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Chen Y, Fan Z, Luo Z, Kang X, Wan R, Li F, Lin W, Han Z, Qi B, Lin J, Sun Y, Huang J, Xu Y, Chen S. Impacts of Nutlin-3a and exercise on murine double minute 2-enriched glioma treatment. Neural Regen Res 2025; 20:1135-1152. [PMID: 38989952 DOI: 10.4103/nrr.nrr-d-23-00875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 12/21/2023] [Indexed: 07/12/2024] Open
Abstract
JOURNAL/nrgr/04.03/01300535-202504000-00029/figure1/v/2024-07-06T104127Z/r/image-tiff Recent research has demonstrated the impact of physical activity on the prognosis of glioma patients, with evidence suggesting exercise may reduce mortality risks and aid neural regeneration. The role of the small ubiquitin-like modifier (SUMO) protein, especially post-exercise, in cancer progression, is gaining attention, as are the potential anti-cancer effects of SUMOylation. We used machine learning to create the exercise and SUMO-related gene signature (ESLRS). This signature shows how physical activity might help improve the outlook for low-grade glioma and other cancers. We demonstrated the prognostic and immunotherapeutic significance of ESLRS markers, specifically highlighting how murine double minute 2 (MDM2), a component of the ESLRS, can be targeted by nutlin-3. This underscores the intricate relationship between natural compounds such as nutlin-3 and immune regulation. Using comprehensive CRISPR screening, we validated the effects of specific ESLRS genes on low-grade glioma progression. We also revealed insights into the effectiveness of Nutlin-3a as a potent MDM2 inhibitor through molecular docking and dynamic simulation. Nutlin-3a inhibited glioma cell proliferation and activated the p53 pathway. Its efficacy decreased with MDM2 overexpression, and this was reversed by Nutlin-3a or exercise. Experiments using a low-grade glioma mouse model highlighted the effect of physical activity on oxidative stress and molecular pathway regulation. Notably, both physical exercise and Nutlin-3a administration improved physical function in mice bearing tumors derived from MDM2-overexpressing cells. These results suggest the potential for Nutlin-3a, an MDM2 inhibitor, with physical exercise as a therapeutic approach for glioma management. Our research also supports the use of natural products for therapy and sheds light on the interaction of exercise, natural products, and immune regulation in cancer treatment.
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Affiliation(s)
- Yisheng Chen
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhongcheng Fan
- Department of Orthopedic Surgery, Hainan Province Clinical Medical Center, Haikou Affiliated Hospital of Central South University Xiangya School of Medicine, Haikou, Hainan Province, China
| | - Zhiwen Luo
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xueran Kang
- Department of Basic Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Renwen Wan
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Fangqi Li
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Weiwei Lin
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Zhihua Han
- Department of Orthopedics, Shanghai General Hospital, School of Medicine Shanghai Jiao Tong University, Shanghai, China
| | - Beijie Qi
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinrong Lin
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yaying Sun
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiebin Huang
- Department of Infectious Diseases, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Yuzhen Xu
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong Province, China
| | - Shiyi Chen
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
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2
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Hwang KP, Elshafeey NA, Kotrotsou A, Chen H, Son JB, Boge M, Mohamed RM, Abdelhafez AH, Adrada BE, Panthi B, Sun J, Musall BC, Zhang S, Candelaria RP, White JB, Ravenberg EE, Tripathy D, Yam C, Litton JK, Huo L, Thompson AM, Wei P, Yang WT, Pagel MD, Ma J, Rauch GM. A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer. Radiol Imaging Cancer 2023; 5:e230009. [PMID: 37505106 PMCID: PMC10413296 DOI: 10.1148/rycan.230009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/18/2023] [Accepted: 06/03/2023] [Indexed: 07/29/2023]
Abstract
Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.
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Affiliation(s)
- Ken-Pin Hwang
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Nabil A. Elshafeey
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Aikaterini Kotrotsou
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Huiqin Chen
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jong Bum Son
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Medine Boge
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Rania M. Mohamed
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Abeer H. Abdelhafez
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Beatriz E. Adrada
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Bikash Panthi
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jia Sun
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Benjamin C. Musall
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Shu Zhang
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Rosalind P. Candelaria
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jason B. White
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Elizabeth E. Ravenberg
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Debu Tripathy
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Clinton Yam
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jennifer K. Litton
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Lei Huo
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Alastair M. Thompson
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Peng Wei
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Wei T. Yang
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Mark D. Pagel
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jingfei Ma
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Gaiane M. Rauch
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
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3
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Duffau H. Oncological and functional neurosurgery: Perspectives for the decade regarding diffuse gliomas. Rev Neurol (Paris) 2023; 179:437-448. [PMID: 36907710 DOI: 10.1016/j.neurol.2023.01.724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 01/17/2023] [Accepted: 01/22/2023] [Indexed: 03/12/2023]
Abstract
For decades, diffuse glioma (DG) studies mostly focused on oncological considerations, whereas functional outcomes received less attention. Currently, because overall survival has increased in DG, especially in low-grade glioma (overall survival > 15 years), quality of life including neurocognitive and behavioral aspects should be assessed and preserved more systematically, particularly regarding surgery. Indeed, early maximal tumor removal results in greater survival in both high-grade and low-grade gliomas, leading to propose "supra-marginal" resection, with excision of the peritumoral zone in diffuse neoplasms. To minimize functional risks while maximizing the extent of resection, traditional "tumor-mass resection" is replaced by "connectome-guided resection" conducted under awake mapping, taking into account inter-individual brain anatomo-functional variability. A better understanding of the dynamic interplay between DG progression and reactional neuroplastic mechanisms is critical to adapt a personalized multistage therapeutic strategy, with integration of functional neurooncological (re)operation(s) in a multimodal management scheme including repeated medical therapies. Because the therapeutic armamentarium remains limited, the aims of this paradigmatic shift are to predict one/several step(s) ahead glioma behavior, its modifications, and compensatory neural networks reconfiguration over time in order to optimize the onco-functional benefit of each treatment - either in isolation or in combination with others - in human beings bearing a chronic tumoral disease while enjoying an active familial and socio-professional life as close as possible to their expectations. Thus, new ecological endpoints such as return to work should be incorporated into future DG trials. "Preventive neurooncology" might also be envisioned, by proposing a screening policy to discover and treat incidental glioma earlier.
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Affiliation(s)
- H Duffau
- Department of Neurosurgery, Montpellier University Medical Center, Gui-de-Chauliac Hospital, 80, avenue Augustin-Fliche, 34295 Montpellier, France; Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors", National Institute for Health and Medical Research (Inserm), U1191 Laboratory, Institute of Functional Genomics, University of Montpellier, 34091 Montpellier, France.
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4
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Long H, Zhang P, Bi Y, Yang C, Wu M, He D, Huang S, Yang K, Qi S, Wang J. MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme. Front Oncol 2023; 12:1042498. [PMID: 36686829 PMCID: PMC9845721 DOI: 10.3389/fonc.2022.1042498] [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: 09/12/2022] [Accepted: 12/02/2022] [Indexed: 01/05/2023] Open
Abstract
Background and purpose As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma(GBM), which may be of value to optimize patients' management. Materials and methods The clinical information and MR images (contrast-enhanced T1 weighted and FLAIR sequences) of 22 patients who have been histologically proven glioblastoma, were retrospectively evaluated. Kaplan-Meier methods was used for survival analysis. Oedematous regions were manually segmented by an expert into recurrence region, non-recurrence region. A set of 94 radiomic features were obtained from each region using the function of analyzing MR image of 3D slicer. Paired t test was performed to identify the features existing significant difference. Subsequently, the data of two patients from TCGA database was used to evaluate whether these features have clinical value. Results Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from the TCGA database with pathologically confirmed diagnosis of GBM. Conclusions Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM.
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Affiliation(s)
- Hao Long
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China,The First Clinical Medicine College, Southern Medical University, Guangzhou, China
| | - Ping Zhang
- Department of oncology, Guangdong 999 Brain Hospital, Guangzhou, China
| | - Yuewei Bi
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Chen Yang
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Manfeng Wu
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Dian He
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Shaozhuo Huang
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Kaijun Yang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China,The First Clinical Medicine College, Southern Medical University, Guangzhou, China
| | - Songtao Qi
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China,The First Clinical Medicine College, Southern Medical University, Guangzhou, China
| | - Jun Wang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China,The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China,*Correspondence: Jun Wang,
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5
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Machine learning–based multiparametric magnetic resonance imaging radiomics model for distinguishing central neurocytoma from glioma of lateral ventricle. Eur Radiol 2022; 33:4259-4269. [PMID: 36547672 DOI: 10.1007/s00330-022-09319-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 10/16/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To develop a machine learning-based radiomics model based on multiparametric magnetic resonance imaging (MRI) for preoperative discrimination between central neurocytomas (CNs) and gliomas of lateral ventricles. METHODS A total of 132 patients from two medical centers were enrolled in this retrospective study. Patients from the first medical center were divided into a training cohort (n = 74) and an internal validation cohort (n = 30). Patients from the second medical center were used as the external validation cohort (n = 28). Features were extracted from contrast-enhanced T1-weighted and T2-weighted images. A support vector machine was used for radiomics model investigation. Performance was evaluated using the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The model's performance was also compared with those of three radiologists. RESULTS The radiomics model achieved an AUC of 0.986 in the training cohort, 0.933 in the internal validation cohort, and 0.903 in the external validation cohort. In the three cohorts, the AUC values were 0.657, 0.786, and 0.708 for radiologist 1; 0.838, 0.799, and 0.790 for radiologist 2; and 0.827, 0.871, and 0.862 for radiologist 3. When assisted by the radiomics model, two radiologists improved their performance in the training cohort (p < 0.05) but not in the internal or external validation cohorts. CONCLUSIONS The machine learning radiomics model based on multiparametric MRI showed better performance for distinguishing CNs from lateral ventricular gliomas than did experienced radiologists, and it showed the potential to improve radiologist performance. KEY POINTS • The machine learning radiomics model shows excellent performance in distinguishing CNs from gliomas. • The radiomics model outweighs two experienced radiologists (area under the receiver operating characteristic curve, 0.90 vs 0.79 and 0.86, respectively). • The radiomics model has the potential to enhance radiologist performance.
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6
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Duffau H. A Personalized Longitudinal Strategy in Low-Grade Glioma Patients: Predicting Oncological and Neural Interindividual Variability and Its Changes over Years to Think One Step Ahead. J Pers Med 2022; 12:jpm12101621. [PMID: 36294760 PMCID: PMC9604939 DOI: 10.3390/jpm12101621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 09/15/2022] [Accepted: 09/22/2022] [Indexed: 11/09/2022] Open
Abstract
Diffuse low-grade glioma (LGG) is a rare cerebral cancer, mostly involving young adults with an active life at diagnosis. If left untreated, LGG widely invades the brain and becomes malignant, generating neurological worsening and ultimately death. Early and repeat treatments for this incurable tumor, including maximal connectome-based surgical resection(s) in awake patients, enable postponement of malignant transformation while preserving quality of life owing to constant neural network reconfiguration. Due to considerable interindividual variability in terms of LGG course and consecutive cerebral reorganization, a multistage longitudinal strategy should be tailored accordingly in each patient. It is crucial to predict how the glioma will progress (changes in growth rate and pattern of migration, genetic mutation, etc.) and how the brain will adapt (changes in patterns of spatiotemporal redistribution, possible functional consequences such as epilepsy or cognitive decline, etc.). The goal is to anticipate therapeutic management, remaining one step ahead in order to select the optimal (re-)treatment(s) (some of them possibly kept in reserve), at the appropriate time(s) in the evolution of this chronic disease, before malignization and clinical worsening. Here, predictive tumoral and non-tumoral factors, and their ever-changing interactions, are reviewed to guide individual decisions in advance based on patient-specific markers, for the treatment of LGG.
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Affiliation(s)
- Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, 80 Av. Augustin Fliche, 34295 Montpellier, France; ; Tel.: +33-4-67-33-66-12; Fax: +33-4-67-33-69-12
- Team “Plasticity of Central Nervous System, Stem Cells and Glial Tumors”, National Institute for Health and Medical Research (INSERM), U1191 Laboratory, Institute of Functional Genomics, University of Montpellier, 34091 Montpellier, France
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7
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Borja AJ, Saini J, Raynor WY, Ayubcha C, Werner TJ, Alavi A, Revheim ME, Nagaraj C. Role of Molecular Imaging with PET/MR Imaging in the Diagnosis and Management of Brain Tumors. PET Clin 2022; 17:431-451. [PMID: 35662494 DOI: 10.1016/j.cpet.2022.03.002] [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] [Indexed: 11/29/2022]
Abstract
Gliomas are the most common primary brain tumors. Hybrid PET/MR imaging has revolutionized brain tumor imaging, allowing for noninvasive, simultaneous assessment of morphologic, functional, metabolic, and molecular parameters within the brain. Molecular information obtained from PET imaging may aid in the detection, classification, prognostication, and therapeutic decision making for gliomas. 18F-fluorodeoxyglucose (FDG) has been widely used in the setting of brain tumor imaging, and multiple techniques may be employed to optimize this methodology. More recently, a number of non-18F-FDG-PET radiotracers have been applied toward brain tumor imaging and are used in clinical practice.
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Affiliation(s)
- Austin J Borja
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Jitender Saini
- Department of Neuro Imaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Hosur Road, Bengaluru, Karnataka 560-029, India
| | - William Y Raynor
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Cyrus Ayubcha
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Thomas J Werner
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Abass Alavi
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Mona-Elisabeth Revheim
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Sognsvannsveien 20, Oslo 0372, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Problemveien 7, Oslo 0315, Norway
| | - Chandana Nagaraj
- Department of Neuro Imaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Hosur Road, Bengaluru, Karnataka 560-029, India.
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8
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Silva M, Vivancos C, Duffau H. The Concept of «Peritumoral Zone» in Diffuse Low-Grade Gliomas: Oncological and Functional Implications for a Connectome-Guided Therapeutic Attitude. Brain Sci 2022; 12:brainsci12040504. [PMID: 35448035 PMCID: PMC9032126 DOI: 10.3390/brainsci12040504] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/10/2022] [Accepted: 04/12/2022] [Indexed: 12/22/2022] Open
Abstract
Diffuse low-grade gliomas (DLGGs) are heterogeneous and poorly circumscribed neoplasms with isolated tumor cells that extend beyond the margins of the lesion depicted on MRI. Efforts to demarcate the glioma core from the surrounding healthy brain led us to define an intermediate region, the so-called peritumoral zone (PTZ). Although most studies about PTZ have been conducted on high-grade gliomas, the purpose here is to review the cellular, metabolic, and radiological characteristics of PTZ in the specific context of DLGG. A better delineation of PTZ, in which glioma cells and neural tissue strongly interact, may open new therapeutic avenues to optimize both functional and oncological results. First, a connectome-based “supratotal” surgical resection (i.e., with the removal of PTZ in addition to the tumor core) resulted in prolonged survival by limiting the risk of malignant transformation, while improving the quality of life, thanks to a better control of seizures. Second, the timing and order of (neo)adjuvant medical treatments can be modulated according to the pattern of peritumoral infiltration. Third, the development of new drugs specifically targeting the PTZ could be considered from an oncological (such as immunotherapy) and epileptological perspective. Further multimodal investigations of PTZ are needed to maximize long-term outcomes in DLGG patients.
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Affiliation(s)
- Melissa Silva
- Department of Neurosurgery, Hospital Garcia de Orta, 2805-267 Almada, Portugal;
| | - Catalina Vivancos
- Department of Neurosurgery, Hospital Universitario La Paz, 28046 Madrid, Spain;
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, 34295 Montpellier, France
- Team “Plasticity of Central Nervous System, Stem Cells and Glial Tumors”, Institute of Functional Genomics, National Institute for Health and Medical Research (INSERM) U1191, University of Montpellier, 34295 Montpellier, France
- Correspondence:
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9
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Deciphering the glioblastoma phenotype by computed tomography radiomics. Radiother Oncol 2021; 160:132-139. [PMID: 33984349 DOI: 10.1016/j.radonc.2021.05.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 04/19/2021] [Accepted: 05/03/2021] [Indexed: 12/23/2022]
Abstract
INTRODUCTION Glioblastoma (GBM) is the most common malignant primary brain tumour which has, despite extensive treatment, a median overall survival of 15 months. Radiomics is the high-throughput extraction of large amounts of image features from radiographic images, which allows capturing the tumour phenotype in 3D and in a non-invasive way. In this study we assess the prognostic value of CT radiomics for overall survival in patients with a GBM. MATERIALS AND METHODS Clinical data and pre-treatment CT images were obtained from 218 patients diagnosed with a GBM via biopsy who underwent radiotherapy +/- temozolomide between 2004 and 2015 treated at three independent institutes (n = 93, 62 and 63). A clinical prognostic score (CPS), a simple radiomics model consisting of volume based score (VPS), a complex radiomics prognostic score (RPS) and a combined clinical and radiomics (C + R)PS model were developed. The population was divided into three risk groups for each prognostic score and respective Kaplan-Meier curves were generated. RESULTS Patient characteristics were broadly comparable. Clinically significant differences were observed with regards to radiation dose, tumour volume and performance status between datasets. Image acquisition parameters differed between institutes. The cross-validated c-indices were moderately discriminative and for the CPS ranged from 0.63 to 0.65; the VPS c-indices ranged between 0.52 and 0.61; the RPS c-indices ranged from 0.57 to 0.64 and the combined clinical and radiomics model resulted in c-indices of 0.59-0.71. CONCLUSION In this study clinical and CT radiomics features were used to predict OS in GBM. Discrimination between low-, middle- and high-risk patients based on the combined clinical and radiomics model was comparable to previous MRI-based models.
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10
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Li ZZ, Liu PF, An TT, Yang HC, Zhang W, Wang JX. Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients. Transl Oncol 2021; 14:101065. [PMID: 33761371 PMCID: PMC8020484 DOI: 10.1016/j.tranon.2021.101065] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 02/25/2021] [Accepted: 03/02/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND This study aimed to identify a series of prognostically relevant immune features by immunophenoscore. Immune features were explored using MRI radiomics features to prediction the overall survival (OS) of lower-grade glioma (LGG) patients and their response to immune checkpoints. METHOD LGG data were retrieved from TCGA and categorized into training and internal validation datasets. Patients attending the First Affiliated Hospital of Harbin Medical University were included in an external validation cohort. An immunophenoscore-based signature was built to predict malignant potential and response to immune checkpoint inhibitors in LGG patients. In addition, a deep learning neural network prediction model was built for validation of the immunophenoscore-based signature. RESULTS Immunophenotype-associated mRNA signatures (IMriskScore) for outcome prediction and ICB therapeutic effects in LGG patients were constructed. Deep learning of neural networks based on radiomics showed that MRI radiomic features determined IMriskScore. Enrichment analysis and ssGSEA correlation analysis were performed. Mutations in CIC significantly improved the prognosis of patients in the high IMriskScore group. Therefore, CIC is a potential therapeutic target for patients in the high IMriskScore group. Moreover, IMriskScore is an independent risk factor that can be used clinically to predict LGG patient outcomes. CONCLUSIONS The IMriskScore model consisting of a sets of biomarkers, can independently predict the prognosis of LGG patients and provides a basis for the development of personalized immunotherapy strategies. In addition, IMriskScore features were predicted by MRI radiomics using a deep learning approach using neural networks. Therefore, they can be used for the prognosis of LGG patients.
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Affiliation(s)
- Zi-Zhuo Li
- Department of Abdominal Ultrasound, The First Affiliated Hospital of Harbin Medical University China
| | - Peng-Fei Liu
- Department of Magnetic Resonance, The First Affiliated Hospital of Harbin Medical University China.
| | - Ting-Ting An
- Department of Abdominal Ultrasound, The First Affiliated Hospital of Harbin Medical University China
| | - Hai-Chao Yang
- Department of Abdominal Ultrasound, The First Affiliated Hospital of Harbin Medical University China
| | - Wei Zhang
- Department of Abdominal Ultrasound, The First Affiliated Hospital of Harbin Medical University China
| | - Jia-Xu Wang
- Department of Abdominal Ultrasound, The First Affiliated Hospital of Harbin Medical University China
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Lombardi G, Barresi V, Castellano A, Tabouret E, Pasqualetti F, Salvalaggio A, Cerretti G, Caccese M, Padovan M, Zagonel V, Ius T. Clinical Management of Diffuse Low-Grade Gliomas. Cancers (Basel) 2020; 12:E3008. [PMID: 33081358 PMCID: PMC7603014 DOI: 10.3390/cancers12103008] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/06/2020] [Accepted: 10/14/2020] [Indexed: 12/21/2022] Open
Abstract
Diffuse low-grade gliomas (LGG) represent a heterogeneous group of primary brain tumors arising from supporting glial cells and usually affecting young adults. Advances in the knowledge of molecular profile of these tumors, including mutations in the isocitrate dehydrogenase genes, or 1p/19q codeletion, and in neuroradiological techniques have contributed to the diagnosis, prognostic stratification, and follow-up of these tumors. Optimal post-operative management of LGG is still controversial, though radiation therapy and chemotherapy remain the optimal treatments after surgical resection in selected patients. In this review, we report the most important and recent research on clinical and molecular features, new neuroradiological techniques, the different therapeutic modalities, and new opportunities for personalized targeted therapy and supportive care.
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Affiliation(s)
- Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Valeria Barresi
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37129 Verona, Italy;
| | - Antonella Castellano
- Neuroradiology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, 20132 Milan, Italy;
| | - Emeline Tabouret
- Team 8 GlioMe, CNRS, INP, Inst Neurophysiopathol, Aix-Marseille University, 13005 Marseille, France;
| | | | - Alessandro Salvalaggio
- Department of Neuroscience, University of Padova, 35128 Padova, Italy;
- Padova Neuroscience Center (PNC), University of Padova, 35128 Padova, Italy
| | - Giulia Cerretti
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Mario Caccese
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Marta Padovan
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Vittorina Zagonel
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Tamara Ius
- Neurosurgery Unit, Department of Neurosciences, Santa Maria della Misericordia University Hospital, 33100 Udine, Italy;
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12
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Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors. Eur J Nucl Med Mol Imaging 2020; 48:683-693. [PMID: 32979059 DOI: 10.1007/s00259-020-05037-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 09/11/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE This is a radiomics study investigating the ability of texture analysis of MRF maps to improve differentiation between intra-axial adult brain tumors and to predict survival in the glioblastoma cohort. METHODS Magnetic resonance fingerprinting (MRF) acquisition was performed on 31 patients across 3 groups: 17 glioblastomas, 6 low-grade gliomas, and 8 metastases. Using regions of interest for the solid tumor and peritumoral white matter on T1 and T2 maps, second-order texture features were calculated from gray-level co-occurrence matrices and gray-level run length matrices. Selected features were compared across the three tumor groups using Wilcoxon rank-sum test. Receiver operating characteristic curve analysis was performed for each feature. Kaplan-Meier method was used for survival analysis with log rank tests. RESULTS Low-grade gliomas and glioblastomas had significantly higher run percentage, run entropy, and information measure of correlation 1 on T1 than metastases (p < 0.017). The best separation of all three tumor types was seen utilizing inverse difference normalized and homogeneity values for peritumoral white matter in both T1 and T2 maps (p < 0.017). In solid tumor T2 maps, lower values in entropy and higher values of maximum probability and high-gray run emphasis were associated with longer survival in glioblastoma patients (p < 0.05). Several texture features were associated with longer survival in glioblastoma patients on peritumoral white matter T1 maps (p < 0.05). CONCLUSION Texture analysis of MRF-derived maps can improve our ability to differentiate common adult brain tumors by characterizing tumor heterogeneity, and may have a role in predicting outcomes in patients with glioblastoma.
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A radiomics-clinical nomogram for preoperative prediction of IDH1 mutation in primary glioblastoma multiforme. Clin Radiol 2020; 75:963.e7-963.e15. [PMID: 32921406 DOI: 10.1016/j.crad.2020.07.036] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 07/31/2020] [Indexed: 02/08/2023]
Abstract
AIM To develop and validate an individualised radiomics-clinical nomogram for the prediction of the isocitrate dehydrogenase 1 (IDH1) mutation status in primary glioblastoma multiforme (GBM) based on radiomics features and clinical variables. MATERIALS AND METHODS In a retrospective study, preoperative magnetic resonance imaging (MRI) images were obtained of 122 patients with primary glioblastoma (development cohort = 101; validation cohort = 21). Radiomics features were extracted from total tumour based on the post-contrast high-resolution three-dimensional (3D) T1-weighted MRI images. Radiomics features were selected by using a least absolute shrinkage and selection operator (LASSO) binomial regression model with nested cross-validation. Then, a radiomics-clinical nomogram was constructed by combining relevant radiomics features and clinical variables and subsequently tested by using the independent validation cohort. RESULTS A total of 105 features were quantified on the 3D MRI images of each patient, and eight were selected to construct the radiomics model for predicting IDH1 mutation status. The mean classification accuracy and mean κ value achieved with the model were 88.4±3% and 0.701±0.08, respectively. The radiomics-clinical nomogram, which combines eight radiomics features and three clinical variables (patient age, sex and tumour location), demonstrated good discrimination (C-index 0.934 [95% CI, 0.874 to 0.994]; F1 score 0.78) and performed well with the validation cohort (C-index 0.963 [95% CI, 0.957 to 0.969]; F1 score 0.91; AUC 0.956). CONCLUSIONS A radiomics-clinical nomogram was developed and proved to be valuable in the non-invasive, individualised prediction of the IDH1 mutation status in patients with primary GBM. The nomogram can be applied using clinical conditions to facilitate preoperative patient evaluation.
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14
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Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis. Neuroradiology 2020; 62:771-790. [DOI: 10.1007/s00234-020-02403-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 03/10/2020] [Indexed: 12/14/2022]
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AlRayahi J, Zapotocky M, Ramaswamy V, Hanagandi P, Branson H, Mubarak W, Raybaud C, Laughlin S. Pediatric Brain Tumor Genetics: What Radiologists Need to Know. Radiographics 2019; 38:2102-2122. [PMID: 30422762 DOI: 10.1148/rg.2018180109] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Brain tumors are the most common solid tumors in the pediatric population. Pediatric neuro-oncology has changed tremendously during the past decade owing to ongoing genomic advances. The diagnosis, prognosis, and treatment of pediatric brain tumors are now highly reliant on the genetic profile and histopathologic features of the tumor rather than the histopathologic features alone, which previously were the reference standard. The clinical information expected to be gleaned from radiologic interpretations also has evolved. Imaging is now expected to not only lead to a relevant short differential diagnosis but in certain instances also aid in predicting the specific tumor and subtype and possibly the prognosis. These processes fall under the umbrella of radiogenomics. Therefore, to continue to actively participate in patient care and/or radiogenomic research, it is important that radiologists have a basic understanding of the molecular mechanisms of common pediatric central nervous system tumors. The genetic features of pediatric low-grade gliomas, high-grade gliomas, medulloblastomas, and ependymomas are reviewed; differences between pediatric and adult gliomas are highlighted; and the critical oncogenic pathways of each tumor group are described. The role of the mitogen-activated protein kinase pathway in pediatric low-grade gliomas and of histone mutations as epigenetic regulators in pediatric high-grade gliomas is emphasized. In addition, the oncogenic drivers responsible for medulloblastoma, the classification of ependymomas, and the associated imaging correlations and clinical implications are discussed. ©RSNA, 2018.
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Affiliation(s)
- Jehan AlRayahi
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Michal Zapotocky
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Vijay Ramaswamy
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Prasad Hanagandi
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Helen Branson
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Walid Mubarak
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Charles Raybaud
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Suzanne Laughlin
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
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16
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Lee TH, Sung WW, Chan L, Lee HL, Chen SL, Huang YH, Kwan AL. The Association between Mortality-to-Incidence Ratios and Health Expenditures in Brain and Nervous System Cancers. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16152739. [PMID: 31370357 PMCID: PMC6696604 DOI: 10.3390/ijerph16152739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 07/18/2019] [Accepted: 07/27/2019] [Indexed: 12/24/2022]
Abstract
Mortality-to-incidence ratios (MIRs) are alternative parameters used to evaluate the prognosis of a disease. In addition, MIRs are associated with the ranking of health care systems and expenditures for certain types of cancer. However, a lack of association between MIRs and pancreatic cancer has been noted. Given the poor prognosis of brain and nervous system cancers, similar to pancreatic cancer, the relation of MIRs and health care disparities is worth investigating. We used the Spearman's rank correlation coefficient (CC) to analyze the correlation between the MIRs in brain and nervous system cancers and inter-country disparities, including expenditures on health and human development index. Interestingly, the MIRs in brain and nervous system cancers are associated with the human development index score (N = 157, CC = -0.394, p < 0.001), current health expenditure (CHE) per capita (N = 157, CC = -0.438, p < 0.001), and CHE as percentage of gross domestic product (N = 157, CC = -0.245, p = 0.002). In conclusion, the MIRs in the brain and nervous system cancer are significantly associated with health expenditures and human development index. However, their role as an indicator of health disparity warrants further investigation.
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Affiliation(s)
- Tsung-Han Lee
- Division of Neurosurgery, Department of Surgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Wen-Wei Sung
- Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
- Department of Urology, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
| | - Lung Chan
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Hsiang-Lin Lee
- Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
- Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
| | - Sung-Lang Chen
- Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
- Department of Urology, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
| | - Yu-Hui Huang
- Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan.
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan.
- Department of Physical Medicine & Rehabilitation, Chung Shan Medical University Hospital, Taichung 40201, Taiwan.
| | - Aij-Lie Kwan
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.
- Department of Neurosurgery, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan.
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Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma. Nat Commun 2019; 10:3170. [PMID: 31320621 PMCID: PMC6639324 DOI: 10.1038/s41467-019-11007-0] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 06/07/2019] [Indexed: 01/04/2023] Open
Abstract
Pseudoprogression (PsP) is a diagnostic clinical dilemma in cancer. In this study, we retrospectively analyse glioblastoma patients, and using their dynamic susceptibility contrast and dynamic contrast-enhanced perfusion MRI images we build a classifier using radiomic features obtained from both Ktrans and rCBV maps coupled with support vector machines. We achieve an accuracy of 90.82% (area under the curve (AUC) = 89.10%, sensitivity = 91.36%, 67 specificity = 88.24%, p = 0.017) in differentiating between pseudoprogression (PsP) and progressive disease (PD). The diagnostic performances of the models built using radiomic features from Ktrans and rCBV separately were equally high (Ktrans: AUC = 94%, 69 p = 0.012; rCBV: AUC = 89.8%, p = 0.004). Thus, this MR perfusion-based radiomic model demonstrates high accuracy, sensitivity and specificity in discriminating PsP from PD, thus provides a reliable alternative for noninvasive identification of PsP versus PD at the time of clinical/radiologic question. This study also illustrates the successful application of radiomic analysis as an advanced processing step on different MR perfusion maps. MRI scans of glioblastoma patients can be misleading and some patients appear to show features of progressive disease although they respond to treatment. Here, the authors use MRI images of progressive disease or pseudoprogression and build a classifier using machine learning to distinguish the two.
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18
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Pérez-Beteta J, Molina-García D, Villena M, Rodríguez MJ, Velásquez C, Martino J, Meléndez-Asensio B, Rodríguez de Lope Á, Morcillo R, Sepúlveda JM, Hernández-Laín A, Ramos A, Barcia JA, Lara PC, Albillo D, Revert A, Arana E, Pérez-García VM. Morphologic Features on MR Imaging Classify Multifocal Glioblastomas in Different Prognostic Groups. AJNR Am J Neuroradiol 2019; 40:634-640. [PMID: 30923085 DOI: 10.3174/ajnr.a6019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 02/25/2019] [Indexed: 12/30/2022]
Abstract
BACKGROUND AND PURPOSE Multifocal glioblastomas (ie, glioblastomas with multiple foci, unconnected in postcontrast pretreatment T1-weighted images) represent a challenge in clinical practice due to their poor prognosis. We wished to obtain imaging biomarkers with prognostic value that have not been found previously. MATERIALS AND METHODS A retrospective review of 1155 patients with glioblastomas from 10 local institutions during 2006-2017 provided 97 patients satisfying the inclusion criteria of the study and classified as having multifocal glioblastomas. Tumors were segmented and morphologic features were computed using different methodologies: 1) measured on the largest focus, 2) aggregating the different foci as a whole, and 3) recording the extreme value obtained for each focus. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell concordance indices (c-indices) were used for the statistical analysis. RESULTS Age (P < .001, hazard ratio = 2.11, c-index = 0.705), surgery (P < .001, hazard ratio = 2.04, c-index = 0.712), contrast-enhancing rim width (P < .001, hazard ratio = 2.15, c-index = 0.704), and surface regularity (P = .021, hazard ratio = 1.66, c-index = 0.639) measured on the largest focus were significant independent predictors of survival. Maximum contrast-enhancing rim width (P = .002, hazard ratio = 2.05, c-index = 0.668) and minimal surface regularity (P = .036, hazard ratio = 1.64, c-index = 0.600) were also significant. A multivariate model using age, surgery, and contrast-enhancing rim width measured on the largest foci classified multifocal glioblastomas into groups with different outcomes (P < .001, hazard ratio = 3.00, c-index = 0.853, median survival difference = 10.55 months). Moreover, quartiles with the highest and lowest individual prognostic scores based on the focus with the largest volume and surgery were identified as extreme groups in terms of survival (P < .001, hazard ratio = 18.67, c-index = 0.967). CONCLUSIONS A prognostic model incorporating imaging findings on pretreatment postcontrast T1-weighted MRI classified patients with glioblastoma into different prognostic groups.
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Affiliation(s)
- J Pérez-Beteta
- From the Department of Mathematics (J.P.-B., D.M.-G., V.M.P.-G.), Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - D Molina-García
- From the Department of Mathematics (J.P.-B., D.M.-G., V.M.P.-G.), Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | | | - M J Rodríguez
- Radiology (M.J.R.), Hospital General de Ciudad Real, Ciudad Real, Spain
| | - C Velásquez
- Department of Neurosurgery (J.M., C.V.), Hospital Universitario Marqués de Valdecilla and Fundación, Instituto de Investigación Marqués de Valdecilla, Santander, Spain
| | - J Martino
- Department of Neurosurgery (J.M., C.V.), Hospital Universitario Marqués de Valdecilla and Fundación, Instituto de Investigación Marqués de Valdecilla, Santander, Spain
| | | | | | - R Morcillo
- Radiology (R.M.), Hospital Virgen de la Salud, Toledo, Spain
| | | | | | - A Ramos
- Radiology (A. Ramos), Hospital Universitario 12 de Octubre, Madrid, Spain
| | - J A Barcia
- Department of Neurosurgery (J.A.B.), Hospital Clínico San Carlos, Madrid, Spain
| | - P C Lara
- Department of Radiation Oncology (P.C.L.), San Roque University Hospital/Universidad Fernando Pessoa Canarias, Gran Canaria, Spain
| | - D Albillo
- Department of Radiology (D.A.), Hospital Universitario de Salamanca, Salamanca, Spain
| | - A Revert
- Department of Radiology (A. Revert), Hospital de Manises, Valencia, Spain
| | - E Arana
- Department of Radiology (E.A.), Fundación Instituto Valenciano de Oncología, Valencia, Spain
| | - V M Pérez-García
- From the Department of Mathematics (J.P.-B., D.M.-G., V.M.P.-G.), Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, Spain
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19
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Bousabarah K, Temming S, Hoevels M, Borggrefe J, Baus WW, Ruess D, Visser-Vandewalle V, Ruge M, Kocher M, Treuer H. Radiomic analysis of planning computed tomograms for predicting radiation-induced lung injury and outcome in lung cancer patients treated with robotic stereotactic body radiation therapy. Strahlenther Onkol 2019; 195:830-842. [PMID: 30874846 DOI: 10.1007/s00066-019-01452-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 03/02/2019] [Indexed: 12/11/2022]
Abstract
OBJECTIVES To predict radiation-induced lung injury and outcome in non-small cell lung cancer (NSCLC) patients treated with robotic stereotactic body radiation therapy (SBRT) from radiomic features of the primary tumor. METHODS In all, 110 patients with primary stage I/IIa NSCLC were analyzed for local control (LC), disease-free survival (DFS), overall survival (OS) and development of local lung injury up to fibrosis (LF). First-order (histogram), second-order (GLCM, Gray Level Co-occurrence Matrix) and shape-related radiomic features were determined from the unprocessed or filtered planning CT images of the gross tumor volume (GTV), subjected to LASSO (Least Absolute Shrinkage and Selection Operator) regularization and used to construct continuous and dichotomous risk scores for each endpoint. RESULTS Continuous scores comprising 1-5 histogram or GLCM features had a significant (p = 0.0001-0.032) impact on all endpoints that was preserved in a multifactorial Cox regression analysis comprising additional clinical and dosimetric factors. At 36 months, LC did not differ between the dichotomous risk groups (93% vs. 85%, HR 0.892, 95%CI 0.222-3.590), while DFS (45% vs. 17%, p < 0.05, HR 0.457, 95%CI 0.240-0.868) and OS (80% vs. 37%, p < 0.001, HR 0.190, 95%CI 0.065-0.556) were significantly lower in the high-risk groups. Also, the frequency of LF differed significantly between the two risk groups (63% vs. 20% at 24 months, p < 0.001, HR 0.158, 95%CI 0.054-0.458). CONCLUSION Radiomic analysis of the gross tumor volume may help to predict DFS and OS and the development of local lung fibrosis in early stage NSCLC patients treated with stereotactic radiotherapy.
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Affiliation(s)
- Khaled Bousabarah
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Susanne Temming
- Department of Radiation Oncology, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Mauritius Hoevels
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Jan Borggrefe
- Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Wolfgang W Baus
- Department of Radiation Oncology, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Daniel Ruess
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Maximilian Ruge
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Martin Kocher
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany.
- Department of Radiation Oncology, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany.
| | - Harald Treuer
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
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Tixier F, Um H, Bermudez D, Iyer A, Apte A, Graham MS, Nevel KS, Deasy JO, Young RJ, Veeraraghavan H. Preoperative MRI-radiomics features improve prediction of survival in glioblastoma patients over MGMT methylation status alone. Oncotarget 2019; 10:660-672. [PMID: 30774763 PMCID: PMC6363013 DOI: 10.18632/oncotarget.26578] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 12/22/2018] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Glioblastoma (GBM) is the most common malignant central nervous system tumor, and MGMT promoter hypermethylation in this tumor has been shown to be associated with better prognosis. We evaluated the capacity of radiomics features to add complementary information to MGMT status, to improve the ability to predict prognosis. METHODS 159 patients with untreated GBM were included in this study and divided into training and independent test sets. 286 radiomics features were extracted from the magnetic resonance images acquired prior to any treatments. A least absolute shrinkage selection operator (LASSO) selection followed by Kaplan-Meier analysis was used to determine the prognostic value of radiomics features to predict overall survival (OS). The combination of MGMT status with radiomics was also investigated and all results were validated on the independent test set. RESULTS LASSO analysis identified 8 out of the 286 radiomic features to be relevant which were then used for determining association to OS. One feature (edge descriptor) remained significant on the external validation cohort after multiple testing (p=0.04) and the combination with MGMT identified a group of patients with the best prognosis with a survival probability of 0.61 after 43 months (p=0.0005). CONCLUSION Our results suggest that combining radiomics with MGMT is more accurate in stratifying patients into groups of different survival risks when compared to with using these predictors in isolation. We identified two subgroups within patients who have methylated MGMT: one with a similar survival to unmethylated MGMT patients and the other with a significantly longer OS.
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Affiliation(s)
- Florent Tixier
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Hyemin Um
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Dalton Bermudez
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Aditi Iyer
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Aditya Apte
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Maya S. Graham
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Kathryn S. Nevel
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Robert J. Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Yaffe MJ. Emergence of "Big Data" and Its Potential and Current Limitations in Medical Imaging. Semin Nucl Med 2018; 49:94-104. [PMID: 30819400 DOI: 10.1053/j.semnuclmed.2018.11.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Although electronic imaging was performed in the early 1950s in nuclear medicine, it was the introduction of computed tomography in 1972 that caused a revolution in medical imaging in that it marked the beginning of the inevitable transformation to digital imaging. This transformation is now more or less complete. While initially these CT images were relatively small, comprised of only about 6400 pixels per slice, the steady move toward higher spatial resolution, multislice imaging, digital radiography, and fluoroscopy rapidly increased the size of images and the amount of data required to be stored, processed, displayed, and moved about in a medical imaging department. The more recent introduction of digital pathology with submicron-sized pixels and the need for color further increases these demands. Rising work volumes in hospital, a push for cost containment, and a move toward greater precision in diagnosis and treatment of disease all work together to motivate the development of automated image analysis algorithms and techniques to improve efficiencies in in vivo imaging and pathology. This may require bringing together information from different imaging and nonimaging sources within the institution. While technological development has provided practical means for storage of the burgeoning data load and the use of multiple processors and high-speed networks has enabled more sophisticated analysis locally or in the cloud, challenges remain in terms of the ability to integrate data from different systems, the development of appropriately annotated image bases for training and testing of algorithms, and issues around privacy and ownership in obtaining access to patient-related data.
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Affiliation(s)
- Martin J Yaffe
- Physical Sciences Program, Sunnybrook Health Sciences Centre and The University of Toronto, Toronto, ON, Canada.
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22
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Pérez-Beteta J, Molina-García D, Martínez-González A, Henares-Molina A, Amo-Salas M, Luque B, Arregui E, Calvo M, Borrás JM, Martino J, Velásquez C, Meléndez-Asensio B, de Lope ÁR, Moreno R, Barcia JA, Asenjo B, Benavides M, Herruzo I, Lara PC, Cabrera R, Albillo D, Navarro M, Pérez-Romasanta LA, Revert A, Arana E, Pérez-García VM. Morphological MRI-based features provide pretreatment survival prediction in glioblastoma. Eur Radiol 2018; 29:1968-1977. [PMID: 30324390 DOI: 10.1007/s00330-018-5758-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 08/19/2018] [Accepted: 09/12/2018] [Indexed: 12/16/2022]
Abstract
OBJECTIVES We wished to determine whether tumor morphology descriptors obtained from pretreatment magnetic resonance images and clinical variables could predict survival for glioblastoma patients. METHODS A cohort of 404 glioblastoma patients (311 discoveries and 93 validations) was used in the study. Pretreatment volumetric postcontrast T1-weighted magnetic resonance images were segmented to obtain the relevant morphological measures. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell's concordance indexes (c-indexes) were used for the statistical analysis. RESULTS A linear prognostic model based on the outstanding variables (age, contrast-enhanced (CE) rim width, and surface regularity) identified a group of patients with significantly better survival (p < 0.001, HR = 2.57) with high accuracy (discovery c-index = 0.74; validation c-index = 0.77). A similar model applied to totally resected patients was also able to predict survival (p < 0.001, HR = 3.43) with high predictive value (discovery c-index = 0.81; validation c-index = 0.92). Biopsied patients with better survival were well identified (p < 0.001, HR = 7.25) by a model including age and CE volume (c-index = 0.87). CONCLUSIONS Simple linear models based on small sets of meaningful MRI-based pretreatment morphological features and age predicted survival of glioblastoma patients to a high degree of accuracy. The partition of the population using the extent of resection improved the prognostic value of those measures. KEY POINTS • A combination of two MRI-based morphological features (CE rim width and surface regularity) and patients' age outperformed previous prognosis scores for glioblastoma. • Prognosis models for homogeneous surgical procedure groups led to even more accurate survival prediction based on Kaplan-Meier analysis and concordance indexes.
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Affiliation(s)
- Julián Pérez-Beteta
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - David Molina-García
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain.
| | - Alicia Martínez-González
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Araceli Henares-Molina
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Mariano Amo-Salas
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Belén Luque
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Elena Arregui
- Department of Radiology, Hospital General de Ciudad Real, Ciudad Real, Spain
| | - Manuel Calvo
- Department of Radiology, Hospital General de Ciudad Real, Ciudad Real, Spain
| | - José M Borrás
- Department of Neurosurgery, Hospital General de Ciudad Real, Ciudad Real, Spain
| | - Juan Martino
- Department of Neurosurgery, Hospital Universitario Marqués de Valdecilla and Fundación Instituto de Investigación Marqués de Valdecilla, Santander, Spain
| | - Carlos Velásquez
- Department of Neurosurgery, Hospital Universitario Marqués de Valdecilla and Fundación Instituto de Investigación Marqués de Valdecilla, Santander, Spain
| | | | | | - Raquel Moreno
- Department of Radiology, Hospital Virgen de la Salud, Toledo, Spain
| | - Juan A Barcia
- Department of Neurosurgery, Hospital Clínico San Carlos, Madrid, Spain
| | - Beatriz Asenjo
- Department of Radiology, Hospital Carlos Haya, Málaga, Spain
| | - Manuel Benavides
- Department of Medical Oncology, Hospital Carlos Haya, Málaga, Spain
| | - Ismael Herruzo
- Department of Radiation Oncology, Hospital Carlos Haya, Málaga, Spain
| | - Pedro C Lara
- Department of Radiation Oncology, Hospital Universitario Doctor Negrín, Gran Canaria, Spain
| | - Raquel Cabrera
- Department of Radiation Oncology, Hospital Universitario Doctor Negrín, Gran Canaria, Spain
| | - David Albillo
- Department of Radiology, Hospital Universitario de Salamanca, Salamanca, Spain
| | - Miguel Navarro
- Department of Medical Oncology, Hospital Universitario de Salamanca, Salamanca, Spain
| | | | - Antonio Revert
- Department of Radiology, Hospital de Manises, Valencia, Spain
| | - Estanislao Arana
- Department of Radiology, Fundación Instituto Valenciano de Oncología, Valencia, Spain
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
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23
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Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:7417126. [PMID: 30344618 PMCID: PMC6174735 DOI: 10.1155/2018/7417126] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 07/24/2018] [Accepted: 09/04/2018] [Indexed: 01/17/2023]
Abstract
Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. Furthermore, the combination with state-of-the-art image analysis methods provides a plethora of quantifiable imaging features, termed radiomics that increases diagnostic accuracy towards individualized therapy planning. More importantly, radiomics can now be complemented by the emerging deep learning techniques for further process automation and correlation with other clinical data which facilitate the monitoring of treatment response, as well as the prediction of patient's outcome, by means of unravelling of the complex underlying pathophysiological mechanisms which are reflected in tissue phenotype. The scope of this review is to provide applications and limitations of radiomics towards the development of clinical decision support systems for breast cancer diagnosis and prognosis.
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Wang Q, Li Q, Mi R, Ye H, Zhang H, Chen B, Li Y, Huang G, Xia J. Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study. J Magn Reson Imaging 2018; 49:825-833. [PMID: 30260592 DOI: 10.1002/jmri.26265] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 06/20/2018] [Accepted: 06/22/2018] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Accurate classification of gliomas is crucial for prescribing therapy and assessing the prognosis of patients. PURPOSE To develop a radiomics nomogram using multiparametric MRI for predicting glioma grading. STUDY TYPE Retrospective. POPULATION This study involved 85 patients (training cohort: n = 56; validation cohort: n = 29) with pathologically confirmed gliomas. FIELD STRENGTH/SEQUENCE 1.5T MR, containing contrast-enhanced T1 -weighted (CET1 WI), axial T2 -weighted (T2 WI), and apparent diffusion coefficient (ADC) sequences. ASSESSMENT A region of interest of the tumor was delineated. A total of 652 radiomics features were extracted and were reduced using least absolute shrinkage and selection operator regression. STATISTICAL TESTING Radiomic signature, participant's age, and gender were analyzed as potential predictors to perform logistic regression analysis and develop a prediction model of glioma grading, and a radiomics nomogram was used to represent this model. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical value in glioma grading. RESULTS The radiomic signature was significantly associated with glioma grade (P < 0.001) in both the training and validation cohorts. The performance of the radiomics nomogram derived from three MRI sequences (with C-index of 0.971 and 0.961 in the training and validation cohorts, respectively) was improved compared to those based on either CET1 WI, T2 WI, or ADC alone in glioma grading (with C-index of 0.914, 0.714, 0.842 in the training cohort, and 0.941, 0.500, 0.730 in the validation cohort). The nomogram derived from three sequences showed good calibration: the calibration curve showed good agreement between the estimated and the actual probability. The decision curve demonstrated that combining three sequences had more favorable clinical predictive value than single sequence imaging. DATA CONCLUSION We created and assessed a multiparametric MRI-based radiomics nomogram that may help clinicians classify gliomas more accurately. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:825-833.
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Affiliation(s)
- Qiuyu Wang
- Department of Radiology, Shenzhen Second People's Hospital, Shenzhen Second Hospital Clinical Medicine College of Anhui Medical University, Shenzhen, China
| | - Qingneng Li
- Department of Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Rui Mi
- Department of Radiology, the First Affiliated Hospital of Shenzhen University, Health Science Center; Shenzhen second people's hospital, Shenzhen, 518035, China
| | - Hai Ye
- Department of Radiology, Shenzhen Second People's Hospital, Shenzhen Second Hospital Clinical Medicine College of Anhui Medical University, Shenzhen, China
| | - Heye Zhang
- Department of Health Information Computing School of Biomedical Engineering, Sun Yat-Sen University
| | - Baodong Chen
- Department of Neurosurgery, the First Affiliated Hospital of Shenzhen University, Health Science Center; Shenzhen second people's hospital, Shenzhen, 518035, China
| | - Ye Li
- Department of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, China
| | - Guodong Huang
- Department of Neurosurgery, the First Affiliated Hospital of Shenzhen University, Health Science Center; Shenzhen second people's hospital, Shenzhen, 518035, China
| | - Jun Xia
- Department of Radiology, the First Affiliated Hospital of Shenzhen University, Health Science Center; Shenzhen second people's hospital, Shenzhen, 518035, China
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25
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Pérez-Beteta J, Molina-García D, Ortiz-Alhambra JA, Fernández-Romero A, Luque B, Arregui E, Calvo M, Borrás JM, Meléndez B, Rodríguez de Lope Á, Moreno de la Presa R, Iglesias Bayo L, Barcia JA, Martino J, Velásquez C, Asenjo B, Benavides M, Herruzo I, Revert A, Arana E, Pérez-García VM. Tumor Surface Regularity at MR Imaging Predicts Survival and Response to Surgery in Patients with Glioblastoma. Radiology 2018; 288:218-225. [DOI: 10.1148/radiol.2018171051] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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26
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Zhang B, He X, Ouyang F, Gu D, Dong Y, Zhang L, Mo X, Huang W, Tian J, Zhang S. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett 2017; 403:21-27. [PMID: 28610955 DOI: 10.1016/j.canlet.2017.06.004] [Citation(s) in RCA: 163] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 05/31/2017] [Accepted: 06/03/2017] [Indexed: 02/08/2023]
Abstract
We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were extracted from MRI images for each patient. Six feature selection methods and nine classification methods were evaluated in terms of their performance. We applied the 10-fold cross-validation as the criterion for feature selection and classification. We repeated each combination for 50 times to obtain the mean area under the curve (AUC) and test error. We observed that the combination methods Random Forest (RF) + RF (AUC, 0.8464 ± 0.0069; test error, 0.3135 ± 0.0088) had the highest prognostic performance, followed by RF + Adaptive Boosting (AdaBoost) (AUC, 0.8204 ± 0.0095; test error, 0.3384 ± 0.0097), and Sure Independence Screening (SIS) + Linear Support Vector Machines (LSVM) (AUC, 0.7883 ± 0.0096; test error, 0.3985 ± 0.0100). Our radiomics study identified optimal machine-learning methods for the radiomics-based prediction of local failure and distant failure in advanced NPC, which could enhance the applications of radiomics in precision oncology and clinical practice.
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Affiliation(s)
- Bin Zhang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, PR China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, Guangdong, PR China
| | - Xin He
- Department of Mathematics, City University of Hong Kong, PR China
| | - Fusheng Ouyang
- Department of Radiology, The First People's Hospital of Shunde, Foshan, Guangdong, PR China
| | - Dongsheng Gu
- Key Laboratory of Molecular Imaging, Chinese Academy of Science, Beijing, PR China
| | - Yuhao Dong
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China; Shantou University Medical College, Guangdong, PR China
| | - Lu Zhang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China
| | - Xiaokai Mo
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China; Shantou University Medical College, Guangdong, PR China
| | - Wenhui Huang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China; School of Medicine, South China University of Technology, Guangzhou, Guangdong, PR China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Chinese Academy of Science, Beijing, PR China.
| | - Shuixing Zhang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, PR China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, Guangdong, PR China.
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27
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Zhang B, Ouyang F, Gu D, Dong Y, Zhang L, Mo X, Huang W, Zhang S. Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics. Oncotarget 2017; 8:72457-72465. [PMID: 29069802 PMCID: PMC5641145 DOI: 10.18632/oncotarget.19799] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Accepted: 06/28/2017] [Indexed: 01/03/2023] Open
Abstract
We aimed to investigate the potential of radiomic features of magnetic resonance imaging (MRI) to predict progression in patients with advanced nasopharyngeal carcinoma (NPC). One hundred and thirteen consecutive patients (01/2007-07/2013) (training cohort: n = 80; validation cohort: n = 33) with advanced NPC were enrolled. A total of 970 initial features were extracted from T2-weighted (T2-w) (n = 485) and contrast-enhanced T1-weighted (CET1-w) MRI (n = 485) for each patient. We used least absolute shrinkage and selection operator (Lasso) method to select features that were most significantly associated with the progression. The selected features were used to construct radiomics-based models and the predictive performance of which were assessed with respect to the area under the curve (AUC). As a result, eight features significantly associated with the progression of advanced NPC were identified. In the training cohort, a radiomic model based on combined CET1-w and T2-w images (AUC: 0.886, 95%CI: 0.815-0.956) demonstrated better prognostic performance than models based on CET1-w (AUC: 0.793, 95%CI: 0.698-0.889) or T2-w images alone (AUC: 0.813, 95%CI: 0.721-0.904). These results were confirmed in the validation cohort. Accordingly, MRI-based radiomic biomarkers present high accuracy in the pre-treatment prediction of progression in advanced NPC.
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Affiliation(s)
- Bin Zhang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, P.R. China.,Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, P.R. China
| | - Fusheng Ouyang
- Department of Radiology, The First People's Hospital of Shunde, Foshan, P.R. China
| | - Dongsheng Gu
- Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, P.R. China
| | - Yuhao Dong
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Lu Zhang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Xiaokai Mo
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Wenhui Huang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Shuixing Zhang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, P.R. China.,Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, P.R. China
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