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Gagnon L, Gupta D, Mastorakos G, White N, Goodwill V, McDonald CR, Beaumont T, Conlin C, Seibert TM, Nguyen U, Hattangadi-Gluth J, Kesari S, Schulte JD, Piccioni D, Schmainda KM, Farid N, Dale AM, Rudie JD. Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma. Radiol Artif Intell 2024; 6:e230489. [PMID: 39166970 PMCID: PMC11427928 DOI: 10.1148/ryai.230489] [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] [Indexed: 08/23/2024]
Abstract
Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free survival (PFS). Materials and Methods This retrospective study included 1397 MRI scans in 1297 patients with glioblastoma, including an internal set of 243 MRI scans (January 2010 to June 2022) for model training and cross-validation and four external test cohorts. Cellular tumor maps were segmented by two radiologists on the basis of imaging, clinical history, and pathologic findings. Multimodal MRI data with perfusion and multishell diffusion imaging were inputted into a nnU-Net DL model to segment cellular tumor. Segmentation performance (Dice score) and performance in distinguishing recurrent tumor from posttreatment changes (area under the receiver operating characteristic curve [AUC]) were quantified. Model performance in predicting OS and PFS was assessed using Cox multivariable analysis. Results A cohort of 178 patients (mean age, 56 years ± 13 [SD]; 116 male, 62 female) with 243 MRI timepoints, as well as four external datasets with 55, 70, 610, and 419 MRI timepoints, respectively, were evaluated. The median Dice score was 0.79 (IQR, 0.53-0.89), and the AUC for detecting residual or recurrent tumor was 0.84 (95% CI: 0.79, 0.89). In the internal test set, estimated cellular tumor volume was significantly associated with OS (hazard ratio [HR] = 1.04 per milliliter; P < .001) and PFS (HR = 1.04 per milliliter; P < .001) after adjustment for age, sex, and gross total resection (GTR) status. In the external test sets, estimated cellular tumor volume was significantly associated with OS (HR = 1.01 per milliliter; P < .001) after adjustment for age, sex, and GTR status. Conclusion A DL model incorporating advanced imaging could accurately segment enhancing and nonenhancing cellular tumor, distinguish recurrent or residual tumor from posttreatment changes, and predict OS and PFS in patients with glioblastoma. Keywords: Segmentation, Glioblastoma, Multishell Diffusion MRI Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Louis Gagnon
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Diviya Gupta
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - George Mastorakos
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Nathan White
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Vanessa Goodwill
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Carrie R McDonald
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Thomas Beaumont
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Christopher Conlin
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Tyler M Seibert
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Uyen Nguyen
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Jona Hattangadi-Gluth
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Santosh Kesari
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Jessica D Schulte
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - David Piccioni
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Kathleen M Schmainda
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Nikdokht Farid
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Anders M Dale
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
| | - Jeffrey D Rudie
- From the Departments of Radiology (L.G., D.G., C.C., T.M.S., U.N., N.F., A.M.D., J.D.R.), Pathology (V.G.), Radiation Medicine and Applied Sciences (C.R.M., T.M.S., J.H.G.), Neurologic Surgery (T.B.), Bioengineering (T.M.S.), and Neurosciences (J.D.S., D.P., A.M.D.), University of California San Diego, 9500 Gillman Dr, La Jolla, CA 92093; Cortechs.ai, San Diego, Calif (G.M., N.W.); Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, Calif (S.K.); and Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wis (K.M.S.)
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Yamamoto S, Okita Y, Arita H, Sanada T, Sakai M, Arisawa A, Kagawa N, Shimosegawa E, Nakanishi K, Kinoshita M, Kishima H. Qualitative MR features to identify non-enhancing tumors within glioblastoma's T2-FLAIR hyperintense lesions. J Neurooncol 2023; 165:251-259. [PMID: 37917281 DOI: 10.1007/s11060-023-04454-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 09/13/2023] [Indexed: 11/04/2023]
Abstract
PURPOSE To identify qualitative MRI features of non-(contrast)-enhancing tumor (nCET) in glioblastoma's T2-FLAIR hyperintense lesion. METHODS Thirty-three histologically confirmed glioblastoma patients whose T1-, T2- and contrast-enhanced T1-weighted MRI and 11C-methionine positron emission tomography (Met-PET) were available were included in this study. Met-PET was utilized as a surrogate for tumor burden. Imaging features for identifying nCET were searched by qualitative examination of 156 targets. A new scoring system to identify nCET was established and validated by two independent observers. RESULTS Three imaging features were found helpful for identifying nCET; "Bulky gray matter involvement", "Around the rim of contrast-enhancement (Around-rim)," and "High-intensity on T1WI and low-intensity on T2WI (HighT1LowT2)" resulting in an nCET score = 2 × Bulky gray matter involvement - 2 × Around-rim + HighT1LowT2 + 2. The nCET score's classification performances of two independent observers measured by AUC were 0.78 and 0.80, with sensitivities and specificities using a threshold of four being 0.443 and 0.771, and 0.916 and 0.768, respectively. The weighted kappa coefficient for the nCET score was 0.946. CONCLUSION The current investigation demonstrated that qualitative assessments of glioblastoma's MRI might help identify nCET in T2/FLAIR high-intensity lesions. The novel nCET score is expected to aid in expanding treatment targets within the T2/FLAIR high-intensity lesions.
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Affiliation(s)
- Shota Yamamoto
- Department of Neurosurgery, Osaka Greneral Medical Center, Bandai-higashi 3-1-56, Sumiyoshi-ku, Osaka, 558-8558, Japan
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan
- Department of Neurosurgery, Asahikawa Medical University, Midorigaoka-higashi 2-1-1-1, Asahikawa, Hokkaido, 078-8510, Japan
| | - Yoshiko Okita
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan
| | - Hideyuki Arita
- Department of Neurosurgery, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Takahiro Sanada
- Department of Neurosurgery, Asahikawa Medical University, Midorigaoka-higashi 2-1-1-1, Asahikawa, Hokkaido, 078-8510, Japan
| | - Mio Sakai
- Department of Diagnostic and Interventional Radiology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Atsuko Arisawa
- Department of Diagnostic Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan
| | - Naoki Kagawa
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan
| | - Eku Shimosegawa
- Department of Molecular Imaging in Medicine, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan
| | - Katsuyuki Nakanishi
- Department of Diagnostic and Interventional Radiology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Manabu Kinoshita
- Department of Neurosurgery, Asahikawa Medical University, Midorigaoka-higashi 2-1-1-1, Asahikawa, Hokkaido, 078-8510, Japan.
| | - Haruhiko Kishima
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan
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Yan Q, Li F, Cui Y, Wang Y, Wang X, Jia W, Liu X, Li Y, Chang H, Shi F, Xia Y, Zhou Q, Zeng Q. Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm. J Digit Imaging 2023; 36:1480-1488. [PMID: 37156977 PMCID: PMC10406764 DOI: 10.1007/s10278-023-00838-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/13/2023] [Accepted: 04/18/2023] [Indexed: 05/10/2023] Open
Abstract
This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 patients with solitary brain tumor (104 glioblastoma and 98 BM) were retrospectively obtained between February 2016 and September 2022. The data were divided into training and validation sets in a 7:3 ratio. An additional 32 patients (19 glioblastoma and 13 BM) from a different hospital were considered testing set. Single-MRI-sequence DL models were developed using the 3D residual network-18 architecture in tumoral (T model) and tumoral + peritumoral regions (T&P model). Furthermore, the combination model based on conventional MRI and DWI was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The attention area of the model was visualized as a heatmap by gradient-weighted class activation mapping technique. For the single-MRI-sequence DL model, the T2WI sequence achieved the highest AUC in the validation set with either T models (0.889) or T&P models (0.934). In the combination models of the T&P model, the model of DWI combined with T2WI and contrast-enhanced T1WI showed increased AUC of 0.949 and 0.930 compared with that of single-MRI sequences in the validation set, respectively. And the highest AUC (0.956) was achieved by combined contrast-enhanced T1WI, T2WI, and DWI. In the heatmap, the central region of the tumoral was hotter and received more attention than other areas and was more important for differentiating glioblastoma from BM. A conventional MRI-based DL model could differentiate glioblastoma from solitary BM, and the combination models improved classification performance.
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Affiliation(s)
- Qingqing Yan
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong First Medical University, Jinan, China
| | - Fuyan Li
- Department of Radiology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
| | - Yi Cui
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Yong Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
| | - Xiao Wang
- Department of Radiology, Jining NO.1 People's Hospital, Jining, China
| | - Wenjing Jia
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Xinhui Liu
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Yuting Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Huan Chang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Feng Shi
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Yuwei Xia
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Qing Zhou
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
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Martucci M, Russo R, Giordano C, Schiarelli C, D’Apolito G, Tuzza L, Lisi F, Ferrara G, Schimperna F, Vassalli S, Calandrelli R, Gaudino S. Advanced Magnetic Resonance Imaging in the Evaluation of Treated Glioblastoma: A Pictorial Essay. Cancers (Basel) 2023; 15:3790. [PMID: 37568606 PMCID: PMC10417432 DOI: 10.3390/cancers15153790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
MRI plays a key role in the evaluation of post-treatment changes, both in the immediate post-operative period and during follow-up. There are many different treatment's lines and many different neuroradiological findings according to the treatment chosen and the clinical timepoint at which MRI is performed. Structural MRI is often insufficient to correctly interpret and define treatment-related changes. For that, advanced MRI modalities, including perfusion and permeability imaging, diffusion tensor imaging, and magnetic resonance spectroscopy, are increasingly utilized in clinical practice to characterize treatment effects more comprehensively. This article aims to provide an overview of the role of advanced MRI modalities in the evaluation of treated glioblastomas. For a didactic purpose, we choose to divide the treatment history in three main timepoints: post-surgery, during Stupp (first-line treatment) and at recurrence (second-line treatment). For each, a brief introduction, a temporal subdivision (when useful) or a specific drug-related paragraph were provided. Finally, the current trends and application of radiomics and artificial intelligence (AI) in the evaluation of treated GB have been outlined.
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Affiliation(s)
- Matia Martucci
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Rosellina Russo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Carolina Giordano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Chiara Schiarelli
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Gabriella D’Apolito
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Laura Tuzza
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Francesca Lisi
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Giuseppe Ferrara
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Francesco Schimperna
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Stefania Vassalli
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Rosalinda Calandrelli
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Simona Gaudino
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
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5
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Giambra M, Di Cristofori A, Valtorta S, Manfrellotti R, Bigiogera V, Basso G, Moresco RM, Giussani C, Bentivegna A. The peritumoral brain zone in glioblastoma: where we are and where we are going. J Neurosci Res 2023; 101:199-216. [PMID: 36300592 PMCID: PMC10091804 DOI: 10.1002/jnr.25134] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/01/2022] [Accepted: 10/01/2022] [Indexed: 12/13/2022]
Abstract
Glioblastoma (GBM) is the most aggressive and invasive primary brain tumor. Current therapies are not curative, and patients' outcomes remain poor with an overall survival of 20.9 months after surgery. The typical growing pattern of GBM develops by infiltrating the surrounding apparent normal brain tissue within which the recurrence is expected to appear in the majority of cases. Thus, in the last decades, an increased interest has developed to investigate the cellular and molecular interactions between GBM and the peritumoral brain zone (PBZ) bordering the tumor tissue. The aim of this review is to provide up-to-date knowledge about the oncogenic properties of the PBZ to highlight possible druggable targets for more effective treatment of GBM by limiting the formation of recurrence, which is almost inevitable in the majority of patients. Starting from the description of the cellular components, passing through the illustration of the molecular profiles, we finally focused on more clinical aspects, represented by imaging and radiological details. The complete picture that emerges from this review could provide new input for future investigations aimed at identifying new effective strategies to eradicate this still incurable tumor.
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Affiliation(s)
- Martina Giambra
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,PhD Program in Neuroscience, University of Milano-Bicocca, Monza, Italy
| | - Andrea Di Cristofori
- PhD Program in Neuroscience, University of Milano-Bicocca, Monza, Italy.,Division of Neurosurgery, Azienda Socio Sanitaria Territoriale - Monza, Ospedale San Gerardo, Monza, Italy
| | - Silvia Valtorta
- Department of Nuclear Medicine, San Raffaele Scientific Institute, IRCCS, Milan, Italy.,Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Italy.,NBFC, National Biodiversity Future Center, 90133, Palermo, Italy
| | - Roberto Manfrellotti
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,Division of Neurosurgery, Azienda Socio Sanitaria Territoriale - Monza, Ospedale San Gerardo, Monza, Italy
| | - Vittorio Bigiogera
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Gianpaolo Basso
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Rosa Maria Moresco
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,Department of Nuclear Medicine, San Raffaele Scientific Institute, IRCCS, Milan, Italy.,Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Italy
| | - Carlo Giussani
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,Division of Neurosurgery, Azienda Socio Sanitaria Territoriale - Monza, Ospedale San Gerardo, Monza, Italy
| | - Angela Bentivegna
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
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6
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Wahl J, Klint E, Hallbeck M, Hillman J, Wårdell K, Ramser K. Impact of preprocessing methods on the Raman spectra of brain tissue. BIOMEDICAL OPTICS EXPRESS 2022; 13:6763-6777. [PMID: 36589553 PMCID: PMC9774863 DOI: 10.1364/boe.476507] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/04/2022] [Accepted: 11/10/2022] [Indexed: 06/01/2023]
Abstract
Delineating cancer tissue while leaving functional tissue intact is crucial in brain tumor resection. Despite several available aids, surgeons are limited by preoperative or subjective tools. Raman spectroscopy is a label-free optical technique with promising indications for tumor tissue identification. To allow direct comparisons between measurements preprocessing of the Raman signal is required. There are many recognized methods for preprocessing Raman spectra; however, there is no universal standard. In this paper, six different preprocessing methods were tested on Raman spectra (n > 900) from fresh brain tissue samples (n = 34). The sample cohort included both primary brain tumors, such as adult-type diffuse gliomas and meningiomas, as well as metastases of breast cancer. Each tissue sample was classified according to the CNS WHO 2021 guidelines. The six methods include both direct and iterative polynomial fitting, mathematical morphology, signal derivative, commercial software, and a neural network. Data exploration was performed using principal component analysis, t-distributed stochastic neighbor embedding, and k-means clustering. For each of the six methods, the parameter combination that explained the most variance in the data, i.e., resulting in the highest Gap-statistic, was chosen and compared to the other five methods. Depending on the preprocessing method, the resulting clusters varied in number, size, and associated spectral features. The detected features were associated with hemoglobin, neuroglobin, carotenoid, water, and protoporphyrin, as well as proteins and lipids. However, the spectral features seen in the Raman spectra could not be unambiguously assigned to tissue labels, regardless of preprocessing method. We have illustrated that depending on the chosen preprocessing method, the spectral appearance of Raman features from brain tumor tissue can change. Therefore, we argue both for caution in comparing spectral features from different Raman studies, as well as the importance of transparency of methodology and implementation of the preprocessing. As discussed in this study, Raman spectroscopy for in vivo guidance in neurosurgery requires fast and adaptive preprocessing. On this basis, a pre-trained neural network appears to be a promising approach for the operating room.
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Affiliation(s)
- Joel Wahl
- Department of Engineering Sciences and Mathematics, Luleå University of Technology, 971 87, Luleå, Sweden
| | - Elisabeth Klint
- Department of Biomedical Engineering, Linköping University, 581 85 Linköping, Sweden
| | - Martin Hallbeck
- Department of Clinical Pathology and Biomedical and Clinical Sciences, Linköping University, 581 85 Linköping, Sweden
| | - Jan Hillman
- Department of Neurosurgery and Biomedical and Clinical Sciences, Linköping University, 581 85 Linköping, Sweden
| | - Karin Wårdell
- Department of Biomedical Engineering, Linköping University, 581 85 Linköping, Sweden
| | - Kerstin Ramser
- Department of Engineering Sciences and Mathematics, Luleå University of Technology, 971 87, Luleå, Sweden
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7
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Martin P, Holloway L, Metcalfe P, Koh ES, Brighi C. Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation. Cancers (Basel) 2022; 14:3897. [PMID: 36010891 PMCID: PMC9406186 DOI: 10.3390/cancers14163897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/04/2022] [Accepted: 08/09/2022] [Indexed: 11/17/2022] Open
Abstract
Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.
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Affiliation(s)
- Philip Martin
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Lois Holloway
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical Campus, School of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Peter Metcalfe
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Eng-Siew Koh
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical Campus, School of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Caterina Brighi
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
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8
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A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas. J Clin Med 2022; 11:jcm11133802. [PMID: 35807084 PMCID: PMC9267404 DOI: 10.3390/jcm11133802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/18/2022] [Accepted: 06/29/2022] [Indexed: 02/04/2023] Open
Abstract
Glioma is the most common primary malignant tumor of the adult central nervous system (CNS), which mostly shows invasive growth. In most cases, surgery is often difficult to completely remove, and the recurrence rate and mortality of patients are high. With the continuous development of molecular genetics and the great progress of molecular biology technology, more and more molecular biomarkers have been proved to have important guiding significance in the individualized diagnosis, treatment, and prognosis evaluation of glioma. With the updates of the World Health Organization (WHO) classification of tumors of the CNS in 2021, the diagnosis and treatment of glioma has entered the era of precision medicine in the true sense. Due to its ability to non-invasively achieve accurate identification of glioma from other intracranial tumors, and to predict the grade, genotyping, treatment response, and prognosis of glioma, which provides a scientific basis for the clinical application of individualized diagnosis and treatment model of glioma, radiomics has become a research hotspot in the field of precision medicine. This paper reviewed the research related to radiomics of adult gliomas published in recent years and summarized the research proceedings of radiomics in differential diagnosis, preoperative grading and genotyping, treatment and efficacy evaluation, and survival prediction of adult gliomas.
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9
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Booth TC, Wiegers EC, Warnert EAH, Schmainda KM, Riemer F, Nechifor RE, Keil VC, Hangel G, Figueiredo P, Álvarez-Torres MDM, Henriksen OM. High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics. Front Oncol 2022; 11:811425. [PMID: 35340697 PMCID: PMC8948428 DOI: 10.3389/fonc.2021.811425] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/28/2021] [Indexed: 01/16/2023] Open
Abstract
Objective To summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedside developments. Methods The current evidence regarding the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed. Perfusion, permeability, and microstructure imaging were similarly analyzed in Part 1 of this two-part review article and are valuable reading as background to this article. We appraise the clinic readiness of all the individual modalities and consider methodologies involving machine learning (radiomics) and the combination of MRI approaches (multiparametric imaging). Results The biochemical composition of high-grade gliomas is markedly different from healthy brain tissue. Magnetic resonance spectroscopy allows the simultaneous acquisition of an array of metabolic alterations, with choline-based ratios appearing to be consistently discriminatory in treatment response assessment, although challenges remain despite this being a mature technique. Promising directions relate to ultra-high field strengths, 2-hydroxyglutarate analysis, and the use of non-proton nuclei. Labile protons on endogenous proteins can be selectively targeted with chemical exchange saturation transfer to give high resolution images. The body of evidence for clinical application of amide proton transfer imaging has been building for a decade, but more evidence is required to confirm chemical exchange saturation transfer use as a monitoring biomarker. Multiparametric methodologies, including the incorporation of nuclear medicine techniques, combine probes measuring different tumor properties. Although potentially synergistic, the limitations of each individual modality also can be compounded, particularly in the absence of standardization. Machine learning requires large datasets with high-quality annotation; there is currently low-level evidence for monitoring biomarker clinical application. Conclusion Advanced MRI techniques show huge promise in treatment response assessment. The clinical readiness analysis highlights that most monitoring biomarkers require standardized international consensus guidelines, with more facilitation regarding technique implementation and reporting in the clinic.
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Affiliation(s)
- Thomas C. Booth
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Evita C. Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Ruben E. Nechifor
- Department of Clinical Psychology and Psychotherapy International Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
| | - Gilbert Hangel
- Department of Neurosurgery & High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria
| | - Patrícia Figueiredo
- Department of Bioengineering and Institute for Systems and Robotics - Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | | | - Otto M. Henriksen
- Department of Clinical Physiology, Nuclear medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
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10
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Aftab K, Aamir FB, Mallick S, Mubarak F, Pope WB, Mikkelsen T, Rock JP, Enam SA. Radiomics for precision medicine in glioblastoma. J Neurooncol 2022; 156:217-231. [PMID: 35020109 DOI: 10.1007/s11060-021-03933-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 12/20/2021] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients. METHODS We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma. RESULTS Classifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice. CONCLUSION Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.
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Affiliation(s)
- Kiran Aftab
- Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan
| | | | - Saad Mallick
- Medical College, Aga Khan University, Karachi, Pakistan
| | - Fatima Mubarak
- Department of Radiology, Aga Khan University, Karachi, Pakistan
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Tom Mikkelsen
- Departments of Neurology and Neurosurgery, Henry Ford Hospital, Detroit, MI, USA
| | - Jack P Rock
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI, USA
| | - Syed Ather Enam
- Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan.
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11
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Tillmanns N, Lum AE, Cassinelli G, Merkaj S, Verma T, Zeevi T, Staib L, Subramanian H, Bahar RC, Brim W, Lost J, Jekel L, Brackett A, Payabvash S, Ikuta I, Lin M, Bousabarah K, Johnson MH, Cui J, Malhotra A, Omuro A, Turowski B, Aboian MS. Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries. Neurooncol Adv 2022; 4:vdac093. [PMID: 36071926 PMCID: PMC9446682 DOI: 10.1093/noajnl/vdac093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background While there are innumerable machine learning (ML) research algorithms used for segmentation of gliomas, there is yet to be a US FDA cleared product. The aim of this study is to explore the systemic limitations of research algorithms that have prevented translation from concept to product by a review of the current research literature. Methods We performed a systematic literature review on 4 databases. Of 11 727 articles, 58 articles met the inclusion criteria and were used for data extraction and screening using TRIPOD. Results We found that while many articles were published on ML-based glioma segmentation and report high accuracy results, there were substantial limitations in the methods and results portions of the papers that result in difficulty reproducing the methods and translation into clinical practice. Conclusions In addition, we identified that more than a third of the articles used the same publicly available BRaTS and TCIA datasets and are responsible for the majority of patient data on which ML algorithms were trained, which leads to limited generalizability and potential for overfitting and bias.
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Affiliation(s)
- Niklas Tillmanns
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany
| | - Avery E Lum
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Gabriel Cassinelli
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Sara Merkaj
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Tej Verma
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Tal Zeevi
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Lawrence Staib
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Harry Subramanian
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ryan C Bahar
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Waverly Brim
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jan Lost
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Leon Jekel
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Alexandria Brackett
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, Connecticut, USA
| | - Sam Payabvash
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ichiro Ikuta
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - MingDe Lin
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Visage Imaging, Inc., San Diego, California, USA
| | | | - Michele H Johnson
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jin Cui
- Department of Pathology, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Ajay Malhotra
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Antonio Omuro
- Department of Neurology and Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut, USA
| | - Bernd Turowski
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany
| | - Mariam S Aboian
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
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12
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Radiomic Features Associated with Extent of Resection in Glioma Surgery. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:341-347. [PMID: 34862558 DOI: 10.1007/978-3-030-85292-4_38] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Radiomics defines a set of techniques for extraction and quantification of digital medical data in an automated and reproducible way. Its goal is to detect features potentially related to a clinical task, like classification, diagnosis, prognosis, and response to treatment, going beyond the intrinsic limits of operator-dependency and qualitative description of conventional radiological evaluation on a mesoscopic scale. In the field of neuro-oncology, researchers have tried to create prognostic models for a better tumor diagnosis, histological and biomolecular classification, prediction of response to treatment, and identification of disease relapse. Concerning glioma surgery, the most significant aid that radiomics can give to surgery is to improve tumor extension detection and identify areas that are more prone to recurrence to increase the extent of tumor resection, thereby ameliorating the patients' prognosis. This chapter aims to review the fundamentals of radiomics models' creation, the latest advance of radiomics in neuro-oncology, and possible radiomic features associated with the extent of resection in the brain gliomas.
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Zander E, Ardeleanu A, Singleton R, Bede B, Wu Y, Zheng S. A functional artificial neural network for noninvasive pretreatment evaluation of glioblastoma patients. Neurooncol Adv 2021; 4:vdab167. [PMID: 35059640 PMCID: PMC8765794 DOI: 10.1093/noajnl/vdab167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Pretreatment assessments for glioblastoma (GBM) patients, especially elderly or frail patients, are critical for treatment planning. However, genetic profiling with intracranial biopsy carries a significant risk of permanent morbidity. We previously demonstrated that the CUL2 gene, encoding the scaffold cullin2 protein in the cullin2-RING E3 ligase (CRL2), can predict GBM radiosensitivity and prognosis. CUL2 expression levels are closely regulated with its copy number variations (CNVs). This study aims to develop artificial neural networks (ANNs) for pretreatment evaluation of GBM patients with inputs obtainable without intracranial surgical biopsies.
Methods
Public datasets including Ivy-GAP, The Cancer Genome Atlas Glioblastoma (TCGA-GBM), and the Chinese Glioma Genome Atlas (CGGA) were used for training and testing of the ANNs. T1 images from corresponding cases were studied using automated segmentation for features of heterogeneity and tumor edge contouring. A ratio comparing the surface area of tumor borders versus the total volume (SvV) was derived from the DICOM-SEG conversions of segmented tumors. The edges of these borders were detected using the canny edge detector. Packages including Keras, Pytorch, and TensorFlow were tested to build the ANNs. A 4-layered ANN (8-8-8-2) with a binary output was built with optimal performance after extensive testing.
Results
The 4-layered deep learning ANN can identify a GBM patient’s overall survival (OS) cohort with 80%–85% accuracy. The ANN requires 4 inputs, including CUL2 copy number, patients’ age at GBM diagnosis, Karnofsky Performance Scale (KPS), and SvV ratio.
Conclusion
Quantifiable image features can significantly improve the ability of ANNs to identify a GBM patients’ survival cohort. Features such as clinical measures, genetic data, and image data, can be integrated into a single ANN for GBM pretreatment evaluation.
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Affiliation(s)
- Eric Zander
- Department of Mathematics, DigiPen Institute of Technology, Redmond, Washington, USA
| | - Andrew Ardeleanu
- Department of Mathematics, DigiPen Institute of Technology, Redmond, Washington, USA
| | - Ryan Singleton
- Department of Mathematics, DigiPen Institute of Technology, Redmond, Washington, USA
| | - Barnabas Bede
- Department of Mathematics, DigiPen Institute of Technology, Redmond, Washington, USA
| | - Yilin Wu
- Department of Mathematics, DigiPen Institute of Technology, Redmond, Washington, USA
| | - Shuhua Zheng
- Department of General Surgery, San Joaquin General Hospital, French Camp, California, USA
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14
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Characterizing the Genomic Profile in High-Grade Gliomas: From Tumor Core to Peritumoral Brain Zone, Passing through Glioma-Derived Tumorspheres. BIOLOGY 2021; 10:biology10111157. [PMID: 34827152 PMCID: PMC8615186 DOI: 10.3390/biology10111157] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 11/04/2021] [Accepted: 11/06/2021] [Indexed: 02/07/2023]
Abstract
Glioblastoma is an extremely heterogeneous disease. Treatment failure and tumor recurrence primarily reflect the presence in the tumor core (TC) of the glioma stem cells (GSCs), and secondly the contribution, still to be defined, of the peritumoral brain zone (PBZ). Using the array-CGH platform, we deepened the genomic knowledge about the different components of GBM and we identified new specific biomarkers useful for new therapies. We firstly investigated the genomic profile of 20 TCs of GBM; then, for 14 cases and 7 cases, respectively, we compared these genomic profiles with those of the related GSC cultures and PBZ biopsies. The analysis on 20 TCs confirmed the intertumoral heterogeneity and a high percentage of copy number alterations (CNAs) in GBM canonical pathways. Comparing the genomic profiles of 14 TC-GSC pairs, we evidenced a robust similarity among the two samples of each patient. The shared imbalanced genes are related to the development and progression of cancer and in metabolic pathways, as shown by bioinformatic analysis using DAVID. Finally, the comparison between 7 TC-PBZ pairs leads to the identification of PBZ-unique alterations that require further investigation.
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Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI. Sci Rep 2021; 11:9974. [PMID: 33976264 PMCID: PMC8113258 DOI: 10.1038/s41598-021-89218-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 04/22/2021] [Indexed: 02/03/2023] Open
Abstract
Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903-1.000) for local recurrence; 0.864 (0.726-0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or "radiomic risk score", increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.
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16
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Quantitative mapping of individual voxels in the peritumoral region of IDH-wildtype glioblastoma to distinguish between tumor infiltration and edema. J Neurooncol 2021; 153:251-261. [PMID: 33905055 DOI: 10.1007/s11060-021-03762-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 04/20/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE The peritumoral region (PTR) in glioblastoma (GBM) represents a combination of infiltrative tumor and vasogenic edema, which are indistinguishable on magnetic resonance imaging (MRI). We developed a radiomic signature by using imaging data from low grade glioma (LGG) (marker of tumor) and PTR of brain metastasis (BM) (marker of edema) and applied it on the GBM PTR to generate probabilistic maps. METHODS 270 features were extracted from T1-weighted, T2-weighted, and apparent diffusion coefficient maps in over 3.5 million voxels of LGG (36 segments) and BM (45 segments) scanned in a 1.5T MRI. A support vector machine classifier was used to develop the radiomics model from approximately 50% voxels (downsampled to 10%) and validated with the remaining. The model was applied to over 575,000 voxels of the PTR of 10 patients with GBM to generate a quantitative map using Platt scaling (infiltrative tumor vs. edema). RESULTS The radiomics model had an accuracy of 0.92 and 0.79 in the training and test set, respectively (LGG vs. BM). When extrapolated on the GBM PTR, 9 of 10 patients had a higher percentage of voxels with a tumor-like signature over radiological recurrence areas. In 7 of 10 patients, the areas under curves (AUC) were > 0.50 confirming a positive correlation. Including all the voxels from the GBM patients, the infiltration signature had an AUC of 0.61 to predict recurrence. CONCLUSION A radiomic signature can demarcate areas of microscopic tumors from edema in the PTR of GBM, which correlates with areas of future recurrence.
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Yan JL, Toh CH, Ko L, Wei KC, Chen PY. A Neural Network Approach to Identify Glioblastoma Progression Phenotype from Multimodal MRI. Cancers (Basel) 2021; 13:cancers13092006. [PMID: 33919447 PMCID: PMC8121245 DOI: 10.3390/cancers13092006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/16/2021] [Accepted: 04/19/2021] [Indexed: 11/16/2022] Open
Abstract
The phenotypes of glioblastoma (GBM) progression after treatment are heterogeneous in both imaging and clinical prognosis. This study aims to apply radiomics and neural network analysis to preoperative multimodal MRI data to characterize tumor progression phenotypes. We retrospectively reviewed 41 patients with newly diagnosed cerebral GBM from 2009-2016 who comprised the machine learning training group, and prospectively included 18 patients from 2017-2018 for data validation. Preoperative MRI examinations included structural MRI, diffusion tensor imaging, and perfusion MRI. Tumor progression patterns were categorized as diffuse or localized. A supervised machine learning model and neural network-based models (VGG16 and ResNet50) were used to establish the prediction model of the pattern of progression. The diffuse progression pattern showed a significantly worse prognosis regarding overall survival (p = 0.032). A total of 153 of the 841 radiomic features were used to classify progression patterns using different machine learning models with an overall accuracy of 81% (range: 77.5-82.5%, AUC = 0.83-0.89). Further application of the pretrained ResNet50 and VGG 16 neural network models demonstrated an overall accuracy of 93.1 and 96.1%. The progression patterns of GBM are an important prognostic factor and can potentially be predicted by combining multimodal MR radiomics with machine learning.
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Affiliation(s)
- Jiun-Lin Yan
- Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung 204, Taiwan;
- Department of Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
- Correspondence: (J.-L.Y.); (P.-Y.C.)
| | - Cheng-Hong Toh
- Department of Radiology, Chang Gung Memorial Hospital at Linkou, Taoyuan 333, Taiwan;
| | - Li Ko
- Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung 204, Taiwan;
| | - Kuo-Chen Wei
- Department of Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
- Department of Neurosurgery, Chang Gung Memorial Hospital at Linkou, Taoyuan 333, Taiwan
| | - Pin-Yuan Chen
- Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung 204, Taiwan;
- Department of Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
- Correspondence: (J.-L.Y.); (P.-Y.C.)
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18
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d’Este SH, Nielsen MB, Hansen AE. Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature. Diagnostics (Basel) 2021; 11:diagnostics11040592. [PMID: 33806195 PMCID: PMC8067218 DOI: 10.3390/diagnostics11040592] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 03/23/2021] [Indexed: 01/14/2023] Open
Abstract
The aim of this study was to systematically review the literature concerning the integration of multimodality imaging with artificial intelligence methods for visualization of tumor cell infiltration in glioma patients. The review was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. The literature search was conducted in PubMed, Embase, The Cochrane Library and Web of Science and yielded 1304 results. 14 studies were included in the qualitative analysis. The reference standard for tumor infiltration was either histopathology or recurrence on image follow-up. Critical assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS2). All studies concluded their findings to be of significant value for future clinical practice. Diagnostic test accuracy reached an area under the curve of 0.74–0.91 reported in six studies. There was no consensus with regard to included image modalities, models or training and test strategies. The integration of artificial intelligence with multiparametric imaging shows promise for visualizing tumor cell infiltration in glioma patients. This approach can possibly optimize surgical resection margins and help provide personalized radiotherapy planning.
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Affiliation(s)
- Sabrina Honoré d’Este
- Department of Diagnostic Radiology, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (M.B.N.); (A.E.H.)
- Correspondence:
| | - Michael Bachmann Nielsen
- Department of Diagnostic Radiology, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (M.B.N.); (A.E.H.)
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Adam Espe Hansen
- Department of Diagnostic Radiology, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (M.B.N.); (A.E.H.)
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
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19
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Abstract
Reoperation for glioma is increasingly common but there is neither firm agreement on the indications nor unequivocally proven benefit from clinical trials. Patient and tumor factors should be considered when offering reoperation and a clear surgical goal set. Reoperation is challenging because of placement of previous incisions, wound devascularization by preceding radiotherapy and/or chemotherapy, chronic steroid use, the need for further adjuvant therapy, and adherent and defective dura. This article reviews indications, challenges, and recommendations for repeat surgery in the patient with glioma.
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Affiliation(s)
- Rasheed Zakaria
- Department of Neurosurgery, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 442, Houston, TX 77030, USA
| | - Jeffrey S Weinberg
- Department of Neurosurgery, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 442, Houston, TX 77030, USA.
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20
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Unkelbach J, Bortfeld T, Cardenas CE, Gregoire V, Hager W, Heijmen B, Jeraj R, Korreman SS, Ludwig R, Pouymayou B, Shusharina N, Söderberg J, Toma-Dasu I, Troost EGC, Vasquez Osorio E. The role of computational methods for automating and improving clinical target volume definition. Radiother Oncol 2020; 153:15-25. [PMID: 33039428 DOI: 10.1016/j.radonc.2020.10.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/01/2020] [Accepted: 10/01/2020] [Indexed: 12/25/2022]
Abstract
Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume (GTV), the clinical target volume (CTV), and the planning target volume (PTV). Over time, GTV definition and PTV margins have improved through the development of novel imaging techniques and better image guidance, respectively. CTV definition is sometimes considered the weakest element in the planning process. CTV definition is particularly complex since the extension of microscopic disease cannot be seen using currently available in-vivo imaging techniques. Instead, CTV definition has to incorporate knowledge of the patterns of tumor progression. While CTV delineation has largely been considered the domain of radiation oncologists, this paper, arising from a 2019 ESTRO Physics research workshop, discusses the contributions that medical physics and computer science can make by developing computational methods to support CTV definition. First, we overview the role of image segmentation algorithms, which may in part automate CTV delineation through segmentation of lymph node stations or normal tissues representing anatomical boundaries of microscopic tumor progression. The recent success of deep convolutional neural networks has also enabled learning entire CTV delineations from examples. Second, we discuss the use of mathematical models of tumor progression for CTV definition, using as example the application of glioma growth models to facilitate GTV-to-CTV expansion for glioblastoma that is consistent with neuroanatomy. We further consider statistical machine learning models to quantify lymphatic metastatic progression of tumors, which may eventually improve elective CTV definition. Lastly, we discuss approaches to incorporate uncertainty in CTV definition into treatment plan optimization as well as general limitations of the CTV concept in the case of infiltrating tumors without natural boundaries.
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Affiliation(s)
- Jan Unkelbach
- Department of Radiation Oncology, University Hospital Zurich, Switzerland.
| | - Thomas Bortfeld
- Division of Radiation Biophysics, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Carlos E Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Wille Hager
- Department of Physics, Medical Radiation Physics, Stockholm University and Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| | - Ben Heijmen
- Department of Radiation Oncology, Erasmus University Medical Center (Erasmus MC), Rotterdam, The Netherlands
| | - Robert Jeraj
- Department of Medical Physics, University of Wisconsin, Madison, USA
| | - Stine S Korreman
- Department of Oncology and Danish Center for Particle Therapy, Aarhus University Hospital, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Roman Ludwig
- Department of Radiation Oncology, University Hospital Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Radiation Oncology, University Hospital Zurich, Switzerland
| | - Nadya Shusharina
- Division of Radiation Biophysics, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | | | - Iuliana Toma-Dasu
- Department of Physics, Medical Radiation Physics, Stockholm University and Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| | - Esther G C Troost
- Dept. of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; OncoRay - National Center for Radiation Research in Oncology, Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, UK
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