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Satragno C, Schiavetti I, Cella E, Picichè F, Falcitano L, Resaz M, Truffelli M, Caneva S, Mattioli P, Esposito D, Ginulla A, Scaffidi C, Fiaschi P, D'Andrea A, Bianconi A, Zona G, Barletta L, Roccatagliata L, Castellan L, Morbelli S, Bauckneht M, Donegani I, Nozza P, Arnaldi D, Vidano G, Gianelli F, Barra S, Bennicelli E, Belgioia L. Systemic inflammatory markers and volume of enhancing tissue on post-contrast T1w MRI images in differentiating true tumor progression from pseudoprogression in high-grade glioma. Clin Transl Radiat Oncol 2024; 49:100849. [PMID: 39318678 PMCID: PMC11419878 DOI: 10.1016/j.ctro.2024.100849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 05/31/2024] [Accepted: 07/03/2024] [Indexed: 09/26/2024] Open
Abstract
Background High-grade glioma (HGG) patients post-radiotherapy often face challenges distinguishing true tumor progression (TTP) from pseudoprogression (PsP). This study evaluates the effectiveness of systemic inflammatory markers and volume of enhancing tissue on post-contrast T1 weighted (T1WCE) MRI images for this differentiation within the first six months after treatment. Material and Methods We conducted a retrospective analysis on a cohort of HGG patients from 2015 to 2021, categorized per WHO 2016 and 2021 criteria. We analyzed treatment responses using modified RANO criteria and conducted volumetry on T1WCE and T2W/FLAIR images.Blood parameters assessed included neutrophil/lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI). We employed Chi-square, Fisher's exact test, and Mann-Whitney U test for statistical analyses, using log-transformed predictors due to multicollinearity. A Cox regression analysis assessed the impact of PsP- and TTP-related factors on overall survival (OS). Results The cohort consisted of 39 patients, where 16 exhibited PsP and 23 showed TTP. Univariate analysis revealed significantly higher NLR and SII in the TTP group [NLR: 4.1 vs 7.3, p = 0.002; SII 546.5 vs 890.5p = 0.009]. T1WCE volume distinctly differentiated PsP from TTP [2.2 vs 11.7, p < 0.001]. In multivariate regression, significant predictors included NLR and T1WCE volume in the "NLR Model," and T1WCE volume and SII in the "SII Model." The study also found a significantly lower OS rate in TTP patients compared to those with PsP [HR 3.97, CI 1.59 to 9.93, p = 0.003]. Conclusion Elevated both, SII and NLR, and increased T1WCE volume were effective in differentiating TTP from PsP in HGG patients post-radiotherapy. These results suggest the potential utility of incorporating these markers into clinical practice, though further research is necessary to confirm these findings in larger patient cohorts.
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Affiliation(s)
- Camilla Satragno
- Dept. of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy
| | - Irene Schiavetti
- Dept. of Health Science (DISSAL), University of Genoa, Genoa, Italy
| | - Eugenia Cella
- U.O. Oncologia Medica 2, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Dept. of Internal Medicine and Medical Speciality (DIMI), University of Genoa, Genoa, Italy
| | - Federica Picichè
- Dept. of Health Science (DISSAL), University of Genoa, Genoa, Italy
| | - Laura Falcitano
- U.O. Neuroradiologia, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Martina Resaz
- U.O. Neuroradiologia, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Monica Truffelli
- U.O. Clinica Neurochirurgica e Neurotraumatologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Stefano Caneva
- U.O. Clinica Neurochirurgica e Neurotraumatologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Neuroscience Ophthalmological Rehabilitation Genetics and Mother and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Pietro Mattioli
- Department of Neuroscience Ophthalmological Rehabilitation Genetics and Mother and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- U.O. Neurofisiopatologia, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Daniela Esposito
- Dept. of Health Science (DISSAL), University of Genoa, Genoa, Italy
| | - Alessio Ginulla
- Dept. of Health Science (DISSAL), University of Genoa, Genoa, Italy
| | - Claudio Scaffidi
- Dept. of Health Science (DISSAL), University of Genoa, Genoa, Italy
| | - Pietro Fiaschi
- U.O. Clinica Neurochirurgica e Neurotraumatologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Neuroscience Ophthalmological Rehabilitation Genetics and Mother and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Alessandro D'Andrea
- U.O. Clinica Neurochirurgica e Neurotraumatologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Andrea Bianconi
- U.O. Clinica Neurochirurgica e Neurotraumatologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Gianluigi Zona
- U.O. Clinica Neurochirurgica e Neurotraumatologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Neuroscience Ophthalmological Rehabilitation Genetics and Mother and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Laura Barletta
- U.O. Neuroradiologia, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Luca Roccatagliata
- Dept. of Health Science (DISSAL), University of Genoa, Genoa, Italy
- U.O. Neuroradiologia, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Lucio Castellan
- U.O. Neuroradiologia, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Silvia Morbelli
- Dept. of Health Science (DISSAL), University of Genoa, Genoa, Italy
- U.O. Medicina Nucleare, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Matteo Bauckneht
- Dept. of Health Science (DISSAL), University of Genoa, Genoa, Italy
- U.O. Medicina Nucleare, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Isabella Donegani
- U.O. Medicina Nucleare, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Paolo Nozza
- U.O. Anatomia Patologica Ospedaliera, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Dario Arnaldi
- Department of Neuroscience Ophthalmological Rehabilitation Genetics and Mother and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- U.O. Neurofisiopatologia, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Giulia Vidano
- U.O. Radioterapia Oncologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Flavio Gianelli
- U.O. Radioterapia Oncologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Salvina Barra
- U.O. Radioterapia Oncologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Elisa Bennicelli
- U.O. Oncologia Medica 2, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Liliana Belgioia
- Dept. of Health Science (DISSAL), University of Genoa, Genoa, Italy
- U.O. Radioterapia Oncologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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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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3
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Li K, Zhu Q, Yang J, Zheng Y, Du S, Song M, Peng Q, Yang R, Liu Y, Qi L. Imaging and Liquid Biopsy for Distinguishing True Progression From Pseudoprogression in Gliomas, Current Advances and Challenges. Acad Radiol 2024; 31:3366-3383. [PMID: 38614827 DOI: 10.1016/j.acra.2024.03.019] [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: 12/10/2023] [Revised: 01/14/2024] [Accepted: 03/18/2024] [Indexed: 04/15/2024]
Abstract
RATIONALE AND OBJECTIVES Gliomas are aggressive brain tumors with a poor prognosis. Assessing treatment response is challenging because magnetic resonance imaging (MRI) may not distinguish true progression (TP) from pseudoprogression (PsP). This review aims to discuss imaging techniques and liquid biopsies used to distinguish TP from PsP. MATERIALS AND METHODS This review synthesizes existing literature to examine advances in imaging techniques, such as magnetic resonance diffusion imaging (MRDI), perfusion-weighted imaging (PWI) MRI, and liquid biopsies, for identifying TP or PsP through tumor markers and tissue characteristics. RESULTS Advanced imaging techniques, including MRDI and PWI MRI, have proven effective in delineating tumor tissue properties, offering valuable insights into glioma behavior. Similarly, liquid biopsy has emerged as a potent tool for identifying tumor-derived markers in biofluids, offering a non-invasive glimpse into tumor evolution. Despite their promise, these methodologies grapple with significant challenges. Their sensitivity remains inconsistent, complicating the accurate differentiation between TP and PSP. Furthermore, the absence of standardized protocols across platforms impedes the reliability of comparisons, while inherent biological variability adds complexity to data interpretation. CONCLUSION Their potential applications have been highlighted, but gaps remain before routine clinical use. Further research is needed to develop and validate these promising methods for distinguishing TP from PsP in gliomas.
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Affiliation(s)
- Kaishu Li
- Department of Neurosurgery, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China; Department of Neurosurgery & Medical Research Center, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), 1# Jiazi Road, Foshan, Guangdong 528300, China.; Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Qihui Zhu
- Department of Neurosurgery, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Junyi Yang
- Department of Neurosurgery, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Yin Zheng
- Department of Neurosurgery, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Siyuan Du
- Institute of Digestive Disease of Guangzhou Medical University, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Meihui Song
- Institute of Digestive Disease of Guangzhou Medical University, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Qian Peng
- Institute of Digestive Disease of Guangzhou Medical University, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Runwei Yang
- Department of Neurosurgery & Medical Research Center, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), 1# Jiazi Road, Foshan, Guangdong 528300, China
| | - Yawei Liu
- Department of Neurosurgery & Medical Research Center, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), 1# Jiazi Road, Foshan, Guangdong 528300, China
| | - Ling Qi
- Institute of Digestive Disease of Guangzhou Medical University, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China.
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Sanvito F, Castellano A, Cloughesy TF, Wen PY, Ellingson BM. RANO 2.0 criteria: concepts applicable to the neuroradiologist's clinical practice. Curr Opin Oncol 2024:00001622-990000000-00192. [PMID: 39011735 DOI: 10.1097/cco.0000000000001077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
PURPOSE OF REVIEW The Response Assessment in Neuro-Oncology (RANO) 2.0 criteria aim at improving the standardization and reliability of treatment response assessment in clinical trials studying central nervous system (CNS) gliomas. This review presents the evidence supporting RANO 2.0 updates and discusses which concepts can be applicable to the clinical practice, particularly in the clinical radiographic reads. RECENT FINDINGS Updates in RANO 2.0 were supported by recent retrospective analyses of multicenter data from recent clinical trials. As proposed in RANO 2.0, in tumors receiving radiation therapy, the post-RT MRI scan should be used as a reference baseline for the following scans, as opposed to the pre-RT scan, and radiographic findings suggesting progression within three months after radiation therapy completion should be verified with confirmatory scans. Volumetric assessments should be considered, when available, especially for low-grade gliomas, and the evaluation of nonenhancing disease should have a marginal role in glioblastoma. However, the radiographic reads in the clinical setting also benefit from aspects that lie outside RANO 2.0 criteria, such as qualitative evaluations, patient-specific clinical considerations, and advanced imaging. SUMMARY While RANO 2.0 criteria are meant for the standardization of the response assessment in clinical trials, some concepts have the potential to improve patients' management in the clinical practice.
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Affiliation(s)
- Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California, USA
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele Vita-Salute San Raffaele University, Milan, Italy
| | - Timothy F Cloughesy
- UCLA Brain Tumor Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Patrick Y Wen
- Center For Neuro-Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California, USA
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Gu X, He X, Wang H, Li J, Chen R, Liu H. Dynamic Susceptibility Contrast-Enhanced Perfusion-Weighted Imaging in Differentiation Between Recurrence and Pseudoprogression in High-Grade Glioma: A Meta-analysis. J Comput Assist Tomogr 2024; 48:303-310. [PMID: 37654056 DOI: 10.1097/rct.0000000000001543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
INTRODUCTION In glioma patients that have undergone surgical tumor resection, the ability to reliably distinguish between pseudoprogression (PsP) and a recurrent tumor (RT) is of key clinical importance. Accordingly, this meta-analysis evaluated the utility of dynamic susceptibility contrast-enhanced perfusion-weighted imaging as a means of distinguishing between PsP and RT when analyzing patients with high-grade glioma. MATERIALS AND METHODS The PubMed, Web of Science, and Wanfang databases were searched for relevant studies. Pooled analyses of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) values were conducted, after which the area under the curve (AUC) for summary receiver operating characteristic curves was computed. RESULTS This meta-analysis ultimately included 21 studies enrolling 879 patients with 888 lesions. Cerebral blood volume-associated diagnostic results were reported in 20 of the analyzed studies, and the respective pooled sensitivity, specificity, PLR, and NLR values were 86% (95% confidence interval [CI], 0.81-0.89), 83% (95% CI, 0.77-0.87), 4.94 (95% CI, 3.61-6.75), and 0.18 (95% CI, 0.13-0.23) for these 20 studies. The corresponding AUC value was 0.91 (95% CI, 0.88-0.93), and the publication bias risk was low ( P = 0.976). Cerebral blood flow-related diagnostic results were additionally reported in 6 of the analyzed studies, with respective pooled sensitivity, specificity, PLR, and NLR values of 85% (95% CI, 0.78-0.90), 85% (95% CI, 0.76-0.91), 5.54 (95% CI, 3.40-9.01), and 0.18 (95% CI, 0.12-0.26). The corresponding AUC value was 0.92 (95% CI, 0.89-0.94), and the publication bias risk was low ( P = 0.373). CONCLUSIONS The present meta-analysis results suggest that dynamic susceptibility contrast-enhanced perfusion-weighted imaging represents an effective diagnostic approach to distinguishing between PsP and RT in high-grade glioma patients.
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Affiliation(s)
| | - Xining He
- From the Departments of Neurosurgery
| | - Hualong Wang
- Radiology, Binzhou People's Hospital, Binzhou, China
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Thenier-Villa JL, Martínez-Ricarte FR, Figueroa-Vezirian M, Arikan-Abelló F. Glioblastoma Pseudoprogression Discrimination Using Multiparametric Magnetic Resonance Imaging, Principal Component Analysis, and Supervised and Unsupervised Machine Learning. World Neurosurg 2024; 183:e953-e962. [PMID: 38253179 DOI: 10.1016/j.wneu.2024.01.074] [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: 07/01/2023] [Revised: 01/12/2024] [Accepted: 01/13/2024] [Indexed: 01/24/2024]
Abstract
BACKGROUND One of the most frequent phenomena in the follow-up of glioblastoma is pseudoprogression, present in up to half of cases. The clinical usefulness of discriminating this phenomenon through magnetic resonance imaging and nuclear medicine has not yet been standardized; in this study, we used machine learning on multiparametric magnetic resonance imaging to explore discriminators of this phenomenon. METHODS For the study, 30 patients diagnosed with IDH wild-type glioblastoma operated on at both study centers in 2011-2020 were selected; 15 patients corresponded to early tumor progression and 15 patients to pseudoprogression. Using unsupervised learning, the number of clusters and tumor segmentation was recorded using gap-stat and k-means method, adjusting to voxel adjacency. In a second phase, a class prediction was carried out with a multinomial logistic regression supervised learning method; the outcome variables were the percentage of assignment, class overrepresentation, and degree of voxel adjacency. RESULTS Unsupervised learning of the tumor in its diagnosis shows up to 14 well-differentiated tumor areas. In the supervised learning phase, there is a higher percentage of assigned classes (P < 0.01), less overrepresentation of classes (P < 0.01), and greater adjacency (55% vs. 33%) in cases of true tumor progression compared with pseudoprogression. CONCLUSIONS True tumor progression preserves the multidimensional characteristics of the basal tumor at the voxel and region of interest level, resulting in a characteristic differential pattern when supervised learning is used.
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Affiliation(s)
- José Luis Thenier-Villa
- Department of Neurosurgery, University Hospital Arnau de Vilanova, Lleida, Spain; Department of Neurosurgery, Vall d'Hebron University Hospital, Barcelona, Spain; Neurotrauma and Neurosurgery Research Unit (UNINN), Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
| | - Francisco Ramón Martínez-Ricarte
- Department of Neurosurgery, Vall d'Hebron University Hospital, Barcelona, Spain; Neurotrauma and Neurosurgery Research Unit (UNINN), Vall d'Hebron Research Institute (VHIR), Barcelona, Spain
| | | | - Fuat Arikan-Abelló
- Department of Neurosurgery, University Hospital Arnau de Vilanova, Lleida, Spain; Department of Neurosurgery, Vall d'Hebron University Hospital, Barcelona, Spain; Neurotrauma and Neurosurgery Research Unit (UNINN), Vall d'Hebron Research Institute (VHIR), Barcelona, Spain
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7
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Galldiks N. Diagnosing pseudoprogression in glioblastoma: A challenging clinical issue. Neurooncol Pract 2024; 11:1-2. [PMID: 38222056 PMCID: PMC10785576 DOI: 10.1093/nop/npad078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024] Open
Affiliation(s)
- Norbert Galldiks
- Department of Neurology, Medical Faculty and University Hospital of Cologne, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Duesseldorf, Germany
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Dagher R, Gad M, da Silva de Santana P, Sadeghi MA, Yewedalsew SF, Gujar SK, Yedavalli V, Köhler CA, Khan M, Tavora DGF, Kamson DO, Sair HI, Luna LP. Umbrella review and network meta-analysis of diagnostic imaging test accuracy studies in Differentiating between brain tumor progression versus pseudoprogression and radionecrosis. J Neurooncol 2024; 166:1-15. [PMID: 38212574 DOI: 10.1007/s11060-023-04528-8] [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: 11/05/2023] [Accepted: 12/01/2023] [Indexed: 01/13/2024]
Abstract
PURPOSE In this study we gathered and analyzed the available evidence regarding 17 different imaging modalities and performed network meta-analysis to find the most effective modality for the differentiation between brain tumor recurrence and post-treatment radiation effects. METHODS We conducted a comprehensive systematic search on PubMed and Embase. The quality of eligible studies was assessed using the Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) instrument. For each meta-analysis, we recalculated the effect size, sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio from the individual study data provided in the original meta-analysis using a random-effects model. Imaging technique comparisons were then assessed using NMA. Ranking was assessed using the multidimensional scaling approach and by visually assessing surface under the cumulative ranking curves. RESULTS We identified 32 eligible studies. High confidence in the results was found in only one of them, with a substantial heterogeneity and small study effect in 21% and 9% of included meta-analysis respectively. Comparisons between MRS Cho/NAA, Cho/Cr, DWI, and DSC were most studied. Our analysis showed MRS (Cho/NAA) and 18F-DOPA PET displayed the highest sensitivity and negative likelihood ratios. 18-FET PET was ranked highest among the 17 studied techniques with statistical significance. APT MRI was the only non-nuclear imaging modality to rank higher than DSC, with statistical insignificance, however. CONCLUSION The evidence regarding which imaging modality is best for the differentiation between radiation necrosis and post-treatment radiation effects is still inconclusive. Using NMA, our analysis ranked FET PET to be the best for such a task based on the available evidence. APT MRI showed promising results as a non-nuclear alternative.
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Affiliation(s)
- Richard Dagher
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Neuroradiology, Johns Hopkins Hospital, 600 N Wolfe Street Phipps B100F, Baltimore, MD, 21287, USA
| | - Mona Gad
- Diagnostic Radiology Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | | | - Mohammad Amin Sadeghi
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Neuroradiology, Johns Hopkins Hospital, 600 N Wolfe Street Phipps B100F, Baltimore, MD, 21287, USA
| | | | - Sachin K Gujar
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Neuroradiology, Johns Hopkins Hospital, 600 N Wolfe Street Phipps B100F, Baltimore, MD, 21287, USA
| | - Vivek Yedavalli
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Neuroradiology, Johns Hopkins Hospital, 600 N Wolfe Street Phipps B100F, Baltimore, MD, 21287, USA
| | - Cristiano André Köhler
- Medical Sciences Post-Graduation Program, Department of Internal Medicine, School of Medicine, Federal University of Ceará, Fortaleza, Brazil
| | - Majid Khan
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Neuroradiology, Johns Hopkins Hospital, 600 N Wolfe Street Phipps B100F, Baltimore, MD, 21287, USA
| | | | - David Olayinka Kamson
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, MD, USA
| | - Haris I Sair
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Neuroradiology, Johns Hopkins Hospital, 600 N Wolfe Street Phipps B100F, Baltimore, MD, 21287, USA
| | - Licia P Luna
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Neuroradiology, Johns Hopkins Hospital, 600 N Wolfe Street Phipps B100F, Baltimore, MD, 21287, USA.
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Alizadeh M, Broomand Lomer N, Azami M, Khalafi M, Shobeiri P, Arab Bafrani M, Sotoudeh H. Radiomics: The New Promise for Differentiating Progression, Recurrence, Pseudoprogression, and Radionecrosis in Glioma and Glioblastoma Multiforme. Cancers (Basel) 2023; 15:4429. [PMID: 37760399 PMCID: PMC10526457 DOI: 10.3390/cancers15184429] [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: 07/11/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool in initial diagnosis, grading, and survival prediction in patients with glioma and can help differentiate these post-treatment scenarios. Preliminary published studies are promising about the role of radiomics in post-treatment glioma/GBM. However, this field faces significant challenges, including a lack of evidence-based solid data, scattering publication, heterogeneity of studies, and small sample sizes. The present review explores radiomics's capabilities in following patients with glioma/GBM status post-treatment and to differentiate tumor progression, recurrence, pseudoprogression, and radionecrosis.
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Affiliation(s)
- Mohammadreza Alizadeh
- Physiology Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
| | - Nima Broomand Lomer
- Faculty of Medicine, Guilan University of Medical Sciences, Rasht 41937-13111, Iran;
| | - Mobin Azami
- Student Research Committee, Kurdistan University of Medical Sciences, Sanandaj 66186-34683, Iran;
| | - Mohammad Khalafi
- Radiology Department, Tabriz University of Medical Sciences, Tabriz 51656-65931, Iran;
| | - Parnian Shobeiri
- School of Medicine, Tehran University of Medical Sciences, Tehran 14167-53955, Iran; (P.S.); (M.A.B.)
| | - Melika Arab Bafrani
- School of Medicine, Tehran University of Medical Sciences, Tehran 14167-53955, Iran; (P.S.); (M.A.B.)
| | - Houman Sotoudeh
- Department of Radiology and Neurology, Heersink School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL 35294, USA
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Young JS, Al-Adli N, Scotford K, Cha S, Berger MS. Pseudoprogression versus true progression in glioblastoma: what neurosurgeons need to know. J Neurosurg 2023; 139:748-759. [PMID: 36790010 PMCID: PMC10412732 DOI: 10.3171/2022.12.jns222173] [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: 09/19/2022] [Accepted: 12/12/2022] [Indexed: 02/16/2023]
Abstract
Management of patients with glioblastoma (GBM) is complex and involves implementing standard therapies including resection, radiation therapy, and chemotherapy, as well as novel immunotherapies and targeted small-molecule inhibitors through clinical trials and precision medicine approaches. As treatments have advanced, the radiological and clinical assessment of patients with GBM has become even more challenging and nuanced. Advances in spatial resolution and both anatomical and physiological information that can be derived from MRI have greatly improved the noninvasive assessment of GBM before, during, and after therapy. Identification of pseudoprogression (PsP), defined as changes concerning for tumor progression that are, in fact, transient and related to treatment response, is critical for successful patient management. These temporary changes can produce new clinical symptoms due to mass effect and edema. Differentiating this entity from true tumor progression is a major decision point in the patient's management and prognosis. Providers may choose to start an alternative therapy, transition to a clinical trial, consider repeat resection, or continue with the current therapy in hopes of resolution. In this review, the authors describe the invasive and noninvasive techniques neurosurgeons need to be aware of to identify PsP and facilitate surgical decision-making.
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Affiliation(s)
- Jacob S. Young
- Department of Neurological Surgery, University of California, San Francisco, California
| | - Nadeem Al-Adli
- Department of Neurological Surgery, University of California, San Francisco, California
- School of Medicine, Texas Christian University, Fort Worth, Texas
| | - Katie Scotford
- Department of Neurological Surgery, University of California, San Francisco, California
| | - Soonmee Cha
- Department of Neurological Surgery, University of California, San Francisco, California
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Mitchel S. Berger
- Department of Neurological Surgery, University of California, San Francisco, California
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11
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Ouyang ZQ, Zheng GR, Duan XR, Zhang XR, Ke TF, Bao SS, Yang J, He B, Liao CD. Diagnostic accuracy of glioma pseudoprogression identification with positron emission tomography imaging: a systematic review and meta-analysis. Quant Imaging Med Surg 2023; 13:4943-4959. [PMID: 37581048 PMCID: PMC10423382 DOI: 10.21037/qims-22-1340] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 05/15/2023] [Indexed: 08/16/2023]
Abstract
Background Positron emission tomography (PET) imaging is a promising molecular neuroimaging technique and has been proposed as one of the criteria for glioma management. However, there is some controversy concerning the diagnostic accuracy of PET using different radiotracers to differentiate between glioma pseudoprogression (PsP) and true progression (TPR). The purpose of this meta-analysis was to systematically evaluate the methodological quality and clinical value of original studies for distinguishing PsP from TPR in glioma. Methods The Medline, Web of Science, Embase, Cochrane Library, and ClinicalTrials.gov were searched from inception until September 1, 2022. Retrieved clinical studies only investigated the PsP cases but did not include the cases of radiation necrosis or other treatment-related changes. Eligible studies were screened for data extraction and evaluated by 2 independent reviewers using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A random effects model was used to describe summary receiver operating characteristics. Meta-regression and subgroup analyses were applied to identify any sources of heterogeneity. Results The meta-analysis included 20 studies, comprising 317 (30.9%) patients with PsP and 708 (69.1%) with TPR. The summary sensitivity and specificity of general PET for identifying PsP were 0.86 [95% confidence interval (CI): 0.77-0.91] and 0.84 (95% CI: 0.79-0.88), respectively. The statistical heterogeneity was explained by sample size, study design, World Health Organization (WHO) grade, gold standard, and radiotracer type. The summary sensitivity and specificity of O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET PET) were 0.80 (95% CI: 0.68-0.88) and 0.81 (95% CI: 0.75-0.85), respectively. The maximum tumor-to-brain ratio (TBRmax) and the mean tumor-to-brain ratio (TBRmean) both showed excellent diagnostic performance in 18F-FET studies, the summary sensitivity was 0.83 (95% CI: 0.72-0.91) and 0.79 (95% CI: 0.65-0.98), respectively, and the specificity was 0.76 (95% CI: 0.68-0.84) and 0.78 (95% CI: 0.64-0.88), respectively. Conclusions PET imaging is generally accurate in identifying glioma PsP. Considering the credibility of meta-evidence and the practicability of using radiotracer, 18F-FET PET holds the highest clinical value, while TBRmax and TBRmean should be regarded as reliable parameters. PET used with the radiotracers and multiple-parameter combinations of PET with magnetic resonance imaging (MRI) and radiomics analysis have broad research and application prospects, whose diagnostic values for identifying glioma PsP warrant further investigation.
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Affiliation(s)
- Zhi-Qiang Ouyang
- Department of Radiology, Yan’an Hospital of Kunming City (Yan’an Hospital Affiliated to Kunming Medical University), Kunming, China
| | - Guang-Rong Zheng
- Department of Radiology, Yan’an Hospital of Kunming City (Yan’an Hospital Affiliated to Kunming Medical University), Kunming, China
| | - Xi-Rui Duan
- Department of Radiology, Yunnan Cancer Hospital (the Third Affiliated Hospital of Kunming Medical University), Kunming, China
| | - Xue-Rong Zhang
- Department of Radiology, Yunnan Cancer Hospital (the Third Affiliated Hospital of Kunming Medical University), Kunming, China
| | - Teng-Fei Ke
- Department of Radiology, Yunnan Cancer Hospital (the Third Affiliated Hospital of Kunming Medical University), Kunming, China
| | - Sha-Sha Bao
- Department of Radiology, Yunnan Cancer Hospital (the Third Affiliated Hospital of Kunming Medical University), Kunming, China
| | - Jun Yang
- Department of Radiology, Yunnan Cancer Hospital (the Third Affiliated Hospital of Kunming Medical University), Kunming, China
| | - Bin He
- Department of Neurosurgery, the Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Cheng-De Liao
- Department of Radiology, Yan’an Hospital of Kunming City (Yan’an Hospital Affiliated to Kunming Medical University), Kunming, China
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Smith NJ, Deaton TK, Territo W, Graner B, Gauger A, Snyder SE, Schulte ML, Green MA, Hutchins GD, Veronesi MC. Hybrid 18F-Fluoroethyltyrosine PET and MRI with Perfusion to Distinguish Disease Progression from Treatment-Related Change in Malignant Brain Tumors: The Quest to Beat the Toughest Cases. J Nucl Med 2023; 64:1087-1092. [PMID: 37116915 PMCID: PMC10315704 DOI: 10.2967/jnumed.122.265149] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/16/2023] [Indexed: 04/30/2023] Open
Abstract
Conventional MRI has important limitations when assessing for progression of disease (POD) versus treatment-related changes (TRC) in patients with malignant brain tumors. We describe the observed impact and pitfalls of implementing 18F-fluoroethyltyrosine (18F-FET) perfusion PET/MRI into routine clinical practice. Methods: Through expanded-access investigational new drug use of 18F-FET, hybrid 18F-FET perfusion PET/MRI was performed during clinical management of 80 patients with World Health Organization central nervous system grade 3 or 4 gliomas or brain metastases of 6 tissue origins for which the prior brain MRI results were ambiguous. The diagnostic performance with 18F-FET PET/MRI was dually evaluated within routine clinical service and for retrospective parametric evaluation. Various 18F-FET perfusion PET/MRI parameters were assessed, and patients were monitored for at least 6 mo to confirm the diagnosis using pathology, imaging, and clinical progress. Results: Hybrid 18F-FET perfusion PET/MRI had high overall accuracy (86%), sensitivity (86%), and specificity (87%) for difficult diagnostic cases for which conventional MRI accuracy was poor (66%). 18F-FET tumor-to-brain ratio static metrics were highly reliable for distinguishing POD from TRC (area under the curve, 0.90). Dynamic tumor-to-brain intercept was more accurate (85%) than SUV slope (73%) or time to peak (73%). Concordant PET/MRI findings were 89% accurate. When PET and MRI conflicted, 18F-FET PET was correct in 12 of 15 cases (80%), whereas MRI was correct in 3 of 15 cases (20%). Clinical management changed after 88% (36/41) of POD diagnoses, whereas management was maintained after 87% (34/39) of TRC diagnoses. Conclusion: Hybrid 18F-FET PET/MRI positively impacted the routine clinical care of challenging malignant brain tumor cases at a U.S. institution. The results add to a growing body of literature that 18F-FET PET complements MRI, even rescuing MRI when it fails.
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Affiliation(s)
- Nathaniel J Smith
- School of Medicine, Indiana University, Indianapolis, Indiana
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana; and
| | | | - Wendy Territo
- School of Medicine, Indiana University, Indianapolis, Indiana
| | - Brian Graner
- School of Medicine, Indiana University, Indianapolis, Indiana
| | - Andrew Gauger
- School of Medicine, Indiana University, Indianapolis, Indiana
| | - Scott E Snyder
- School of Medicine, Indiana University, Indianapolis, Indiana
| | | | - Mark A Green
- School of Medicine, Indiana University, Indianapolis, Indiana
| | - Gary D Hutchins
- School of Medicine, Indiana University, Indianapolis, Indiana
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Sahu A, Mathew R, Ashtekar R, Dasgupta A, Puranik A, Mahajan A, Janu A, Choudhari A, Desai S, Patnam NG, Chatterjee A, Patil V, Menon N, Jain Y, Rangarajan V, Dev I, Epari S, Sahay A, Shetty P, Goda J, Moiyadi A, Gupta T. The complementary role of MRI and FET PET in high-grade gliomas to differentiate recurrence from radionecrosis. FRONTIERS IN NUCLEAR MEDICINE (LAUSANNE, SWITZERLAND) 2023; 3:1040998. [PMID: 39355021 PMCID: PMC11440952 DOI: 10.3389/fnume.2023.1040998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 04/11/2023] [Indexed: 10/03/2024]
Abstract
Introduction Conventional magnetic resonance imaging (MRI) has limitations in differentiating tumor recurrence (TR) from radionecrosis (RN) in high-grade gliomas (HGG), which can present with morphologically similar appearances. Multiparametric advanced MR sequences and Positron Emission Tomography (PET) with amino acid tracers can aid in diagnosing tumor metabolism. The role of both modalities on an individual basis and combined performances were investigated in the current study. Materials and Methods Patients with HGG with MRI and PET within three weeks were included in the retrospective analysis. The multiparametric MRI included T1-contrast, T2-weighted sequences, perfusion, diffusion, and spectroscopy. MRI was interpreted by a neuroradiologist without using information from PET imaging. 18F-Fluoroethyl-Tyrosine (FET) uptake was calculated from the areas of maximum enhancement/suspicion, which was assessed by a nuclear medicine physician (having access to MRI to determine tumor-to-white matter ratio over a specific region). A definitive diagnosis of TR or RN was made based on the combination of multidisciplinary joint clinic decisions, histopathological examination, and clinic-radiological follow-up as applicable. Results 62 patients were included in the study between July 2018 and August 2021. The histology during initial diagnosis was glioblastoma, oligodendroglioma, and astrocytoma in 43, 7, and 6 patients, respectively, while in 6, no definitive histological characterization was available. The median time from radiation (RT) was 23 months. 46 and 16 patients had TR and RN recurrence, respectively. Sensitivity, specificity, and accuracy using MRI were 98, 77, and 94%, respectively. Using PET imaging with T/W cut-off of 2.65, sensitivity, specificity, and accuracy were 79, 84, and 80%, respectively. The best results were obtained using both imaging combined with sensitivity, specificity, and accuracy of 98, 100, and 98%, respectively. Conclusion Combined imaging with MRI and FET-PET offers multiparametric assessment of glioma recurrence that is correlative and complimentary, with higher accuracy and clinical value.
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Affiliation(s)
- Arpita Sahu
- Department of Radiodiagnosis, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Ronny Mathew
- Department of Radiodiagnosis, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Renuka Ashtekar
- Department of Radiodiagnosis, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Archya Dasgupta
- Department of Radiation Oncology, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Ameya Puranik
- Department of Nuclear Medicine, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Abhishek Mahajan
- Department of Radiology, The Clatterbridge Cancer Centre NHS Foundation Trust, Pembroke Place, Liverpool, United Kingdom
| | - Amit Janu
- Department of Radiodiagnosis, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Amitkumar Choudhari
- Department of Radiodiagnosis, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Subhash Desai
- Department of Radiodiagnosis, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Nandakumar G. Patnam
- Department of Radiodiagnosis, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Abhishek Chatterjee
- Department of Radiation Oncology, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Vijay Patil
- Department of Medical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Nandini Menon
- Department of Medical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Yash Jain
- Department of Nuclear Medicine, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Indraja Dev
- Department of Nuclear Medicine, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Sridhar Epari
- Department of Pathology, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Ayushi Sahay
- Department of Pathology, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Prakash Shetty
- Department of Neurosurgery, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Jayant Goda
- Department of Radiation Oncology, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Aliasgar Moiyadi
- Department of Neurosurgery, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
| | - Tejpal Gupta
- Department of Radiation Oncology, Tata Memorial Hospital and Homi Bhabha National Institute, Mumbai, India
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14
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Chilaca-Rosas MF, Garcia-Lezama M, Moreno-Jimenez S, Roldan-Valadez E. Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation. Diagnostics (Basel) 2023; 13:849. [PMID: 36899993 PMCID: PMC10001394 DOI: 10.3390/diagnostics13050849] [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: 01/29/2023] [Revised: 02/13/2023] [Accepted: 02/20/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND Radiomics refers to a recent area of knowledge that studies features extracted from different imaging techniques and subsequently transformed into high-dimensional data that can be associated with biological events. Diffuse midline gliomas (DMG) are one of the most devastating types of cancer, with a median survival of approximately 11 months after diagnosis and 4-5 months after radiological and clinical progression. METHODS A retrospective study. From a database of 91 patients with DMG, only 12 had the H3.3K27M mutation and brain MRI DICOM files available. Radiomic features were extracted from MRI T1 and T2 sequences using LIFEx software. Statistical analysis included normal distribution tests and the Mann-Whitney U test, ROC analysis, and calculation of cut-off values. RESULTS A total of 5760 radiomic values were included in the analyses. AUROC demonstrated 13 radiomics with statistical significance for progression-free survival (PFS) and overall survival (OS). Diagnostic performance tests showed nine radiomics with specificity for PFS above 90% and one with a sensitivity of 97.2%. For OS, 3 out of 4 radiomics demonstrated between 80 and 90% sensitivity. CONCLUSIONS Several radiomic features demonstrated statistical significance and have the potential to further aid DMG diagnostic assessment non-invasively. The most significant radiomics were first- and second-order features with GLCM texture profile, GLZLM_GLNU, and NGLDM_Contrast.
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Affiliation(s)
- Maria-Fatima Chilaca-Rosas
- Radiotherapy Department, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City 06720, Mexico
| | - Melissa Garcia-Lezama
- Directorate of Research, Hospital General de Mexico “Dr Eduardo Liceaga”, Mexico City 06720, Mexico
| | - Sergio Moreno-Jimenez
- Directorate of Surgery, Instituto Nacional de Neurología y Neurocirugia, “Manuel Velasco Suarez”, Mexico City 14269, Mexico
| | - Ernesto Roldan-Valadez
- Directorate of Research, Hospital General de Mexico “Dr Eduardo Liceaga”, Mexico City 06720, Mexico
- Department of Radiology, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow 119992, Russia
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15
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Hooper GW, Ginat DT. MRI radiomics and potential applications to glioblastoma. Front Oncol 2023; 13:1134109. [PMID: 36874083 PMCID: PMC9982088 DOI: 10.3389/fonc.2023.1134109] [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: 12/29/2022] [Accepted: 02/07/2023] [Indexed: 02/19/2023] Open
Abstract
MRI plays an important role in the evaluation of glioblastoma, both at initial diagnosis and follow up after treatment. Quantitative analysis via radiomics can augment the interpretation of MRI in terms of providing insights regarding the differential diagnosis, genotype, treatment response, and prognosis. The various MRI radiomic features of glioblastoma are reviewed in this article.
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Affiliation(s)
- Grayson W Hooper
- Landstuhl Regional Medical Center, Department of Radiology, Landstuhl, Germany
| | - Daniel T Ginat
- University of Chicago, Department of Radiology, Chicago, IL, United States
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