction.
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Affiliation(s)
- Song Li
- grid.89957.3a0000 0000 9255 8984Department of Otorhinolaryngology, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029 China ,grid.412632.00000 0004 1758 2270Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Xia Wan
- grid.510937.9Department of Otolaryngology-Head & Neck Surgery, Ezhou Central Hospital, No. 9 Wenxing Road, Ezhou, 436000 P.R. China
| | - Yu-Qin Deng
- grid.412632.00000 0004 1758 2270Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Hong-Li Hua
- grid.412632.00000 0004 1758 2270Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Sheng-Lan Li
- grid.412632.00000 0004 1758 2270Department of Radiology, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Xi-Xiang Chen
- grid.412632.00000 0004 1758 2270Department of Radiology, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Man-Li Zeng
- grid.510937.9Department of Otolaryngology-Head & Neck Surgery, Ezhou Central Hospital, No. 9 Wenxing Road, Ezhou, 436000 P.R. China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei, 430060, P.R. China.
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei, 430060, P.R. China.
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13
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Mo X, Chen W, Chen S, Chen Z, Guo Y, Chen Y, Wu X, Zhang L, Chen Q, Jin Z, Li M, Chen L, You J, Xiong Z, Zhang B, Zhang S. MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study. Insights Imaging 2023; 14:28. [PMID: 36746892 PMCID: PMC9902579 DOI: 10.1186/s13244-023-01370-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 01/03/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function. METHODS A retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models. RESULTS The models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935-0.940), 0.919 (95%CI 0.916-0.922), and 0.959 (95%CI 0.956-0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800-0.807), 0.852 (95%CI 0.846-0.857), and 0.863 (95%CI 0.857-0.887) in the validation cohorts, respectively. CONCLUSION We developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.
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Affiliation(s)
- Xiaokai Mo
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Wenbo Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China ,grid.470066.3Department of Radiology, Huizhou Municipal Central Hospital, No. 41 Eling Bei Road, Huizhou, 516001 Guangdong People’s Republic of China
| | - Simin Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Zhuozhi Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Yuanshu Guo
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Yulian Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Xuewei Wu
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Lu Zhang
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Qiuying Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Zhe Jin
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Minmin Li
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Luyan Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Jingjing You
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Zhiyuan Xiong
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China.
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China.
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14
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Xu H, Abdallah N, Marion JM, Chauvet P, Tauber C, Carlier T, Lu L, Hatt M. Radiomics prognostic analysis of PET/CT images in a multicenter head and neck cancer cohort: investigating ComBat strategies, sub-volume characterization, and automatic segmentation. Eur J Nucl Med Mol Imaging 2023; 50:1720-1734. [PMID: 36690882 DOI: 10.1007/s00259-023-06118-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 01/16/2023] [Indexed: 01/25/2023]
Abstract
PURPOSE This study aimed to investigate the impact of several ComBat harmonization strategies, intra-tumoral sub-volume characterization, and automatic segmentations for progression-free survival (PFS) prediction through radiomics modeling for patients with head and neck cancer (HNC) in PET/CT images. METHODS The HECKTOR MICCAI 2021 challenge set containing PET/CT images and clinical data of 325 oropharynx HNC patients was exploited. A total of 346 IBSI-compliant radiomic features were extracted for each patient's primary tumor volume defined by the reference manual contours. Modeling relied on least absolute shrinkage Cox regression (Lasso-Cox) for feature selection (FS) and Cox proportional-hazards (CoxPH) models were built to predict PFS. Within this methodological framework, 8 different strategies for ComBat harmonization were compared, including before or after FS, in feature groups separately or all features directly, and with center or clustering-determined labels. Features extracted from tumor sub-volume clustering were also investigated for their prognostic additional value. Finally, 3 automatic segmentations (2 threshold-based and a 3D U-Net) were also compared. All results were evaluated with the concordance index (C-index). RESULTS Radiomics features without harmonization, combined with clinical factors, led to models with C-index values of 0.69 in the testing set. The best version of ComBat harmonization, i.e., after FS, for feature groups separately and relying on clustering-determined labels, achieved a C-index of 0.71. The use of features extracted from tumor sub-volumes further improved the C-index to 0.72. Models that relied on the automatic segmentations yielded close but slightly lower prognostic performance (0.67-0.70) compared to reference contours. CONCLUSION A standard radiomics pipeline allowed for prediction of PFS in a multicenter HNC cohort. Applying a specific strategy of ComBat harmonization improved the performance. The extraction of intra-tumoral sub-volume features and automatic segmentation could contribute to the improvement and automation of prognosis modeling, respectively.
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Affiliation(s)
- Hui Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, China.,LaTIM, INSERM, UMR 1101, University Brest, Brest, France
| | | | | | | | - Clovis Tauber
- INSERM U930, Université François Rabelais de Tours, Tours, France
| | - Thomas Carlier
- Nuclear Medicine Department, CHU and CRCINA, INSERM, CNRS, Univ Angers, Univ Nantes, Nantes, France
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, China. .,Pazhou Lab, Guangzhou, 510330, China.
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France
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15
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Kulanthaivelu R, Kohan A, Hinzpeter R, Liu ZA, Hope A, Huang SH, Waldron J, O’Sullivan B, Ortega C, Metser U, Veit-Haibach P. Prognostic value of PET/CT and MR-based baseline radiomics among patients with non-metastatic nasopharyngeal carcinoma. Front Oncol 2022; 12:952763. [PMID: 36353565 PMCID: PMC9638017 DOI: 10.3389/fonc.2022.952763] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 10/03/2022] [Indexed: 11/09/2023] Open
Abstract
PURPOSE Radiomics is an emerging imaging assessment technique that has shown promise in predicting survival among nasopharyngeal carcinoma (NPC) patients. Studies so far have focused on PET or MR-based radiomics independently. The aim of our study was to evaluate the prognostic value of clinical and radiomic parameters derived from both PET/CT and MR. METHODS Retrospective evaluation of 124 NPC patients with PET/CT and radiotherapy planning MR (RP-MR). Primary tumors were segmented using dedicated software (LIFEx version 6.1) from PET, CT, contrast-enhanced T1-weighted (T1-w), and T2-weighted (T2-w) MR sequences with 376 radiomic features extracted. Summary statistics describe patient, disease, and treatment characteristics. The Kaplan-Meier (KM) method estimates overall survival (OS) and progression-free survival (PFS). Clinical factors selected based on univariable analysis and the multivariable Cox model were subsequently constructed with radiomic features added. RESULTS The final models comparing clinical, clinical + RP-MR, clinical + PET/CT and clinical + RP-MR + PET/CT for OS and PFS demonstrated that combined radiomic signatures were significantly associated with improved survival prognostication (AUC 0.62 vs 0.81 vs 0.75 vs 0.86 at 21 months for PFS and 0.56 vs 0.85 vs 0.79 vs 0.96 at 24 months for OS). Clinical + RP-MR features initially outperform clinical + PET/CT for both OS and PFS (<18 months), and later in the clinical course for PFS (>42 months). CONCLUSION Our study demonstrated that PET/CT-based radiomic features may improve survival prognostication among NPC patients when combined with baseline clinical and MR-based radiomic features.
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Affiliation(s)
- Roshini Kulanthaivelu
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Andres Kohan
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Ricarda Hinzpeter
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Zhihui Amy Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Andrew Hope
- Department of Radiation Oncology, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Shao Hui Huang
- Department of Radiation Oncology, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - John Waldron
- Department of Radiation Oncology, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Brian O’Sullivan
- Department of Radiation Oncology, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Claudia Ortega
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Ur Metser
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
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16
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Wang X, Xu C, Grzegorzek M, Sun H. Habitat radiomics analysis of pet/ct imaging in high-grade serous ovarian cancer: Application to Ki-67 status and progression-free survival. Front Physiol 2022; 13:948767. [PMID: 36091379 PMCID: PMC9452776 DOI: 10.3389/fphys.2022.948767] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose: We aim to develop and validate PET/ CT image-based radiomics to determine the Ki-67 status of high-grade serous ovarian cancer (HGSOC), in which we use the metabolic subregion evolution to improve the prediction ability of the model. At the same time, the stratified effect of the radiomics model on the progression-free survival rate of ovarian cancer patients was illustrated.Materials and methods: We retrospectively reviewed 161 patients with HGSOC from April 2013 to January 2019. 18F-FDG PET/ CT images before treatment, pathological reports, and follow-up data were analyzed. A randomized grouping method was used to divide ovarian cancer patients into a training group and validation group. PET/ CT images were fused to extract radiomics features of the whole tumor region and radiomics features based on the Habitat method. The feature is dimensionality reduced, and meaningful features are screened to form a signature for predicting the Ki-67 status of ovarian cancer. Meanwhile, survival analysis was conducted to explore the hierarchical guidance significance of radiomics in the prognosis of patients with ovarian cancer.Results: Compared with texture features extracted from the whole tumor, the texture features generated by the Habitat method can better predict the Ki-67 state (p < 0.001). Radiomics based on Habitat can predict the Ki-67 expression accurately and has the potential to become a new marker instead of Ki-67. At the same time, the Habitat model can better stratify the prognosis (p < 0.05).Conclusion: We found a noninvasive imaging predictor that could guide the stratification of prognosis in ovarian cancer patients, which is related to the expression of Ki-67 in tumor tissues. This method is of great significance for the diagnosis and treatment of ovarian cancer.
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Affiliation(s)
- Xinghao Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chen Xu
- Department of Surgical Oncology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Luebeck, Luebeck, Germany
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
- *Correspondence: Hongzan Sun,
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Wang L, Feng Q, Ge X, Chen F, Yu B, Chen B, Liao Z, Lin B, Lv Y, Ding Z. Textural features reflecting local activity of the hippocampus improve the diagnosis of Alzheimer’s disease and amnestic mild cognitive impairment: A radiomics study based on functional magnetic resonance imaging. Front Neurosci 2022; 16:970245. [PMID: 36003964 PMCID: PMC9393721 DOI: 10.3389/fnins.2022.970245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 07/19/2022] [Indexed: 11/14/2022] Open
Abstract
Background Textural features of the hippocampus in structural magnetic resonance imaging (sMRI) images can serve as potential diagnostic biomarkers for Alzheimer’s disease (AD), while exhibiting a relatively poor discriminant performance in detecting early AD, such as amnestic mild cognitive impairment (aMCI). In contrast to sMRI, functional magnetic resonance imaging (fMRI) can identify brain functional abnormalities in the early stages of cerebral disorders. However, whether the textural features reflecting local functional activity in the hippocampus can improve the diagnostic performance for AD and aMCI remains unclear. In this study, we combined the textural features of the amplitude of low frequency fluctuation (ALFF) in the slow-5 frequency band and structural images in the hippocampus to investigate their diagnostic performance for AD and aMCI using multimodal radiomics technique. Methods Totally, 84 AD, 50 aMCI, and 44 normal controls (NCs) were included in the current study. After feature extraction and feature selection, the radiomics models incorporating sMRI images, ALFF values and their combinations in the bilateral hippocampus were established for the diagnosis of AD and aMCI. The effectiveness of these models was evaluated by receiver operating characteristic (ROC) analysis. The radiomics models were further validated using the external data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results The results of ROC analysis showed that the radiomics models based on structural images in the hippocampus had a better diagnostic performance for AD compared with the models using ALFF, while the ALFF-based model exhibited better discriminant performance for aMCI than the models with structural images. The radiomics models based on the combinations of structural images and ALFF were found to exhibit the highest accuracy for distinguishing AD from NCs and aMCI from NCs. Conclusion In this study, we found that the textural features reflecting local functional activity could improve the diagnostic performance of traditional structural models for both AD and aMCI. These findings may deepen our understanding of the pathogenesis of AD, contributing to the early diagnosis of AD.
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Affiliation(s)
- Luoyu Wang
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The Center for Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qi Feng
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiuhong Ge
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fenyang Chen
- The Fourth School of Medical, Zhejiang Chinese Medical University, Hangzhou, China
| | - Bo Yu
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, China
| | - Bing Chen
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Zhengluan Liao
- Center for Rehabilitation Medicine, Department of Geriatric VIP No. 3, Department of Clinical Psychology, Zhejiang Provincial People’s Hospital, Hangzhou, China
| | - Biying Lin
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yating Lv
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- *Correspondence: Yating Lv,
| | - Zhongxiang Ding
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhongxiang Ding,
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18
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Gu B, Meng M, Bi L, Kim J, Feng DD, Song S. Prediction of 5-year progression-free survival in advanced nasopharyngeal carcinoma with pretreatment PET/CT using multi-modality deep learning-based radiomics. Front Oncol 2022; 12:899351. [PMID: 35965589 PMCID: PMC9372795 DOI: 10.3389/fonc.2022.899351] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 06/28/2022] [Indexed: 11/18/2022] Open
Abstract
Objective Deep learning-based radiomics (DLR) has achieved great success in medical image analysis and has been considered a replacement for conventional radiomics that relies on handcrafted features. In this study, we aimed to explore the capability of DLR for the prediction of 5-year progression-free survival (PFS) in advanced nasopharyngeal carcinoma (NPC) using pretreatment PET/CT images. Methods A total of 257 patients (170/87 patients in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled. We developed an end-to-end multi-modality DLR model, in which a 3D convolutional neural network was optimized to extract deep features from pretreatment PET/CT images and predict the probability of 5-year PFS. The TNM stage, as a high-level clinical feature, could be integrated into our DLR model to further improve the prognostic performance. For a comparison between conventional radiomics and DLR, 1,456 handcrafted features were extracted, and optimal conventional radiomics methods were selected from 54 cross-combinations of six feature selection methods and nine classification methods. In addition, risk group stratification was performed with clinical signature, conventional radiomics signature, and DLR signature. Results Our multi-modality DLR model using both PET and CT achieved higher prognostic performance (area under the receiver operating characteristic curve (AUC) = 0.842 ± 0.034 and 0.823 ± 0.012 for the internal and external cohorts) than the optimal conventional radiomics method (AUC = 0.796 ± 0.033 and 0.782 ± 0.012). Furthermore, the multi-modality DLR model outperformed single-modality DLR models using only PET (AUC = 0.818 ± 0.029 and 0.796 ± 0.009) or only CT (AUC = 0.657 ± 0.055 and 0.645 ± 0.021). For risk group stratification, the conventional radiomics signature and DLR signature enabled significant difference between the high- and low-risk patient groups in both the internal and external cohorts (p < 0.001), while the clinical signature failed in the external cohort (p = 0.177). Conclusion Our study identified potential prognostic tools for survival prediction in advanced NPC, which suggests that DLR could provide complementary values to the current TNM staging.
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Affiliation(s)
- Bingxin Gu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Center for Biomedical Imaging, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
- Key Laboratory of Nuclear Physics and Ion-beam Application Ministry of Education (MOE), Fudan University, Shanghai, China
| | - Mingyuan Meng
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Lei Bi
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Jinman Kim
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - David Dagan Feng
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Center for Biomedical Imaging, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
- Key Laboratory of Nuclear Physics and Ion-beam Application Ministry of Education (MOE), Fudan University, Shanghai, China
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, China
- *Correspondence: Shaoli Song,
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Falahatpour Z, Geramifar P, Mahdavi SR, Abdollahi H, Salimi Y, Nikoofar A, Ay MR. Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy. J Appl Clin Med Phys 2022; 23:e13696. [PMID: 35699200 PMCID: PMC9512354 DOI: 10.1002/acm2.13696] [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: 11/13/2021] [Revised: 04/20/2022] [Accepted: 05/27/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy. METHODS Thirty-two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey-level co-occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTVCT , CTVCT ), PET (GTVPET40 , CTVPET40 ), and RFMs (GTVRFM , CTVRFM ,). Intratumoral heterogeneity areas were segmented as GTVPET50-Boost and radiomic boost target volume (RTVBoost ) on PET and RFMs, respectively. GTVCT in homogenous tumors and GTVPET40 in heterogeneous tumors were considered as GTVgold standard (GTVGS ). One-way analysis of variance was conducted to determine the threshold that finds the best conformity for GTVRFM with GTVGS . Dice similarity coefficient (DSC) and mean absolute percent error (MAPE) were calculated. Linear regression analysis was employed to report the correlations between the gold standard and RFM-derived target volumes. RESULTS Entropy, contrast, and Haralick correlation (H-correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTVRFM-entropy , GTVRFM-contrast , and GTVRFM-H-correlation with GTVGS , respectively. The linear regression results showed a positive correlation between GTVGS and GTVRFM-entropy (r = 0.98, p < 0.001), between GTVGS and GTVRFM-contrast (r = 0.93, p < 0.001), and between GTVGS and GTVRFM-H-correlation (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTVRFM-entropy and CTVRFM-contrast with CTVGS , respectively. Moreover, we used RFM to determine RTVBoost in the heterogeneous tumors. Comparison of RTVBoost with GTVPET50-Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTVBoost-entropy , RTVBoost-contrast , and RTVBoost-H-correlation , respectively. CONCLUSIONS FDG PET-based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.
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Affiliation(s)
- Zahra Falahatpour
- Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Rabie Mahdavi
- Department of Medical Physics, Faculty of Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiology Technology, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Yazdan Salimi
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Nikoofar
- Department of Radiation Oncology, Faculty of Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Ay
- Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
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Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [ 18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions. Cancers (Basel) 2022; 14:cancers14122922. [PMID: 35740588 PMCID: PMC9221062 DOI: 10.3390/cancers14122922] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/07/2022] [Accepted: 06/09/2022] [Indexed: 11/20/2022] Open
Abstract
Simple Summary Breast cancer is a leading cause of morbidity and mortality worldwide. The metastatic disease is largely responsible for cancer patient deaths, and its treatment implies usually different therapies. In this context, the prediction of response to treatment or depiction of treatment-resistant phenotypes is essential in clinical practice, especially in the new era of precision medicine. In this study, we used several combinations of feature selection methods and machine-learning classifiers to construct predictive models of the metabolic response to the treatment of metastatic breast cancer lesions. These models were based on clinical variables and radiomic features extracted from 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography ([18F]F-FDG PET/CT) images, obtained prior to the treatment. Our main goal was to know if this prediction was feasible and to identify those combinations with better predictive performance. We found that several combinations were successful to predict the metabolic response to treatment, of which the least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM) had the best mean performance in terms of area under the curve, in both training and validation cohorts. Model performances depended largely on the selected combinations. Abstract Background: This study aimed to identify optimal combinations between feature selection methods and machine-learning classifiers for predicting the metabolic response of individual metastatic breast cancer lesions, based on clinical variables and radiomic features extracted from pretreatment [18F]F-FDG PET/CT images. Methods: A total of 48 patients with confirmed metastatic breast cancer, who received different treatments, were included. All patients had an [18F]F-FDG PET/CT scan before and after the treatment. From 228 metastatic lesions identified, 127 were categorized as responders (complete or partial metabolic response) and 101 as non-responders (stable or progressive metabolic response), by using the percentage changes in SULpeak (peak standardized uptake values normalized for body lean body mass). The lesion pool was divided into training (n = 182) and testing cohorts (n = 46); for each lesion, 101 image features from both PET and CT were extracted (202 features per lesion). These features, along with clinical and pathological information, allowed the prediction model’s construction by using seven popular feature selection methods in cross-combination with another seven machine-learning (ML) classifiers. The performance of the different models was investigated with the receiver-operating characteristic curve (ROC) analysis, using the area under the curve (AUC) and accuracy (ACC) metrics. Results: The combinations, least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM), or random forest (RF) had the highest AUC in the cross-validation, with 0.93 ± 0.06 and 0.92 ± 0.03, respectively, whereas Lasso + neural network (NN) or SVM, and mutual information (MI) + RF, had the higher AUC and ACC in the validation cohort, with 0.90/0.72, 0.86/0.76, and 87/85, respectively. On average, the models with Lasso and models with SVM had the best mean performance for both AUC and ACC in both training and validation cohorts. Conclusions: Image features obtained from a pretreatment [18F]F-FDG PET/CT along with clinical vaiables could predict the metabolic response of metastatic breast cancer lesions, by their incorporation into predictive models, whose performance depends on the selected combination between feature selection and ML classifier methods.
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
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Xi Y, Ge X, Ji H, Wang L, Duan S, Chen H, Wang M, Hu H, Jiang F, Ding Z. Prediction of Response to Induction Chemotherapy Plus Concurrent Chemoradiotherapy for Nasopharyngeal Carcinoma Based on MRI Radiomics and Delta Radiomics: A Two-Center Retrospective Study. Front Oncol 2022; 12:824509. [PMID: 35530350 PMCID: PMC9074388 DOI: 10.3389/fonc.2022.824509] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/23/2022] [Indexed: 12/03/2022] Open
Abstract
Objective We aimed to establish an MRI radiomics model and a Delta radiomics model to predict tumor retraction after induction chemotherapy (IC) combined with concurrent chemoradiotherapy (CCRT) for primary nasopharyngeal carcinoma (NPC) in non-endemic areas and to validate its efficacy. Methods A total of 272 patients (155 in the training set, 66 in the internal validation set, and 51 in the external validation set) with biopsy pathologically confirmed primary NPC who were screened for pretreatment MRI were retrospectively collected. The NPC tumor was delineated as a region of interest in the two sequenced images of MRI before treatment and after IC, followed by radiomics feature extraction. With the use of maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms, logistic regression was performed to establish pretreatment MRI radiomics and pre- and post-IC Delta radiomics models. The optimal Youden’s index was taken; the receiver operating characteristic (ROC) curve, calibration curve, and decision curve were drawn to evaluate the predictive efficacy of different models. Results Seven optimal feature subsets were selected from the pretreatment MRI radiomics model, and twelve optimal subsets were selected from the Delta radiomics model. The area under the ROC curve, accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of the MRI radiomics model were 0.865, 0.827, 0.837, 0.813, 0.776, and 0.865, respectively; the corresponding indicators of the Delta radiomics model were 0.941, 0.883, 0.793, 0.968, 0.833, and 0.958, respectively. Conclusion The pretreatment MRI radiomics model and pre- and post-IC Delta radiomics models could predict the IC-CCRT response of NPC in non-epidemic areas.
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Affiliation(s)
- Yuzhen Xi
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, 903rd Hospital of PLA, Hangzhou, China
| | - Xiuhong Ge
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
| | - Haiming Ji
- Department of Radiology, Liangzhu Hospital, Hangzhou, China
| | - Luoyu Wang
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Haonan Chen
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Mengze Wang
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital Affiliated to Medical College Zhejiang University, Hangzhou, China
| | - Feng Jiang
- Department of Head and Neck Radiotherapy, Zhejiang Cancer Hospital/Zhejiang Province Key Laboratory of Radiation Oncology, Hangzhou, China
- *Correspondence: Feng Jiang, ; Zhongxiang Ding,
| | - Zhongxiang Ding
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Feng Jiang, ; Zhongxiang Ding,
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Lv W, Xu H, Han X, Zhang H, Ma J, Rahmim A, Lu L. Context-Aware Saliency Guided Radiomics: Application to Prediction of Outcome and HPV-Status from Multi-Center PET/CT Images of Head and Neck Cancer. Cancers (Basel) 2022; 14:cancers14071674. [PMID: 35406449 PMCID: PMC8996849 DOI: 10.3390/cancers14071674] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/19/2022] [Accepted: 03/21/2022] [Indexed: 12/15/2022] Open
Abstract
Simple Summary This study investigated the ability of context-aware saliency-guided PET/CT radiomics in the prediction of outcome and HPV status for head and neck cancer. In total, 806 HNC patients (training vs. validation vs. external testing: 500 vs. 97 vs. 209) from 9 centers were collected from The Cancer Imaging Archive (TCIA). Saliency-guided radiomics showed enhanced performance for both outcome and HPV-status predictions relative to conventional radiomics. The radiomics-predicted HPV status also showed complementary prognostic value. This multi-center study highlights the feasibility of saliency-guided PET/CT radiomics in outcome predictions of head and neck cancer, confirming that certain regions are more relevant to tumor aggressiveness and prognosis. Abstract Purpose: This multi-center study aims to investigate the prognostic value of context-aware saliency-guided radiomics in 18F-FDG PET/CT images of head and neck cancer (HNC). Methods: 806 HNC patients (training vs. validation vs. external testing: 500 vs. 97 vs. 209) from 9 centers were collected from The Cancer Imaging Archive (TCIA). There were 100/384 and 60/123 oropharyngeal carcinoma (OPC) patients with human papillomavirus (HPV) status in training and testing cohorts, respectively. Six types of images were used for radiomics feature extraction and further model construction, namely (i) the original image (Origin), (ii) a context-aware saliency map (SalMap), (iii, iv) high- or low-saliency regions in the original image (highSal or lowSal), (v) a saliency-weighted image (SalxImg), and finally, (vi) a fused PET-CT image (FusedImg). Four outcomes were evaluated, i.e., recurrence-free survival (RFS), metastasis-free survival (MFS), overall survival (OS), and disease-free survival (DFS), respectively. Multivariate Cox analysis and logistic regression were adopted to construct radiomics scores for the prediction of outcome (Rad_Ocm) and HPV-status (Rad_HPV), respectively. Besides, the prognostic value of their integration (Rad_Ocm_HPV) was also investigated. Results: In the external testing cohort, compared with the Origin model, SalMap and SalxImg achieved the highest C-indices for RFS (0.621 vs. 0.559) and MFS (0.785 vs. 0.739) predictions, respectively, while FusedImg performed the best for both OS (0.685 vs. 0.659) and DFS (0.641 vs. 0.582) predictions. In the OPC HPV testing cohort, FusedImg showed higher AUC for HPV-status prediction compared with the Origin model (0.653 vs. 0.484). In the OPC testing cohort, compared with Rad_Ocm or Rad_HPV alone, Rad_Ocm_HPV performed the best for OS and DFS predictions with C-indices of 0.702 (p = 0.002) and 0.684 (p = 0.006), respectively. Conclusion: Saliency-guided radiomics showed enhanced performance for both outcome and HPV-status predictions relative to conventional radiomics. The radiomics-predicted HPV status also showed complementary prognostic value.
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Affiliation(s)
- Wenbing Lv
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
| | - Hui Xu
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
| | - Xu Han
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
| | - Hao Zhang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China;
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada;
- Department of Radiology, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC V6T 1Z1, Canada
- Department of Physics, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1, Canada
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
- Correspondence: ; Tel.: +86-020-62789116
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Duan W, Xiong B, Tian T, Zou X, He Z, Zhang L. Radiomics in Nasopharyngeal Carcinoma. CLINICAL MEDICINE INSIGHTS: ONCOLOGY 2022; 16:11795549221079186. [PMID: 35237090 PMCID: PMC8883403 DOI: 10.1177/11795549221079186] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 01/20/2022] [Indexed: 12/24/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common head and neck malignancies, and the primary treatment methods are radiotherapy and chemotherapy. Radiotherapy alone, concurrent chemoradiotherapy, and induction chemotherapy combined with concurrent chemoradiotherapy can be used according to different grades. Treatment options and prognoses vary greatly depending on the grade of disease in the patients. Accurate grading and risk assessment are required. Recently, radiomics has combined a large amount of invisible high-dimensional information extracted from computed tomography, magnetic resonance imaging, or positron emission tomography with powerful computing capabilities of machine-learning algorithms, providing the possibility to achieve an accurate diagnosis and individualized treatment for cancer patients. As an effective tumor biomarker of NPC, the radiomic signature has been widely used in grading, differential diagnosis, prediction of prognosis, evaluation of treatment response, and early identification of therapeutic complications. The process of radiomic research includes image segmentation, feature extraction, feature selection, model establishment, and evaluation. Many open-source or commercial tools can be used to achieve these procedures. The development of machine-learning algorithms provides more possibilities for radiomics research. This review aimed to summarize the application of radiomics in NPC and introduce the basic process of radiomics research.
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Affiliation(s)
- Wenyue Duan
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Bingdi Xiong
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Ting Tian
- Department of Clinical Medicine, Chengdu Medical College, Chengdu, People's Republic of China
| | - Xinyun Zou
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Zhennan He
- Department of Clinical Medicine, Chengdu Medical College, Chengdu, People's Republic of China
| | - Ling Zhang
- Department of Oncology, People's Liberation Army The General Hospital of Western Theater Command, Chengdu, People's Republic of China
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Lv J, Chen X, Liu X, Du D, Lv W, Lu L, Wu H. Imbalanced Data Correction Based PET/CT Radiomics Model for Predicting Lymph Node Metastasis in Clinical Stage T1 Lung Adenocarcinoma. Front Oncol 2022; 12:788968. [PMID: 35155231 PMCID: PMC8831550 DOI: 10.3389/fonc.2022.788968] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 01/04/2022] [Indexed: 01/15/2023] Open
Abstract
OBJECTIVES To develop and validate the imbalanced data correction based PET/CT radiomics model for predicting lymph node metastasis (LNM) in clinical stage T1 lung adenocarcinoma (LUAD). METHODS A total of 183 patients (148/35 non-metastasis/LNM) with pathologically confirmed LUAD were retrospectively included. The cohorts were divided into training vs. validation cohort in a ratio of 7:3. A total of 487 radiomics features were extracted from PET and CT components separately for radiomics model construction. Four clinical features and seven PET/CT radiological features were extracted for traditional model construction. To balance the distribution of majority (non-metastasis) class and minority (LNM) class, the imbalance-adjustment strategies using ten data re-sampling methods were adopted. Three multivariate models (denoted as Traditional, Radiomics, and Combined) were constructed using multivariable logistic regression analysis, where the combined model incorporated all of the significant clinical, radiological, and radiomics features. One hundred times repeated Monte Carlo cross-validation was used to assess the application order of feature selection and imbalance-adjustment strategies in the machine learning pipeline. Prediction performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC) and Geometric mean score (G-mean). RESULTS A total of 2 clinical parameters, 2 radiological features, 3 PET, and 5 CT radiomics features were significantly associated with LNM. The combined model with Edited Nearest Neighbors (ENN) re-sampling methods showed strong prediction performance than traditional model or radiomics model with the AUC of 0.94 (95%CI = 0.86-0.97) vs. 0.89 (95%CI = 0.79-0.93), 0.92 (95%CI = 0.85-0.97), and G-mean of 0.88 vs. 0.82, 0.80 in the training cohort, and the AUC of 0.75 (95%CI = 0.57-0.91) vs. 0.68 (95%CI = 0.36-0.83), 0.71 (95%CI = 0.48-0.83) and G-mean of 0.76 vs. 0.64, 0.51 in the validation cohort. The combination of performing feature selection before data re-sampling obtains a better result than the reverse combination (AUC 0.76 ± 0.06 vs. 0.70 ± 0.07, p<0.001). CONCLUSIONS The combined model (consisting of age, histological type, C/T ratio, MATV, and radiomics signature) integrated with ENN re-sampling methods had strong lymph node metastasis prediction performance for imbalance cohorts in clinical stage T1 LUAD. Radiomics signatures extracted from PET/CT images could provide complementary prediction information compared with traditional model.
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Affiliation(s)
- Jieqin Lv
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Xiaohui Chen
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xinran Liu
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Dongyang Du
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Wenbing Lv
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Hubing Wu
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Zhang YM, Gong GZ, Qiu QT, Han YW, Lu HM, Yin Y. Radiomics for Diagnosis and Radiotherapy of Nasopharyngeal Carcinoma. Front Oncol 2022; 11:767134. [PMID: 35070971 PMCID: PMC8766636 DOI: 10.3389/fonc.2021.767134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is a malignant tumor of the head and neck. The primary clinical manifestations are nasal congestion, blood-stained nasal discharge, headache, and hearing loss. It occurs frequently in Southeast Asia, North Africa, and especially in southern China. Radiotherapy is the main treatment, and currently, imaging examinations used for the diagnosis, treatment, and prognosis of NPC include computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)-CT, and PET-MRI. These methods play an important role in target delineation, radiotherapy planning design, dose evaluation, and outcome prediction. However, the anatomical and metabolic information obtained at the macro level of images may not meet the increasing accuracy required for radiotherapy. As a technology used for mining deep image information, radiomics can provide further information for the diagnosis and treatment of NPC and promote individualized precision radiotherapy in the future. This paper reviews the application of radiomics in the diagnosis and treatment of nasopharyngeal carcinoma.
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Affiliation(s)
- Yu-Mei Zhang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Guan-Zhong Gong
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Qing-Tao Qiu
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yun-Wei Han
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - He-Ming Lu
- Department of Radiotherapy, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yong Yin
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.,Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Jiang T, Jiang W, Chang S, Wang H, Niu S, Yue Z, Yang H, Wang X, Zhao N, Fang S, Luo Y, Jiang X. Intratumoral analysis of digital breast tomosynthesis for predicting the Ki-67 level in breast cancer: A multi-center radiomics study. Med Phys 2021; 49:219-230. [PMID: 34861045 DOI: 10.1002/mp.15392] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 11/18/2021] [Accepted: 11/20/2021] [Indexed: 12/16/2022] Open
Abstract
PURPOSE To non-invasively evaluate the Ki-67 level in digital breast tomosynthesis (DBT) images of breast cancer (BC) patients based on subregional radiomics. METHODS A total of 266 patients who underwent DBT scans were consecutively enrolled at two centers, between September 2017 and September 2021. The whole tumor region was partitioned into various intratumoral subregions, based on individual- and population-level clustering. Handcrafted radiomics and deep learning-based features were extracted from the subregions and from the whole tumor region, and were selected by least absolute shrinkage and selection operator (LASSO) regression, yielding radiomics signatures (RSs). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the developed RSs. RESULTS Each breast tumor region was partitioned into an inner subregion (S1) and a marginal subregion (S2). The RSs derived from S1 always generated higher AUCs compared with those from S2 or from the whole tumor region (W), for the external validation cohort (AUCs, S1 vs. W, handcrafted RSs: 0.583 [95% CI, 0.429-0.727] vs. 0.559 [95% CI, 0.405-0.705], p-value: 0.920; deep RSs: 0.670 [95% CI, 0.516-0.802] vs. 0.551 [95% CI, 0.397-0.698], p-value: 0.776). The fusion RSs, combining handcrafted and deep learning-based features derived from S1, yielded the highest AUCs of 0.820 (95% CI, 0.714-0.900) and 0.792 (95% CI, 0.647-0.897) for the internal and external validation cohorts, respectively. CONCLUSIONS The subregional radiomics approach can accurately predict the Ki-67 level based on DBT data; thus, it may be used as a potential non-invasive tool for preoperative treatment planning in BC.
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Affiliation(s)
- Tao Jiang
- School of Intelligent Medicine, China Medical University, Shenyang, P.R. China
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, P.R. China
| | - Shijie Chang
- School of Intelligent Medicine, China Medical University, Shenyang, P.R. China
| | - Hongbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Liaoning, P.R. China
| | - Shuxian Niu
- School of Intelligent Medicine, China Medical University, Shenyang, P.R. China
| | - Zhibin Yue
- School of Intelligent Medicine, China Medical University, Shenyang, P.R. China
| | - Huazhe Yang
- School of Intelligent Medicine, China Medical University, Shenyang, P.R. China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, P.R. China
| | - Nannan Zhao
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, P.R. China
| | - Siqi Fang
- School of Intelligent Medicine, China Medical University, Shenyang, P.R. China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, P.R. China
| | - Xiran Jiang
- School of Intelligent Medicine, China Medical University, Shenyang, P.R. China
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Fan Y, Dong Y, Yang H, Chen H, Yu Y, Wang X, Wang X, Yu T, Luo Y, Jiang X. Subregional radiomics analysis for the detection of the EGFR mutation on thoracic spinal metastases from lung cancer. Phys Med Biol 2021; 66. [PMID: 34633298 DOI: 10.1088/1361-6560/ac2ea7] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/11/2021] [Indexed: 01/20/2023]
Abstract
The present study intended to use radiomic analysis of spinal metastasis subregions to detect epidermal growth factor receptor (EGFR) mutation. In total, 94 patients with thoracic spinal metastasis originated from primary lung adenocarcinoma (2017-2020) were studied. All patients underwent T1-weighted (T1W) and T2 fat-suppressed (T2FS) MRI scans. The spinal metastases (tumor region) were subdivided into phenotypically consistent subregions based on patient- and population-level clustering: Three subregions, S1, S2 and S3, and the total tumor region. Radiomics features were extracted from each subregion and from the whole tumor region as well. Least shrinkage and selection operator (LASSO) regression were used for feature selection and radiomics signature definition. Detection performance of S3 was better than all other regions using T1W (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.720 versus 0.764 versus 0.786 versus 0.758) and T2FS (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.791 versus 0.708 versus 0.838 versus 0.797) MRI. The multi-regional radiomics signature derived from the joint of inner subregion S3 from T1W and T2FS MRI achieved the best detection capabilities with AUCs of 0.879 (ACC = 0.774, SEN = 0.838, SPE = 0.840) and 0.777 (ACC = 0.688, SEN = 0.947, SPE = 0.615) in the training and test sets, respectively. Our study revealed that MRI-based radiomic analysis of spinal metastasis subregions has the potential to detect the EGFR mutation in patients with primary lung adenocarcinoma.
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Affiliation(s)
- Ying Fan
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Huazhe Yang
- Department of Biophysics, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China
| | - Yalian Yu
- Department of Otorhinolaryngology, the First Affiliated Hospital of China Medical University, Shenyang, 110122, People's Republic of China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Xinling Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
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30
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Chen L, Liu K, Zhao X, Shen H, Zhao K, Zhu W. Habitat Imaging-Based 18F-FDG PET/CT Radiomics for the Preoperative Discrimination of Non-small Cell Lung Cancer and Benign Inflammatory Diseases. Front Oncol 2021; 11:759897. [PMID: 34692548 PMCID: PMC8526895 DOI: 10.3389/fonc.2021.759897] [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: 08/17/2021] [Accepted: 09/14/2021] [Indexed: 12/25/2022] Open
Abstract
Purpose To propose and evaluate habitat imaging-based 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics for preoperatively discriminating non-small cell lung cancer (NSCLC) and benign inflammatory diseases (BIDs). Methods Three hundred seventeen 18F-FDG PET/CT scans were acquired from patients who underwent aspiration biopsy or surgical resection. All volumes of interest (VOIs) were semiautomatically segmented. Each VOI was separated into variant subregions, namely, habitat imaging, based on our adapted clustering-based habitat generation method. Radiomics features were extracted from these subregions. Three feature selection methods and six classifiers were applied to construct the habitat imaging-based radiomics models for fivefold cross-validation. The radiomics models whose features extracted by conventional habitat-based methods and nonhabitat method were also constructed. For comparison, the performances were evaluated in the validation set in terms of the area under the receiver operating characteristic curve (AUC). Pairwise t-test was applied to test the significant improvement between the adapted habitat-based method and the conventional methods. Results A total of 1,858 radiomics features were extracted. After feature selection, habitat imaging-based 18F-FDG PET/CT radiomics models were constructed. The AUC of the adapted clustering-based habitat radiomics was 0.7270 ± 0.0147, which showed significantly improved discrimination performance compared to the conventional methods (p <.001). Furthermore, the combination of features extracted by our adaptive habitat imaging-based method and non-habitat method showed the best performance than the other combinations. Conclusion Habitat imaging-based 18F-FDG PET/CT radiomics shows potential as a biomarker for discriminating NSCLC and BIDs, which indicates that the microenvironmental variations in NSCLC and BID can be captured by PET/CT.
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Affiliation(s)
- Ling Chen
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Kanfeng Liu
- Positron Emission Tomography (PET) Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xin Zhao
- Positron Emission Tomography (PET) Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Hui Shen
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Kui Zhao
- Positron Emission Tomography (PET) Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Wentao Zhu
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
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31
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Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021; 11:1523. [PMID: 34573865 PMCID: PMC8465998 DOI: 10.3390/diagnostics11091523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/10/2021] [Accepted: 08/19/2021] [Indexed: 12/23/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.
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Affiliation(s)
- Song Li
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Yu-Qin Deng
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Zhi-Ling Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Hong-Li Hua
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
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Lin M, Wynne JF, Zhou B, Wang T, Lei Y, Curran WJ, Liu T, Yang X. Artificial intelligence in tumor subregion analysis based on medical imaging: A review. J Appl Clin Med Phys 2021; 22:10-26. [PMID: 34164913 PMCID: PMC8292694 DOI: 10.1002/acm2.13321] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 04/17/2021] [Accepted: 05/22/2021] [Indexed: 12/20/2022] Open
Abstract
Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential applications of AI in tumor subregion analysis are discussed.
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Affiliation(s)
- Mingquan Lin
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Jacob F. Wynne
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Boran Zhou
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
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Shen H, Chen L, Liu K, Zhao K, Li J, Yu L, Ye H, Zhu W. A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes. Quant Imaging Med Surg 2021; 11:2918-2932. [PMID: 34249623 DOI: 10.21037/qims-20-1182] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 03/03/2021] [Indexed: 01/06/2023]
Abstract
Background This study classifies lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using subregion-based radiomics features extracted from positron emission tomography/computed tomography (PET/CT) images. Methods In this study, the standard 18F-fluorodeoxyglucose (FDG) PET/CT images of 150 patients with lung ADC and 100 patients with SCC were retrospectively collected from the PET Center of the First Affiliated Hospital, College of Medicine, Zhejiang University. First, the 3D feature vector of each tumor voxel (whose basis is PET value, CT value, and CT local dominant orientation) was extracted. Using K-means individual clustering and population clustering, each tumor was divided into 4 subregions that reflect intratumoral regional heterogeneity. Next, based on each subregion, 385 radiomics features were extracted. Clinical features including age, gender, and smoking history were included. Thus, there were a total of 1,543 features extracted from PET/CT images and clinical reports. Statistical tests were then used to eliminate irrelevant and redundant features, and the recursive feature elimination (RFE) algorithm was used to select the best feature subset to classify SCC and ADC. Finally, 7 types of classifiers were tested to achieve the optimized model for the classification: support vector machine (SVM) with linear kernel, SVM with radial basis function kernel (SVM-RBF), random forest, logistic regression, Gaussian process classifier, linear discriminant analysis, and the AdaBoost classifier. Furthermore, 5-fold cross-validation was applied to obtain the sensitivity, specificity, accuracy, and area under the curve (AUC) for performance evaluation. Results Our model exhibited the best performance with the subregion radiomics features and SVM-RBF classifier, with a 5-fold cross-validation sensitivity, specificity, accuracy, and AUC of 0.8538, 0.8758, 0.8623, and 0.9155, respectively. The interquartile range feature from subregion 2 of CT and the gender feature from the clinical reports are the 2 optimized features that achieved the highest comprehensive score. Conclusions Our proposed model showed that SCC and ADC could be classified successfully using PET/CT images, which could be a promising tool to assist radiologists or medical physicists during diagnosis. The subregion-based method illustrated that non-small cell lung cancer (NSCLC) depicts intratumoral regional heterogeneity on both CT and PET images. By defining these heterogeneities through a subregion-based method, the diagnostic performance was improved. The 3D feature vector (whose basis is PET value, CT value, and CT local dominant orientation) showed superiority in reflecting NSCLC intratumoral regional heterogeneity.
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Affiliation(s)
- Hui Shen
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Ling Chen
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Kanfeng Liu
- PET Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Kui Zhao
- PET Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jingsong Li
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Lijuan Yu
- The Affiliated Cancer Hospital of Hainan Medical University, Haikou, China
| | - Hongwei Ye
- MinFound Medical System Co., Ltd, Shaoxing, China
| | - Wentao Zhu
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
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Bogowicz M, Pavic M, Riesterer O, Finazzi T, Garcia Schüler H, Holz-Sapra E, Rudofsky L, Basler L, Spaniol M, Ambrusch A, Hüllner M, Guckenberger M, Tanadini-Lang S. Targeting Treatment Resistance in Head and Neck Squamous Cell Carcinoma - Proof of Concept for CT Radiomics-Based Identification of Resistant Sub-Volumes. Front Oncol 2021; 11:664304. [PMID: 34123824 PMCID: PMC8191457 DOI: 10.3389/fonc.2021.664304] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/06/2021] [Indexed: 12/18/2022] Open
Abstract
Purpose Radiomics has already been proposed as a prognostic biomarker in head and neck cancer (HNSCC). However, its predictive power in radiotherapy has not yet been studied. Here, we investigated a local radiomics approach to distinguish between tumor sub-volumes with different levels of radiosensitivity as a possible target for radiation dose intensification. Materials and Methods Of 40 patients (n=28 training and n=12 validation) with biopsy confirmed locally recurrent HNSCC, pretreatment contrast-enhanced CT images were registered with follow-up PET/CT imaging allowing identification of controlled (GTVcontrol) vs non-controlled (GTVrec) tumor sub-volumes on pretreatment imaging. A bi-regional model was built using radiomic features extracted from pretreatment CT in the GTVrec and GTVcontrol to differentiate between those regions. Additionally, concept of local radiomics was implemented to perform detection task. The original tumor volume was divided into sub-volumes with no prior information on the location of recurrence. Radiomic features from those sub-volumes were then used to detect recurrent sub-volumes using multivariable logistic regression. Results Radiomic features extracted from non-controlled regions differed significantly from those in controlled regions (training AUC = 0.79 CI 95% 0.66 - 0.91 and validation AUC = 0.88 CI 95% 0.72 – 1.00). Local radiomics analysis allowed efficient detection of non-controlled sub-volumes both in the training AUC = 0.66 (CI 95% 0.56 – 0.75) and validation cohort 0.70 (CI 95% 0.53 – 0.86), however performance of this model was inferior to bi-regional model. Both models indicated that sub-volumes characterized by higher heterogeneity were linked to tumor recurrence. Conclusion Local radiomics is able to detect sub-volumes with decreased radiosensitivity, associated with location of tumor recurrence in HNSCC in the pre-treatment CT imaging. This proof of concept study, indicates that local CT radiomics can be used as predictive biomarker in radiotherapy and potential target for dose intensification.
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Affiliation(s)
- Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matea Pavic
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Oliver Riesterer
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Centre for Radiation Oncology KSA-KSB, Cantonal Hospital Aarau, Aarau, Switzerland
| | - Tobias Finazzi
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Helena Garcia Schüler
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Edna Holz-Sapra
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Leonie Rudofsky
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Lucas Basler
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Manon Spaniol
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Andreas Ambrusch
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martin Hüllner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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Wu LL, Wang JL, Huang W, Liu X, Huang YY, Zeng J, Cui CY, Lu JB, Lin P, Long H, Zhang LJ, Wei J, Lu Y, Ma GW. Prognostic Modeling of Patients Undergoing Surgery Alone for Esophageal Squamous Cell Carcinoma: A Histopathological and Computed Tomography Based Quantitative Analysis. Front Oncol 2021; 11:565755. [PMID: 33912439 PMCID: PMC8072145 DOI: 10.3389/fonc.2021.565755] [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: 11/09/2020] [Accepted: 03/17/2021] [Indexed: 12/24/2022] Open
Abstract
Objective To evaluate the effectiveness of a novel computerized quantitative analysis based on histopathological and computed tomography (CT) images for predicting the postoperative prognosis of esophageal squamous cell carcinoma (ESCC) patients. Methods We retrospectively reviewed the medical records of 153 ESCC patients who underwent esophagectomy alone and quantitatively analyzed digital histological specimens and diagnostic CT images. We cut pathological images (6000 × 6000) into 50 × 50 patches; each patient had 14,400 patches. Cluster analysis was used to process these patches. We used the pathological clusters to all patches ratio (PCPR) of each case for pathological features and we obtained 20 PCPR quantitative features. Totally, 125 computerized quantitative (20 PCPR and 105 CT) features were extracted. We used a recursive feature elimination approach to select features. A Cox hazard model with L1 penalization was used for prognostic indexing. We compared the following prognostic models: Model A: clinical features; Model B: quantitative CT and clinical features; Model C: quantitative histopathological and clinical features; and Model D: combined information of clinical, CT, and histopathology. Indices of concordance (C-index) and leave-one-out cross-validation (LOOCV) were used to assess prognostic model accuracy. Results Five PCPR and eight CT features were treated as significant indicators in ESCC prognosis. C-indices adjusted for LOOCV were comparable among four models, 0.596 (Model A) vs. 0.658 (Model B) vs. 0.651 (Model C), and improved to 0.711with Model D combining information of clinical, CT, and histopathology (all p<0.05). Using Model D, we stratified patients into low- and high-risk groups. The 3-year overall survival rates of low- and high-risk patients were 38.0% and 25.0%, respectively (p<0.001). Conclusion Quantitative prognostic modeling using a combination of clinical data, histopathological, and CT images can stratify ESCC patients with surgery alone into high-risk and low-risk groups.
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Affiliation(s)
- Lei-Lei Wu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jin-Long Wang
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Wei Huang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xuan Liu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yang-Yu Huang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jing Zeng
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Chun-Yan Cui
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jia-Bin Lu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Peng Lin
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Hao Long
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Lan-Jun Zhang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jun Wei
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | - Yao Lu
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Guo-Wei Ma
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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Peng L, Hong X, Yuan Q, Lu L, Wang Q, Chen W. Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal [18F]FDG PET/CT images. Ann Nucl Med 2021; 35:458-468. [PMID: 33543393 DOI: 10.1007/s12149-021-01585-9] [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: 09/21/2020] [Accepted: 01/11/2021] [Indexed: 12/13/2022]
Abstract
OBJECTIVES To develop a radiomics signature to predict locoregional recurrence (LR) and distant metastasis (DM), as extracted from pretreatment 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/X-ray computed tomography (PET/CT) images in locally advanced nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS Eighty-five patients with Stage III-IVB NPC underwent pretreatment [18F]FDG PET/CT scans and received radiotherapy or chemoradiotherapy. 53 of them achieved disease control, and 32 of them failed after treatment (15: LR, 17: DM). A total of 114 radiomic features were extracted from PET/CT images. For univariate analysis, Wilcoxon test and Chi-square test were used to compare median values of features between different treatment outcomes and predict the risk of treatment failure, respectively. For multivariate analysis, all features were grouped into clusters based on Pearson correlation using hierarchical clustering, and the representative feature of each cluster was chosen by the Relief algorithm. Then sequential floating forward selection (SFFS) coupled with a support vector machine (SVM) classifier were used to derive the optimized feature set in terms of the area under receiver operating characteristic (ROC) curve (AUC). The performance of the model was evaluated by leave-one-out-cross-validation, fivefold cross-validation, tenfold cross-validation. RESULTS Twenty features had significant differences between disease control and treatment failure. NPC patients with values of Compactness1, Compactness2, Coarseness_NGTDM or SGE_GLGLM above the median as well as patients with values of Irregularity, RLN_GLRLM or GLV_GLSZM below the median, showed a significant (p < 0.05) higher risk of treatment failure (about 50% vs. 25%). The derived radiomics signature consisted of 5 features with the highest AUC value of 0.8290 (sensitivity: 0.8438, specificity: 0.7736) using leave-one-out-cross-validation. CONCLUSION Locoregional recurrence (LR) and DM of locally advanced NPC can be predicted using radiomics analysis of pretreatment [18F]FDG PET/CT. The SFFS feature selection coupled with SVM classifier can derive the optimized feature set with correspondingly highest AUC value for pretreatment prediction of LR and/or DM of NPC.
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Affiliation(s)
- Lihong Peng
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Xiaotong Hong
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Qingyu Yuan
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Quanshi Wang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Wufan Chen
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China.
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Peng Z, Wang Y, Wang Y, Jiang S, Fan R, Zhang H, Jiang W. Application of radiomics and machine learning in head and neck cancers. Int J Biol Sci 2021; 17:475-486. [PMID: 33613106 PMCID: PMC7893590 DOI: 10.7150/ijbs.55716] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 12/10/2020] [Indexed: 02/07/2023] Open
Abstract
With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.
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Affiliation(s)
- Zhouying Peng
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Yaxuan Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Sijie Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Ruohao Fan
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Hua Zhang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
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Cerdá Alberich L, Sangüesa Nebot C, Alberich-Bayarri A, Carot Sierra JM, Martínez de las Heras B, Veiga Canuto D, Cañete A, Martí-Bonmatí L. A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors. Cancers (Basel) 2020; 12:cancers12123858. [PMID: 33371218 PMCID: PMC7767170 DOI: 10.3390/cancers12123858] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/15/2020] [Accepted: 12/18/2020] [Indexed: 12/11/2022] Open
Abstract
Simple Summary There is growing interest in applying quantitative diffusion techniques to magnetic resonance imaging for cancer diagnosis and treatment. These measurements are used as a surrogate marker of tumor cellularity and aggressiveness, although there may be factors that introduce some bias to these approaches. Thus, we explored a novel methodology based on confidence habitats and voxel uncertainty to improve the power of the apparent diffusion coefficient to discriminate between benign and malignant neuroblastic tumor profiles in children. We were able to show this offered an improved sensitivity and negative predictive value relative to standard voxel-based methodologies. Abstract Background/Aim: In recent years, the apparent diffusion coefficient (ADC) has been used in many oncology applications as a surrogate marker of tumor cellularity and aggressiveness, although several factors may introduce bias when calculating this coefficient. The goal of this study was to develop a novel methodology (Fit-Cluster-Fit) based on confidence habitats that could be applied to quantitative diffusion-weighted magnetic resonance images (DWIs) to enhance the power of ADC values to discriminate between benign and malignant neuroblastic tumor profiles in children. Methods: Histogram analysis and clustering-based algorithms were applied to DWIs from 33 patients to perform tumor voxel discrimination into two classes. Voxel uncertainties were quantified and incorporated to obtain a more reproducible and meaningful estimate of ADC values within a tumor habitat. Computational experiments were performed by smearing the ADC values in order to obtain confidence maps that help identify and remove noise from low-quality voxels within high-signal clustered regions. The proposed Fit-Cluster-Fit methodology was compared with two other methods: conventional voxel-based and a cluster-based strategy. Results: The cluster-based and Fit-Cluster-Fit models successfully differentiated benign and malignant neuroblastic tumor profiles when using values from the lower ADC habitat. In particular, the best sensitivity (91%) and specificity (89%) of all the combinations and methods explored was achieved by removing uncertainties at a 70% confidence threshold, improving standard voxel-based sensitivity and negative predictive values by 4% and 10%, respectively. Conclusions: The Fit-Cluster-Fit method improves the performance of imaging biomarkers in classifying pediatric solid tumor cancers and it can probably be adapted to dynamic signal evaluation for any tumor.
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Affiliation(s)
- Leonor Cerdá Alberich
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain;
- Correspondence: ; Tel.: +34-615224988
| | - Cinta Sangüesa Nebot
- Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain; (C.S.N.); (D.V.C.)
| | - Angel Alberich-Bayarri
- Quantitative Imaging Biomarkers in Medicine, QUIBIM SL. Edificio Europa, Av. d’Aragó, 30, Planta 12, 46021 Valencia, Spain;
| | - José Miguel Carot Sierra
- Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain;
| | - Blanca Martínez de las Heras
- Unidad de Oncohematología Pediátrica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain; (B.M.d.l.H.); (A.C.)
| | - Diana Veiga Canuto
- Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain; (C.S.N.); (D.V.C.)
| | - Adela Cañete
- Unidad de Oncohematología Pediátrica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain; (B.M.d.l.H.); (A.C.)
| | - Luis Martí-Bonmatí
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain;
- Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain; (C.S.N.); (D.V.C.)
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Park YW, Kang Y, Ahn SS, Ku CR, Kim EH, Kim SH, Lee EJ, Kim SH, Lee SK. Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas. Pituitary 2020; 23:691-700. [PMID: 32851505 DOI: 10.1007/s11102-020-01077-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE To investigate whether radiomic features from magnetic resonance image (MRI) can predict the granulation pattern of growth hormone (GH)-secreting pituitary adenoma patients. METHODS Sixty-nine pathologically proven acromegaly patients (densely granulated [DG] = 50, sparsely granulated [SG] = 19) were included. Radiomic features (n = 214) were extracted from contrast-enhancing and total tumor portions from T2-weighted (T2) MRIs. Imaging features were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression model with fivefold cross-validation. Diagnostic performance for predicting granulation pattern was compared with that for qualitative T2 signal intensity assessment and T2 relative signal intensity (rSI) using the area under the receiver operating characteristics curve (AUC). RESULTS Four significant radiomic features from the contrast-enhancing tumor (1 from shape, 1 from first order feature, and 2 from second order features) were selected by LASSO for model construction. The radiomics model showed an AUC, accuracy, sensitivity, and specificity of 0.834 (95% confidence interval [CI] 0.738-0.930), 73.7%, 74.0%, and 73.9%, respectively. The radiomics model showed significantly better performance than the model using qualitative T2 signal intensity assessment (AUC 0.597 [95% CI 0.447-0.747], P = 0.009) and T2 rSI (AUC 0.647 [95% CI 0.523-0.759], P = 0.037). CONCLUSION Radiomic features may be useful biomarkers to differentiate granulation pattern of GH-secreting pituitary adenoma patients, and showed better performance than qualitative assessment or rSI evaluation.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
| | - Yunjun Kang
- Integrated Science and Engineering Division, Underwood International College, Yonsei University, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
| | - Cheol Ryong Ku
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
- Department of Endocrinology, Yonsei University College of Medicine, Seoul, Korea
| | - Eui Hyun Kim
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea.
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Eun Jig Lee
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
- Department of Endocrinology, Yonsei University College of Medicine, Seoul, Korea
| | - Sun Ho Kim
- Department of Neurosurgery, Ewha Womans University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
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Gu B, Zhang J, Ma G, Song S, Shi L, Zhang Y, Yang Z. Establishment and validation of a nomogram with intratumoral heterogeneity derived from 18F-FDG PET/CT for predicting individual conditional risk of 5-year recurrence before initial treatment of nasopharyngeal carcinoma. BMC Cancer 2020; 20:37. [PMID: 31941465 PMCID: PMC6964088 DOI: 10.1186/s12885-020-6520-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 01/07/2020] [Indexed: 02/08/2023] Open
Abstract
Background Intratumoral heterogeneity has an enormous effect on patient treatment and outcome. The purpose of the current study was to establish and validate a nomogram with intratumoral heterogeneity derived from 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for prognosis of 5-Year progression-free survival (PFS) of patients with nasopharyngeal carcinoma (NPC). Methods A total of 171 NPC patients who underwent pretreatment 18F-FDG PET/CT were retrospectively enrolled. Data was randomly divided into training cohort (n = 101) and validation cohort (n = 70). The clinicopathologic parameters and the following PET parameters were analyzed: maximum and mean standardized uptake value (SUVmax, SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity index (HI, SUVmax/SUVmean) for primary tumor and maximal neck lymph node. Cox analyses were performed on PFS in the training cohort. A prognostic nomogram based on this model was developed and validated. Results For the primary tumor, MTV-2.5, TLG-2.5, MTV-70%, and TLG-70% were significantly correlated with PFS. For the maximal neck lymph node, short diameter and HI were significantly correlated with PFS. Among the clinicopathologic parameters, M stage was a significant prognostic factor for recurrence. In multivariate analysis, M stage (P = 0.006), TLG-T-70% (P = 0.002), and HI-N (P = 0.018) were independent predictors. Based on this prognostic model, a nomogram was generated. The C-index of this model was 0.74 (95% CI: 0.63–0.85). For the cross validation, the C-index for the model was 0.73 (95% CI: 0.62–0.83) with the validation cohort. Patients with a risk score of ≥111 had poorer survival outcomes than those with a risk score of 0–76 and 77–110. Conclusions Intratumoral heterogeneity derived from 18F-FDG PET/CT could predict long-term outcome in patients with primary NPC. A combination of PET parameters and the TNM stage enables better stratification of patients into subgroups with different PFS rates.
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Affiliation(s)
- Bingxin Gu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Shanghai, 200032, Xuhui District, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Center for Biomedical Imaging, Fudan University, Shanghai, 200032, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, 200032, China
| | - Jianping Zhang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Shanghai, 200032, Xuhui District, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Center for Biomedical Imaging, Fudan University, Shanghai, 200032, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, 200032, China.,Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Fudan University, Shanghai, 200433, China.,Department of Nuclear Science and Technology, Fudan University, Shanghai, 200433, China
| | - Guang Ma
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Shanghai, 200032, Xuhui District, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Center for Biomedical Imaging, Fudan University, Shanghai, 200032, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, 200032, China
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Shanghai, 200032, Xuhui District, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Center for Biomedical Imaging, Fudan University, Shanghai, 200032, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, 200032, China
| | - Liqun Shi
- Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Fudan University, Shanghai, 200433, China.,Department of Nuclear Science and Technology, Fudan University, Shanghai, 200433, China
| | - Yingjian Zhang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Shanghai, 200032, Xuhui District, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Center for Biomedical Imaging, Fudan University, Shanghai, 200032, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, 200032, China
| | - Zhongyi Yang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Shanghai, 200032, Xuhui District, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China. .,Center for Biomedical Imaging, Fudan University, Shanghai, 200032, China. .,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, 200032, China.
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