Distinguishing Brain Tumor Recurrence from Radiation Necrosis: Diagnostic Limitations of Multimodal MRI and the Role of Surgical Resection

Distinguishing Brain Tumor Recurrence from Radiation Necrosis: Diagnostic Limitations of Multimodal MRI and the Role of Surgical Resection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Distinguishing Brain Tumor Recurrence from Radiation Necrosis: Diagnostic Limitations of Multimodal MRI and the Role of Surgical Resection Maria Maggio, Maria Teresa Bozzi, Domenico Sergio Zimatore, Luigi de Gennaro, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6527725/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Differentiating tumor recurrence from radiation necrosis (RN) after radiotherapy in brain tumors remains a diagnostic challenge. Multimodal MRI (mMRI), combining diffusion-weighted imaging (DWI), perfusion-weighted imaging, and MR spectroscopy (MRS), is commonly used to improve diagnostic accuracy, though its reliability is still debated. This study evaluates the diagnostic performance of mMRI and the role of surgical resection in establishing a definitive diagnosis. Methods We retrospectively and prospectively included patients with brain tumors who developed new or enlarging contrast-enhancing lesions on follow-up MRI after stereotactic radiosurgery (SRS) or whole-brain radiotherapy (WBRT). All patients underwent mMRI (DWI, perfusion, and MRS) and were assessed by a multidisciplinary team. Surgical resection was performed based on clinical and radiological findings, and histopathology provided the definitive diagnosis. Diagnostic performance metrics and ROC analysis were calculated. Results Fifty-four patients were included. mMRI suggested recurrence in 40 cases (74%), RN in 12 (22%), and was inconclusive in 2 (4%). Histology confirmed pure tumor recurrence in 27 (50%), mixed recurrence and RN in 9 (17%), and pure RN in 6 (11%). Among cases with mMRI-suggested RN, only 1 (8%) was confirmed as pure RN. Both inconclusive cases had FDG-PET-confirmed recurrence with mixed pathology. mMRI showed a 15% false-positive rate for recurrence and a 92% false-negative rate for RN. The area under the ROC curve was 0.45. Conclusion mMRI has significant limitations in distinguishing recurrence from RN. Surgical resection remains the most reliable diagnostic method and should be integrated into a multidisciplinary approach for managing post-radiotherapy lesions. Brain Neoplasms/diagnostic imaging Radiation Injuries/diagnosis Magnetic Resonance Imaging/methods Neurosurgical Procedures/methods Biopsy/methods Treatment Outcome Figures Figure 1 Introduction Brain tumor resection followed by postoperative radiotherapy (RT), with or without chemotherapy, is considered the standard of care for primary brain tumors and brain metastases [ 1 ]. RT is an effective local treatment; however, it can also cause damage to normal brain tissue, leading to radiation-induced changes detectable on follow-up magnetic resonance imaging (MRI) after the completion of treatment [ 1 ]. One of the most common adverse effects of RT is radiation necrosis (RN), which can develop anywhere between three months and several years after treatment [ 2 ]. RN is particularly relevant in the management of brain metastases and gliomas but can also affect patients undergoing RT for meningiomas and vestibular schwannomas [ 2 , 3 ]. The major clinical challenge is distinguishing RN from tumor recurrence, as both conditions often appear similar on conventional MRI scans [ 4 ]. A key pathological feature of both RN and tumor recurrence is blood-brain barrier breakdown, leading to gliosis and edema. This process results in gadolinium uptake on contrast-enhanced T1-weighted MRI sequences, making the two conditions radiologically indistinguishable [ 4 ]. While conventional MRI remains the gold standard for detecting brain tumors, its limited specificity in differentiating RN from recurrence has led to the adoption of advanced imaging techniques, including MR spectroscopy, perfusion-weighted MRI, dynamic susceptibility contrast perfusion, susceptibility-weighted imaging, diffusion-weighted imaging, single-photon emission computed tomography (SPECT), and positron emission tomography (PET) [ 5 ]. Despite their widespread clinical use, these imaging techniques lack definitive validation for distinguishing RN from tumor recurrence [ 2 ]. Emerging postprocessing methods, such as textural analysis and MR fingerprinting, show promise in improving diagnostic accuracy. However, their implementation is limited by the lack of large, multicenter datasets needed for robust data training [ 5 ]. Given these challenges, histological examination remains the current gold standard for distinguishing RN from tumor recurrence [ 6 ]. However, even biopsy has limitations, as RN is often heterogeneously mixed with tumor recurrence, making it difficult to accurately target viable tumor areas [ 7 ]. Very few studies have directly compared biopsy and surgical resection in the differential diagnosis of RN versus tumor recurrence. Available data suggests that surgical resection is more reliable, with false-positive and false-negative rates of 0% and 5%, respectively, and specificity and sensitivity approaching 100% [ 7 – 9 ]. In contrast, biopsy has false-positive and false-negative rates of approximately 5–10% and 10–30%, respectively, with specificity and sensitivity ranging from 80–90% and 70–90%, respectively [ 8 ]. These findings indicate that surgical resection provides a more definitive diagnosis than biopsy. Study Rationale and Objectives Based on these challenges, we developed the working hypothesis that preoperative imaging and biopsy have lower diagnostic accuracy than surgical resection due to the histological heterogeneity of the lesion, where areas of RN are mixed with viable tumor recurrence. At our institution, multimodal MRI combining diffusion- and perfusion-weighted imaging and spectroscopy is routinely used to aid in the differential diagnosis between RN and tumor recurrence. Thus, the aims of this study were to verify our working hypothesis that histopathological heterogeneity within the surgical specimen is common and to evaluate the accuracy of multimodal MRI in differentiating RN from tumor recurrence, assessing the impact of histopathological heterogeneity on its diagnostic specificity and sensitivity. Materials and methods Study Population This study was conducted using a prospective database of primary and metastatic brain tumors that underwent surgery at our unit between January 2018 and April 2024, comprising a total of 1.260 patients. From this cohort, we selected all patients who subsequently received adjuvant conventional fractionated radiotherapy or radiosurgery, amounting to 750 patients. Within this group, we identified those who exhibited disease progression where the differentiation between RN and local recurrence was unclear on conventional MRI follow-up (presence of new or enlarging contrast-enhancing lesion suggestive of tumor recurrence or radiation necrosis), as determined by a multidisciplinary meeting. As part of our institutional protocol, these patients underwent multimodal MRI, including perfusion, diffusion and spectroscopy sequences, and were reassessed in a second multidisciplinary meeting to determine further management. The final study population consisted of all patients who underwent surgical resection based on the decision of the multidisciplinary meeting. A definitive diagnosis was established through histological examination of the entire surgical specimen, allowing for the distinction between pure tumor recurrence, pure RN, and mixed pathology, where both entities coexisted. Inclusion criteria (1) Prior history of brain metastases treated with conventional fractionated radiotherapy or radiosurgery, (2) presence of new or enlarging contrast-enhancing lesions on conventional MRI suggestive of tumor recurrence or RN, (3) availability of multimodal MRI including perfusion, diffusion and spectroscopy sequences and (4) availability of histopathological confirmation. Patients with inadequate imaging quality or incomplete histopathological data were excluded. Imaging Protocol All patients were scanned using a 1.5T MRI scanner (Philips Achieva, Best, the Netherlands). The protocol included both standard and advanced sequences to provide a comprehensive evaluation. Routine anatomical sequences were: 3D-T1-weighted pre- and post-contrast imaging (TR 6,7 ms, TE 3 ms, matrix 232x229, FOV 256x256 mm, slice thickness 1,1 mm, NEX 1); axial T2-weighted imaging (TR 5296 ms, TE 110 ms, ETL 21, matrix 400x260, FOV 260x211 mm, slice thickness 5mm, slice spacing 1 mm, NEX 1,8), 3D fluid-attenuated inversion recovery (FLAIR, TR 4800 ms, TE 271 ms, TI 1660 ms, matrix192x192, FOV 240x240 mm, slice thickness 1,25 mm, NEX 2). The multimodal magnetic resonance imaging (MRI) workflow incorporated advanced imaging sequences, including diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI), and magnetic resonance spectroscopy (MRS). DWI was performed with the following parameters: TR 3845 ms, TE 88 ms, matrix 152x106 mm, FOV 230x230 mm, slice thickness 5 mm, slice spacing 1 mm, b value 1000 s/mm², NEX 1. SWI was performed with the following parameters: TR 52 ms, TE 12 ms, delta TE 11 ms, matrix 272x220, FOV 230x186 mm, slice thickness 1mm, slice spacing − 1 mm, NEX 1. DSC-PWI was performed using a gradient-echo echo-planar imaging (GRE-EPI) sequence with the following parameters: TR 1802 ms, TE 40 ms, FA 75, slice thickness 5 mm, FOV 224x224, total scan duration 1,10 minutes, NEX 1. A 15 mL bolus of gadolinium-based contrast agent (Pixxoscan, 1 mmol/kg, GE Healthcare) was administered intravenously at an injection rate of 3–5 mL/s, adjusted according to the patient’s venous access. Point-Resolved Spectroscopy (PRESS) Magnetic Resonance Spectroscopy (MRS) was conducted in either single-voxel or multi-voxel modes, determined by tumor size and localization. The long TE parameter set comprised: TR 2000 ms, TE 144 ms, and NSA of 1 (multi-voxel) or 128 (single-voxel). The short TE parameter set was: TR 2000 ms, TE 40 ms, and NSA of 1 (multi-voxel) or 128 (single-voxel). FOV and voxel dimensions were adjusted based on the anatomical region of interest. Diffusion, Perfusion and Spectroscopy MRI Analysis Post-processing was performed using Philips IntelliSpace Portal v7.0 (Best, the Netherlands). Diffusion-weighted imaging (DWI) was analyzed using corresponding apparent diffusion coefficient (ADC) maps. Regions of interest (ROIs) were placed on ADC maps within the contrast-enhancing lesion and areas suspected of tumor recurrence, and ADC values were recorded. Tumor recurrence was expected to demonstrate lower ADC values due to increased cellularity, while radiation necrosis was expected to exhibit higher ADC values, reflecting necrotic tissue and decreased cellularity. A definitive ADC threshold for distinguishing between RN and recurrence was not established, given its potential variability based on tumor type, treatment protocols, and imaging parameters [ 10 ]. Consequently, the interpretation of diffusion-derived changes following radiotherapy necessitates careful consideration, as similar diffusion metrics can reflect both tumor recurrence (diffusion restriction due to hypercellularity) and RN (diffusion restriction due to post-radiotherapy coagulative necrosis) [ 11 , 12 ]. The proportion of hypointense signal within lesions on susceptibility-weighted imaging (SWI) was quantified by comparison with the pre-radiotherapy SWI signal. Hypointensity on SWI is attributed to the presence of hemosiderin, an iron-containing blood breakdown product, frequently observed in radionecrotic tissue. However, hemorrhage, which also results in hypointensity, can occur in recurrent tumors due to increased vascularity. Furthermore, the diagnostic utility of SWI in differentiating RN from recurrence demonstrates variability across tumor types, with reported higher robustness in high-grade gliomas compared to metastatic lesions [ 13 ]. For perfusion analysis, relative cerebral blood volume (rCBV) maps were generated. Regions of interest (ROIs) were manually placed over the enhancing lesion and contralateral normal appearing white matter to calculate normalized rCBV (nRCBV). An nRCBV value exceeding 1.5–2 relative to the contralateral white matter ROI was considered indicative of tumor recurrence. Magnetic resonance spectroscopy (MRS) was performed utilizing single-voxel or multi-voxel acquisition techniques. Metabolic ratios, particularly focusing on choline (Cho), creatine (Cr), and N-acetylaspartate (NAA), were analyzed. Tumor recurrence was characterized by elevated Cho/NAA and Cho/Cr ratios, indicative of increased cellular proliferation. Conversely, RN exhibited a decreased Cho/NAA ratio with elevated lipid and lactate peaks, reflecting necrotic tissue and oxidative stress. The integrated analysis of relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC), and MRS parameters were employed to enhance diagnostic accuracy (Multiparametric MRI) [ 10 ]. In cases of discordant MRI findings, a tiered diagnostic strategy was implemented. When one modality suggested radiation necrosis while two favored tumor recurrence, further evaluation, including clinical assessment, longitudinal imaging follow-up, and metabolic imaging (FDG-PET) when clinically warranted, was conducted. Surgical intervention was pursued if the lesion demonstrated progression or metabolic activity, whereas stable or regressing lesions were managed conservatively. Similarly, when two modalities indicated RN while one suggested tumor recurrence, a conservative management approach was initially adopted, with close (bimonthly) radiological and clinical monitoring. If the lesion remained stable or regressed, RN was confirmed. However, lesion progression prompted further evaluation through metabolic imaging (FDG-PET) or histopathological confirmation. This stepwise diagnostic algorithm facilitated refined diagnostic accuracy, minimizing unnecessary interventions while ensuring the timely identification of true tumor recurrences. Histopathological Analysis All patients underwent surgical resection within 1 to 2 weeks of MRI evaluation. The surgical specimen was fixed overnight in 10% neutral-buffered formalin, sampled and embedded in paraffin blocks, sectioned at 4-µm thickness and stained with haematoxylin-eosin (H&E). Histopathological analysis was performed by a board-certified neuropathologist blinded to MRI report, who classified the lesions as tumor recurrence or RN based on cellularity, vascular status, and necrotic type (in particular, radiation necrosis comprises coagulative and fibrinoid necrosis, gliosis, wall thickening and hyalinization of vessels, telangiectasia and calcium deposition, situated mainly in the white matter). Statistical Analysis The diagnostic accuracy of DSC perfusion and DWI MRI, and MRS were assessed using receiver operating characteristic (ROC) curve analysis. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using histopathology as the reference standard. The optimal rCBV, ADC, and Cho/NAA cut-off values for differentiating tumor recurrence from radiation necrosis were determined using the Youden index. These cut-off values were derived from the study cohort by generating ROC curves and selecting the thresholds that maximized the sum of sensitivity and specificity. Additionally, these values were compared to previously validated thresholds from the literature to assess consistency. Interobserver variability in rCBV, ADC, and MRS measurements were evaluated using the intraclass correlation coefficient (ICC). A p-value < 0.05 was considered statistically significant. Data were analyzed using [Statistical Software, Version]. Results We included 54 patients in the study, (22 males and 32 females), with a median age of 57 years. All patients had previously undergone a gross total resection of their tumor. Histopathological examination identified primary tumors in 48 patients, accounting for 89% of the cohort. Among them, 38 were gliomas, including 27 grade IV, 2 grade III, 8 grade II, and 1 grade I. Seven patients had meningiomas, of which 6 were classified as atypical WHO grade II and 1 as papillary WHO grade III. Additionally, 1 patient had a solitary fibrous tumor (grade III), 1 had a medulloblastoma, and 1 had a vestibular schwannoma. In the remaining 6 patients, representing 11% of the study population, histopathological analysis confirmed metastatic disease, with 4 cases originating from breast cancer, 1 from ovarian cancer, and 1 from lung cancer. A conventional MRI showing the presence of new or enlarging contrast-enhancing lesions suggestive of tumor recurrence or RN was performed in 24 patients (44%) due to the onset of symptoms. Specifically, 10 patients exhibited new or worsening neurological deficits, 6 patients experienced acute symptoms related to increased intracranial pressure, and 8 patients presented with seizures. The remaining 30 patients underwent MRI as part of routine follow-up. All 54 patients included in the study underwent a new MRI scan incorporating DSC perfusion, DWI, and MRS sequences in addition to standard sequences, including T1 with contrast, T2, and FLAIR. The interpretation of multimodal MRI suggested tumor recurrence in 40 patients (70%) and radiation necrosis in 12 patients (22%), with complete concordance among the three modalities in all these cases. In 2 cases (4% of the cohort), multimodal MRI results were discordant, with DSC perfusion MRI and MRS indicating tumor recurrence, while DWI suggested RN. These two cases underwent additional FDG-PET imaging, which also supported tumor recurrence due to high metabolic activity (Table 1 ). A multidisciplinary team evaluated each case and determined the surgical indication for both diagnostic and therapeutic purposes. Among the 12 patients with MRI findings suggestive of RN, surgical intervention was recommended in 10 cases due to severe symptoms persisting despite corticosteroid treatment, while in 2 cases, surgery was indicated due to rapid radiological progression. In all cases where multimodal MRI or metabolic imaging suggested tumor recurrence, the multidisciplinary team advised surgical intervention. Table 1 Patient demographics Patient First Surgery Histological Diagnosis Adjuvant Therapy Disease Progression F 69 2010 Atypical Meningioma SRT June 2018 M 39 2004 Atypical Meningioma SRT September 2018 F 47 2016 Grade II Astrocytoma CT + SRT November 2018 F 62 April 2018 Grade IV GBM STUPP protocol November 2018 M 59 March 2019 Grade IV GBM STUPP protocol October 2019 F 53 2006 Grade II Astrocytoma CT + SRT November 2019 F 57 May 2019 Grade IV GBM STUPP protocol January 2020 F 47 August 2019 Grade IV GBM STUPP protocol April 2020 M 67 2019 Grade IV GBM STUPP protocol July 2020 F 36 2012 Grade II Astrocytoma SRT + CT + cyberknife September 2020 M 56 April 2020 Grade IV GBM STUPP protocol November 2020 F 42 2010–2013 Atypical Meningioma SRT December 2020 M 60 2020 Grade IV GBM STUPP protocol January 2021 F 21 February 2020 Grade IV GBM STUPP protocol January 2021 M 67 August 2020 Grade IV GBM STUPP protocol February 2021 F 55 June 2020 Grade IV GBM STUPP protocol February 2021 M 46 2006 Vestibular schwannoma SRT February 2021 F 71 2015–2017 Grade II Meningioma Cyberknife + SRT April 2021 M 66 July 2020 Lung cancer metastasis SRT May 2021 M 53 May 2018 Grade IV GBM STUPP protocol June 2021 F 66 August 2018 Grade III Glioma STUPP protocol June 2021 M 49 October 2019 Grade III Hemangiopericytoma SRT July 2021 F 64 October 2019 Grade IV GBM STUPP protocol August 2021 M 66 2020 Grade IV GBM STUPP protocol August 2021 F 43 November 2020 Grade III Papillary meningioma SRT December 2021 M 50 April 2021 Grade IV GBM STUPP protocol March 2022 F 65 June 2021 Grade IV GBM STUPP protocol April 2022 F 65 November 2021 Grade IV GBM STUPP protocol May 2022 M 53 July 2021 Grade IV GBM STUPP protocol May 2022 F 63 2018 Breast cancer metastasis SRT May 2022 F 61 2016 – March 21 Breast cancer metastasis SRT July 2022 F 68 May 2021 Grade IV GBM STUPP protocol July 2022 M 51 1981- June 2018 Grade I Pilocytic astrocytoma SRT August 2022 M 71 February 2022 Grade IV GBM STUPP protocol October 2022 M 78 March 2022 Grade IV GBM STUPP protocol October 2022 F 66 2011 Grade III Glioma STUPP protocol December 2022 F 56 February 2021 Breast cancer metastasis SRT December 2022 M 55 July 2022 Grade IV GBM STUPP protocol December 2022 M 58 September 2020 Grade IV GBM STUPP protocol December 2022 F 56 February 2021 Breast cancer metastasis SRT December 2022 F 72 April 2019 Grade II Astrocytoma STUPP protocol January 2023 F 67 November 2021 Grade IV GBM STUPP protocol February 2023 F 16 November 2013 Grade IV medulloblastoma STUPP protocol March 2023 F 42 2010-2013-2020 Atypical Meningioma SRT December 2022 F 72 November 2022 Atypical Meningioma SRT May 2023 M 53 January 2022 Grade IV GBM STUPP protocol May 2023 F 64 2005 Grade II Oligodendroglioma whole brain RT August 2023 M 31 February 2022 Grade II Astrocytoma SRT November 2023 F 63 2021 Grade IV GBM SRT November 2023 M 22 March 2023 Grade IV GBM STUPP protocol February 2024 M 65 October 2023 Grade IV GBM STUPP protocol February 2024 F 54 November 2022 Grade II Oligodendroglioma STUPP protocol March 2024 F 59 September 2020 Ovarian cancer metastasis SRT March 2024 F 23 February 2022 Grade II Ependimoma SRT April 2024 CT, Chemotherapy; STR, Stereotactic Radiation Therapy; GBM, Glioblastoma multiforme The radiological diagnosis was then correlated with the histological findings from the second-look analysis (Table 2 ). Among the 40 patients for whom all multimodal MRI modalities suggested tumor recurrence, histological examination confirmed recurrence in 27 patients (67%), while 7 patients (18%) had recurrence associated with radiation necrosis, and 6 patients (15%) had pure radiation necrosis without evidence of tumor progression. This resulted in a 15% false-positive rate, indicating that while multimodal MRI demonstrated good sensitivity for detecting recurrence, it had limitations in distinguishing pure recurrence from radiation necrosis. Conversely, among the 12 patients for whom all multimodal MRI modalities suggested radiation necrosis, histopathological analysis confirmed pure radiation necrosis in only 1 patient (8%). The remaining cases revealed pure tumor recurrence in 8 patients (67%) and tumor recurrence associated with radiation necrosis in 3 patients (25%), highlighting the low sensitivity of multimodal MRI for detecting radiation necrosis. Additionally, in both cases where multimodal MRI was inconclusive and FDG-PET favored tumor recurrence, histopathological examination confirmed the presence of recurrence associated with radiation necrosis. Table 2 Correlation between second look histological diagnosis and radiological features Patient Histological Diagnosis Second Look Histological Diagnosis Radiological Features F 69 Atypical Meningioma Atypical Meningioma recurrence M 39 Atypical Meningioma Atypical Meningioma recurrence F 47 Grade II Astrocytoma Grade II Astrocytoma + RN recurrence F 62 Grade IV GBM Grade IV GBM recurrence M 59 Grade IV GBM Grade IV GBM recurrence F 53 Grade II Astrocytoma Grade II Astrocytoma recurrence F 57 Grade IV GBM Grade IV GBM RN F 47 Grade IV GBM RN without recurrence recurrence M 67 Grade IV GBM Grade IV GBM recurrence F 36 Grade II Astrocytoma Grade IV GBM recurrence M 56 Grade IV GBM Grade IV GBM + RN RN F 42 Atypical Meningioma Atypical Meningioma recurrence M 60 Grade IV GBM Grade IV GBM recurrence F 21 Grade IV GBM Grade IV GBM recurrence M 67 Grade IV GBM Grade IV GBM RN F 55 Grade IV GBM Grade IV GBM + RN recurrence M 46 Vestibular schwannoma Vestibular schwannoma recurrence F 71 Grade II Meningioma Grade II Meningioma recurrence M 66 Lung cancer metastasis Lung cancer metastasis + RN recurrence M 53 Grade IV GBM RN without recurrence recurrence F 66 Grade III Glioma Grade III Glioma recurrence M 49 Grade III Hemangiopericytoma RN without recurrence RN F 64 Grade IV GBM RN without recurrence recurrence M 66 Grade IV GBM Grade IV GBM recurrence F 43 Grade III Papillary meningioma Grade III Papillary meningioma recurrence M 50 Grade IV GBM Grade IV GBM + RN dubious F 65 Grade IV GBM Grade IV GBM recurrence F 65 Grade IV GBM Grade IV GBM recurrence M 53 Grade IV GBM Grade IV GBM recurrence F 63 Breast cancer metastasis Breast cancer metastasis RN F 61 Breast cancer metastasis RN without recurrence recurrence F 68 Grade IV GBM Grade IV GBM recurrence M 51 Grade I Pilocytic astrocytoma Grade I Pilocytic astrocytoma recurrence M 71 Grade IV GBM Grade IV GBM + RN recurrence M 78 Grade IV GBM Grade IV GBM recurrence F 66 Grade III Glioma Grade IV GBM recurrence F 56 Breast cancer metastasis Breast cancer metastasis RN M 55 Grade IV GBM Grade IV GBM + RN recurrence M 58 Grade IV GBM RN without recurrence recurrence F 56 Breast cancer metastasis Breast cancer metastasis + RN RN F 72 Grade II Astrocytoma Grade II Astrocytoma RN F 67 Grade IV GBM Grade IV GBM + RN RN F 16 Grade IV medulloblastoma Grade IV medulloblastoma RN F 42 Atypical Meningioma Atypical Meningioma + RN recurrence F 72 Atypical Meningioma Atypical Meningioma RN M 53 Grade IV GBM Grade IV GBM recurrence F 64 Grade II Oligodendroglioma Grade II Oligodendroglioma recurrence M 31 Grade II Astrocytoma Grade II Astrocytoma recurrence F 63 Grade IV GBM Grade IV GBM + RN dubious M 22 Grade IV GBM Grade IV GBM + RN recurrence M 65 Grade IV GBM Grade IV GBM RN F 54 Grade II Oligodendroglioma Grade II Oligodendroglioma recurrence F 59 Ovarian cancer metastasis RN without recurrence recurrence F 23 Grade II Ependimoma Grade II Ependimoma recurrence GBM, Glioblastoma multiforme; RN, radiation necrosis Using the collected data, we generated a ROC curve to evaluate the diagnostic performance of multimodal MRI in distinguishing tumor recurrence from radiation necrosis. The Area Under the Curve (AUC) was 0.45, indicating that multimodal MRI performed worse than chance in differentiating the two conditions. This poor diagnostic performance suggests a high rate of both false positives and false negatives, limiting its reliability in clinical decision-making. The study cohort-derived cut-off values were compared with previously validated thresholds in the literature, revealing no significant differences, thus eliminating the need for external validation (Fig. 1 ). Discussion Our study highlights the significant limitations of multimodal MRI in differentiating tumor recurrence from radiation necrosis. While multimodal MRI demonstrated good sensitivity for detecting tumor recurrence, its false-positive rate of 15% indicates that a subset of patients diagnosed with recurrence had pure radiation necrosis, leading to potential overtreatment. Conversely, the low sensitivity (8%) for detecting radiation necrosis suggests that some patients classified as having RN based on imaging alone may in fact harbor recurrent tumor tissue, reinforcing the risk of undertreatment. These findings are consistent with prior studies that have reported similar challenges in distinguishing these two conditions using advanced imaging techniques [ 14 ]. Previous literature suggests that while DSC perfusion, DWI, and MRS can provide valuable adjunctive information, their specificity remains limited due to overlapping imaging characteristics between recurrent tumor and radiation necrosis [ 15 ]. For instance, some studies have shown that while elevated rCBV values on perfusion MRI are commonly associated with tumor recurrence, radiation necrosis can also exhibit increased perfusion due to inflammatory neovascularization [ 16 ]. Similarly, ADC values on DWI, though useful, have substantial overlap between these two conditions, making them unreliable as standalone diagnostic tools [ 2 ]. Furthermore, our findings emphasize the importance of histopathological confirmation in cases of diagnostic uncertainty. Biopsy alone is often unreliable, as it may sample an area of radiation necrosis rather than viable tumor tissue, leading to misdiagnosis. This limitation is particularly relevant in our study, where a substantial proportion of cases revealed a mixed pathology of radiation necrosis coexisting with tumor recurrence. The AUC of 0.45 observed in our study further underscores the poor performance of multimodal MRI, reinforcing the necessity of histological evaluation for definitive diagnosis. Study limitations Despite the valuable insights provided by this study, several limitations should be acknowledged. First, the sample size of 54 patients, though sufficient for preliminary analysis, remains relatively small for a study assessing diagnostic accuracy. A larger, multicenter cohort would be beneficial to improve the generalizability of our findings. Second, all imaging analyses were conducted using a 1.5T MRI scanner, which may have lower spatial resolution and sensitivity compared to 3T MRI systems commonly used in advanced neuroimaging studies. Third, while histopathological analysis following surgical resection was considered the gold standard, there remains an inherent risk of sampling bias even with extensive tissue examination. Additionally, the study focused exclusively on patients who underwent surgical resection, potentially introducing selection bias, as patients with stable or regressing lesions managed conservatively were not included. Finally, while we implemented a standardized protocol for MRI acquisition and interpretation, interobserver variability in image analysis was not explicitly assessed, which may influence the reproducibility of our findings. Given these limitations, future studies should focus on larger, multicenter cohorts, incorporate higher-resolution MRI protocols, and explore the role of emerging diagnostic techniques, such as radiomics and machine learning-based texture analysis, to enhance diagnostic precision. Furthermore, a comparative evaluation of metabolic imaging techniques, such as FET-PET or FDG-PET, in combination with multimodal MRI, could provide additional insights into refining the differentiation between tumor recurrence and radiation necrosis. Conclusions Multimodal MRI has significant limitations in distinguishing recurrence from RN. Surgical resection remains the most reliable diagnostic method and should be integrated into a multidisciplinary approach for managing post-radiotherapy lesions. Declarations Author contributions Conceptualization, M. Maggio; Methodology, M. Maggio and D.S. Zimatore and G. Ingravallo; Vali­dation, F. Signorelli; Formal Analysis, F. Signorelli; Investigation, M. Maggio; Resources, M. Maggio; Data Curation, M. Maggio; Writing—Original Draft Preparation, M. Maggio; Writ­ing—Review and Editing, F. Signorelli and R. Messina; Vi­sualization, L. de Gennaro; Supervision, M.T. Bozzi; Project Administration, R. Messina. All authors have read and approved the submit­ted version of the manuscript. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Data availability The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. 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Clin Radiol 77(8):e585–e591. 10.1016/j.crad.2022.05.005 Bobek-Billewicz B, Stasik-Pres G, Majchrzak H, Zarudzki L (2010) Differentiation between brain tumor recurrence and radiation injury using perfusion, diffusion-weighted imaging and MR spectroscopy. Folia Neuropathol 48(2):81–92 Lee D, Riestenberg RA, Haskell-Mendoza A, Bloch O (2020) Brain Metastasis Recurrence Versus Radiation Necrosis: Evaluation and Treatment. Neurosurg Clin N Am 31(4):575–587. 10.1016/j.nec.2020.06.007 Wang B, Zhao B, Zhang Y, Ge M, Zhao P, Na S, Li C, Pang Q, Xu S, Liu Y Absolute CBV for the differentiation of recurrence and radionecrosis of brain metastases after gamma knife radiotherapy: a comparison with relative CBV. Clin Radiol 73(8):758.e1-758.e7. doi: 10.1016/j.crad.2018.04.006 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6527725","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":448564852,"identity":"c2a2eba3-a8d8-4c92-b961-d058fa880e26","order_by":0,"name":"Maria Maggio","email":"","orcid":"","institution":"Neurosurgery Unit, University Hospital Policlinico of Bari, Department of Translational Biomedicine and Neurosciences (DiBraiN), University of Bari Aldo Moro, Bari, Italy","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Maggio","suffix":""},{"id":448564853,"identity":"e9cd8f65-245a-416f-b1b3-16f95eb689f7","order_by":1,"name":"Maria Teresa Bozzi","email":"","orcid":"","institution":"Neurosurgery Unit, University Hospital Policlinico of Bari, Department of Translational Biomedicine and Neurosciences (DiBraiN), University of Bari Aldo Moro, Bari, Italy","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Teresa","lastName":"Bozzi","suffix":""},{"id":448564854,"identity":"a9c4f61a-29d8-4f38-be42-b65f764ad42f","order_by":2,"name":"Domenico Sergio Zimatore","email":"","orcid":"","institution":"Interventional and Diagnostic Neuroradiology Department, University Hospital Policlinico of Bari, Bari, Italy","correspondingAuthor":false,"prefix":"","firstName":"Domenico","middleName":"Sergio","lastName":"Zimatore","suffix":""},{"id":448564855,"identity":"72611fa7-d3c3-482d-bbb9-aab23adcfa90","order_by":3,"name":"Luigi de Gennaro","email":"","orcid":"","institution":"Neurosurgery Unit, University Hospital Policlinico of Bari, Department of Translational Biomedicine and Neurosciences (DiBraiN), University of Bari Aldo Moro, Bari, Italy","correspondingAuthor":false,"prefix":"","firstName":"Luigi","middleName":"","lastName":"de Gennaro","suffix":""},{"id":448564856,"identity":"04a4990f-04ce-40ee-a43f-3039e5315acc","order_by":4,"name":"Giuseppe Ingravallo","email":"","orcid":"","institution":"Section of Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari \"Aldo Moro\", Bari, Italy","correspondingAuthor":false,"prefix":"","firstName":"Giuseppe","middleName":"","lastName":"Ingravallo","suffix":""},{"id":448564857,"identity":"cc61fa3b-a96a-48e0-bc63-2a2d8140b7a8","order_by":5,"name":"Raffaella Messina","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABG0lEQVRIie2PMUvDQBiGvyMQB78z6ycNzV9oEdoO4m/pEXAqOhREUPRKoC6666J/wVKoHQMBs9wPULqkdO2gWwpRvEBRlJw4OtwDN3zwPrzvAVgs/xKWvQB0/PVFABsSQOjnGhWnea2T+KVgvFaMzneltLq6WiummvZFwmR+TOjJTTHPi86Bd7NsZfPpHmwFslLxVcgGl4qQYj7e4UPq06zXbggVGocRHKaZTiLEfFJjkoSc9Vokho5Z8RZs8PZOGMT8YZUXJO6eVamcmxUKWcQlYUO3ALok7p+wVJJflAWL/EfCZsLHNb1QjNT+EQmVout2DcP095enu/V6ejV6zYszcZsmk+3V9KQeRHF1zSfOjxsrUxaLxWL5Ex+j6lXEaLw53AAAAABJRU5ErkJggg==","orcid":"","institution":"Neurosurgery Unit, University Hospital Policlinico of Bari, Department of Translational Biomedicine and Neurosciences (DiBraiN), University of Bari Aldo Moro, Bari, Italy","correspondingAuthor":true,"prefix":"","firstName":"Raffaella","middleName":"","lastName":"Messina","suffix":""},{"id":448564858,"identity":"c017d98f-be31-4ddd-a32d-6d4d05aecdd0","order_by":6,"name":"Francesco Signorelli","email":"","orcid":"","institution":"Neurosurgery Unit, University Hospital Policlinico of Bari, Department of Translational Biomedicine and Neurosciences (DiBraiN), University of Bari Aldo Moro, Bari, Italy","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Signorelli","suffix":""}],"badges":[],"createdAt":"2025-04-25 09:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6527725/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6527725/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82124910,"identity":"68a613c1-4403-4c7d-a689-883e23e98c9a","added_by":"auto","created_at":"2025-05-07 03:42:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":249826,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve. The area under the curve (AUC) illustrates the value of multimodal MRI with perfusion in the differential diagnosis between tumor recurrence and RN. The AUC is 0.45. This means that the performance of the multimodal MRI test is worse than random chance in distinguishing between tumor recurrence and RN. This indicates that the multimodal MRI is not very effective in correctly differentiating the two conditions.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6527725/v1/57377b1846dd3877bfd7096f.jpeg"},{"id":82174731,"identity":"5cfa49ab-0169-4dee-bdea-d8659c79d7c9","added_by":"auto","created_at":"2025-05-07 11:01:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1284409,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6527725/v1/5ff59aa9-aebd-4aca-bbd8-edf34809ca35.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distinguishing Brain Tumor Recurrence from Radiation Necrosis: Diagnostic Limitations of Multimodal MRI and the Role of Surgical Resection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBrain tumor resection followed by postoperative radiotherapy (RT), with or without chemotherapy, is considered the standard of care for primary brain tumors and brain metastases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. RT is an effective local treatment; however, it can also cause damage to normal brain tissue, leading to radiation-induced changes detectable on follow-up magnetic resonance imaging (MRI) after the completion of treatment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the most common adverse effects of RT is radiation necrosis (RN), which can develop anywhere between three months and several years after treatment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. RN is particularly relevant in the management of brain metastases and gliomas but can also affect patients undergoing RT for meningiomas and vestibular schwannomas [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The major clinical challenge is distinguishing RN from tumor recurrence, as both conditions often appear similar on conventional MRI scans [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA key pathological feature of both RN and tumor recurrence is blood-brain barrier breakdown, leading to gliosis and edema. This process results in gadolinium uptake on contrast-enhanced T1-weighted MRI sequences, making the two conditions radiologically indistinguishable [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While conventional MRI remains the gold standard for detecting brain tumors, its limited specificity in differentiating RN from recurrence has led to the adoption of advanced imaging techniques, including MR spectroscopy, perfusion-weighted MRI, dynamic susceptibility contrast perfusion, susceptibility-weighted imaging, diffusion-weighted imaging, single-photon emission computed tomography (SPECT), and positron emission tomography (PET) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite their widespread clinical use, these imaging techniques lack definitive validation for distinguishing RN from tumor recurrence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Emerging postprocessing methods, such as textural analysis and MR fingerprinting, show promise in improving diagnostic accuracy. However, their implementation is limited by the lack of large, multicenter datasets needed for robust data training [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven these challenges, histological examination remains the current gold standard for distinguishing RN from tumor recurrence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, even biopsy has limitations, as RN is often heterogeneously mixed with tumor recurrence, making it difficult to accurately target viable tumor areas [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Very few studies have directly compared biopsy and surgical resection in the differential diagnosis of RN versus tumor recurrence. Available data suggests that surgical resection is more reliable, with false-positive and false-negative rates of 0% and 5%, respectively, and specificity and sensitivity approaching 100% [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In contrast, biopsy has false-positive and false-negative rates of approximately 5\u0026ndash;10% and 10\u0026ndash;30%, respectively, with specificity and sensitivity ranging from 80\u0026ndash;90% and 70\u0026ndash;90%, respectively [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These findings indicate that surgical resection provides a more definitive diagnosis than biopsy.\u003c/p\u003e\n\u003ch3\u003eStudy Rationale and Objectives\u003c/h3\u003e\n\u003cp\u003eBased on these challenges, we developed the working hypothesis that preoperative imaging and biopsy have lower diagnostic accuracy than surgical resection due to the histological heterogeneity of the lesion, where areas of RN are mixed with viable tumor recurrence. At our institution, multimodal MRI combining diffusion- and perfusion-weighted imaging and spectroscopy is routinely used to aid in the differential diagnosis between RN and tumor recurrence. Thus, the aims of this study were to verify our working hypothesis that histopathological heterogeneity within the surgical specimen is common and to evaluate the accuracy of multimodal MRI in differentiating RN from tumor recurrence, assessing the impact of histopathological heterogeneity on its diagnostic specificity and sensitivity.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThis study was conducted using a prospective database of primary and metastatic brain tumors that underwent surgery at our unit between January 2018 and April 2024, comprising a total of 1.260 patients. From this cohort, we selected all patients who subsequently received adjuvant conventional fractionated radiotherapy or radiosurgery, amounting to 750 patients. Within this group, we identified those who exhibited disease progression where the differentiation between RN and local recurrence was unclear on conventional MRI follow-up (presence of new or enlarging contrast-enhancing lesion suggestive of tumor recurrence or radiation necrosis), as determined by a multidisciplinary meeting. As part of our institutional protocol, these patients underwent multimodal MRI, including perfusion, diffusion and spectroscopy sequences, and were reassessed in a second multidisciplinary meeting to determine further management. The final study population consisted of all patients who underwent surgical resection based on the decision of the multidisciplinary meeting. A definitive diagnosis was established through histological examination of the entire surgical specimen, allowing for the distinction between pure tumor recurrence, pure RN, and mixed pathology, where both entities coexisted.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion criteria\u003c/h3\u003e\n\u003cp\u003e(1) Prior history of brain metastases treated with conventional fractionated radiotherapy or radiosurgery, (2) presence of new or enlarging contrast-enhancing lesions on conventional MRI suggestive of tumor recurrence or RN, (3) availability of multimodal MRI including perfusion, diffusion and spectroscopy sequences and (4) availability of histopathological confirmation. Patients with inadequate imaging quality or incomplete histopathological data were excluded.\u003c/p\u003e\n\u003ch3\u003eImaging Protocol\u003c/h3\u003e\n\u003cp\u003eAll patients were scanned using a 1.5T MRI scanner (Philips Achieva, Best, the Netherlands). The protocol included both standard and advanced sequences to provide a comprehensive evaluation. Routine anatomical sequences were: 3D-T1-weighted pre- and post-contrast imaging (TR 6,7 ms, TE 3 ms, matrix 232x229, FOV 256x256 mm, slice thickness 1,1 mm, NEX 1); axial T2-weighted imaging (TR 5296 ms, TE 110 ms, ETL 21, matrix 400x260, FOV 260x211 mm, slice thickness 5mm, slice spacing 1 mm, NEX 1,8), 3D fluid-attenuated inversion recovery (FLAIR, TR 4800 ms, TE 271 ms, TI 1660 ms, matrix192x192, FOV 240x240 mm, slice thickness 1,25 mm, NEX 2). The multimodal magnetic resonance imaging (MRI) workflow incorporated advanced imaging sequences, including diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI), and magnetic resonance spectroscopy (MRS). DWI was performed with the following parameters: TR 3845 ms, TE 88 ms, matrix 152x106 mm, FOV 230x230 mm, slice thickness 5 mm, slice spacing 1 mm, b value 1000 s/mm\u0026sup2;, NEX 1. SWI was performed with the following parameters: TR 52 ms, TE 12 ms, delta TE 11 ms, matrix 272x220, FOV 230x186 mm, slice thickness 1mm, slice spacing \u0026minus;\u0026thinsp;1 mm, NEX 1. DSC-PWI was performed using a gradient-echo echo-planar imaging (GRE-EPI) sequence with the following parameters: TR 1802 ms, TE 40 ms, FA 75, slice thickness 5 mm, FOV 224x224, total scan duration 1,10 minutes, NEX 1. A 15 mL bolus of gadolinium-based contrast agent (Pixxoscan, 1 mmol/kg, GE Healthcare) was administered intravenously at an injection rate of 3\u0026ndash;5 mL/s, adjusted according to the patient\u0026rsquo;s venous access. Point-Resolved Spectroscopy (PRESS) Magnetic Resonance Spectroscopy (MRS) was conducted in either single-voxel or multi-voxel modes, determined by tumor size and localization. The long TE parameter set comprised: TR 2000 ms, TE 144 ms, and NSA of 1 (multi-voxel) or 128 (single-voxel). The short TE parameter set was: TR 2000 ms, TE 40 ms, and NSA of 1 (multi-voxel) or 128 (single-voxel). FOV and voxel dimensions were adjusted based on the anatomical region of interest.\u003c/p\u003e\n\u003ch3\u003eDiffusion, Perfusion and Spectroscopy MRI Analysis\u003c/h3\u003e\n\u003cp\u003ePost-processing was performed using Philips IntelliSpace Portal v7.0 (Best, the Netherlands). Diffusion-weighted imaging (DWI) was analyzed using corresponding apparent diffusion coefficient (ADC) maps. Regions of interest (ROIs) were placed on ADC maps within the contrast-enhancing lesion and areas suspected of tumor recurrence, and ADC values were recorded. Tumor recurrence was expected to demonstrate lower ADC values due to increased cellularity, while radiation necrosis was expected to exhibit higher ADC values, reflecting necrotic tissue and decreased cellularity. A definitive ADC threshold for distinguishing between RN and recurrence was not established, given its potential variability based on tumor type, treatment protocols, and imaging parameters [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, the interpretation of diffusion-derived changes following radiotherapy necessitates careful consideration, as similar diffusion metrics can reflect both tumor recurrence (diffusion restriction due to hypercellularity) and RN (diffusion restriction due to post-radiotherapy coagulative necrosis) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe proportion of hypointense signal within lesions on susceptibility-weighted imaging (SWI) was quantified by comparison with the pre-radiotherapy SWI signal. Hypointensity on SWI is attributed to the presence of hemosiderin, an iron-containing blood breakdown product, frequently observed in radionecrotic tissue. However, hemorrhage, which also results in hypointensity, can occur in recurrent tumors due to increased vascularity. Furthermore, the diagnostic utility of SWI in differentiating RN from recurrence demonstrates variability across tumor types, with reported higher robustness in high-grade gliomas compared to metastatic lesions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor perfusion analysis, relative cerebral blood volume (rCBV) maps were generated. Regions of interest (ROIs) were manually placed over the enhancing lesion and contralateral normal appearing white matter to calculate normalized rCBV (nRCBV). An nRCBV value exceeding 1.5\u0026ndash;2 relative to the contralateral white matter ROI was considered indicative of tumor recurrence.\u003c/p\u003e \u003cp\u003eMagnetic resonance spectroscopy (MRS) was performed utilizing single-voxel or multi-voxel acquisition techniques. Metabolic ratios, particularly focusing on choline (Cho), creatine (Cr), and N-acetylaspartate (NAA), were analyzed. Tumor recurrence was characterized by elevated Cho/NAA and Cho/Cr ratios, indicative of increased cellular proliferation. Conversely, RN exhibited a decreased Cho/NAA ratio with elevated lipid and lactate peaks, reflecting necrotic tissue and oxidative stress. The integrated analysis of relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC), and MRS parameters were employed to enhance diagnostic accuracy (Multiparametric MRI) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In cases of discordant MRI findings, a tiered diagnostic strategy was implemented. When one modality suggested radiation necrosis while two favored tumor recurrence, further evaluation, including clinical assessment, longitudinal imaging follow-up, and metabolic imaging (FDG-PET) when clinically warranted, was conducted. Surgical intervention was pursued if the lesion demonstrated progression or metabolic activity, whereas stable or regressing lesions were managed conservatively. Similarly, when two modalities indicated RN while one suggested tumor recurrence, a conservative management approach was initially adopted, with close (bimonthly) radiological and clinical monitoring. If the lesion remained stable or regressed, RN was confirmed. However, lesion progression prompted further evaluation through metabolic imaging (FDG-PET) or histopathological confirmation. This stepwise diagnostic algorithm facilitated refined diagnostic accuracy, minimizing unnecessary interventions while ensuring the timely identification of true tumor recurrences.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHistopathological Analysis\u003c/h2\u003e \u003cp\u003eAll patients underwent surgical resection within 1 to 2 weeks of MRI evaluation. The surgical specimen was fixed overnight in 10% neutral-buffered formalin, sampled and embedded in paraffin blocks, sectioned at 4-\u0026micro;m thickness and stained with haematoxylin-eosin (H\u0026amp;E). Histopathological analysis was performed by a board-certified neuropathologist blinded to MRI report, who classified the lesions as tumor recurrence or RN based on cellularity, vascular status, and necrotic type (in particular, radiation necrosis comprises coagulative and fibrinoid necrosis, gliosis, wall thickening and hyalinization of vessels, telangiectasia and calcium deposition, situated mainly in the white matter).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe diagnostic accuracy of DSC perfusion and DWI MRI, and MRS were assessed using receiver operating characteristic (ROC) curve analysis. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using histopathology as the reference standard. The optimal rCBV, ADC, and Cho/NAA cut-off values for differentiating tumor recurrence from radiation necrosis were determined using the Youden index. These cut-off values were derived from the study cohort by generating ROC curves and selecting the thresholds that maximized the sum of sensitivity and specificity. Additionally, these values were compared to previously validated thresholds from the literature to assess consistency. Interobserver variability in rCBV, ADC, and MRS measurements were evaluated using the intraclass correlation coefficient (ICC). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Data were analyzed using [Statistical Software, Version].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe included 54 patients in the study, (22 males and 32 females), with a median age of 57 years. All patients had previously undergone a gross total resection of their tumor. Histopathological examination identified primary tumors in 48 patients, accounting for 89% of the cohort. Among them, 38 were gliomas, including 27 grade IV, 2 grade III, 8 grade II, and 1 grade I. Seven patients had meningiomas, of which 6 were classified as atypical WHO grade II and 1 as papillary WHO grade III. Additionally, 1 patient had a solitary fibrous tumor (grade III), 1 had a medulloblastoma, and 1 had a vestibular schwannoma. In the remaining 6 patients, representing 11% of the study population, histopathological analysis confirmed metastatic disease, with 4 cases originating from breast cancer, 1 from ovarian cancer, and 1 from lung cancer.\u003c/p\u003e \u003cp\u003eA conventional MRI showing the presence of new or enlarging contrast-enhancing lesions suggestive of tumor recurrence or RN was performed in 24 patients (44%) due to the onset of symptoms. Specifically, 10 patients exhibited new or worsening neurological deficits, 6 patients experienced acute symptoms related to increased intracranial pressure, and 8 patients presented with seizures. The remaining 30 patients underwent MRI as part of routine follow-up.\u003c/p\u003e \u003cp\u003eAll 54 patients included in the study underwent a new MRI scan incorporating DSC perfusion, DWI, and MRS sequences in addition to standard sequences, including T1 with contrast, T2, and FLAIR. The interpretation of multimodal MRI suggested tumor recurrence in 40 patients (70%) and radiation necrosis in 12 patients (22%), with complete concordance among the three modalities in all these cases. In 2 cases (4% of the cohort), multimodal MRI results were discordant, with DSC perfusion MRI and MRS indicating tumor recurrence, while DWI suggested RN. These two cases underwent additional FDG-PET imaging, which also supported tumor recurrence due to high metabolic activity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA multidisciplinary team evaluated each case and determined the surgical indication for both diagnostic and therapeutic purposes. Among the 12 patients with MRI findings suggestive of RN, surgical intervention was recommended in 10 cases due to severe symptoms persisting despite corticosteroid treatment, while in 2 cases, surgery was indicated due to rapid radiological progression. In all cases where multimodal MRI or metabolic imaging suggested tumor recurrence, the multidisciplinary team advised surgical intervention.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst Surgery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHistological Diagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjuvant Therapy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDisease Progression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJune 2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeptember 2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;SRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovember 2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApril 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovember 2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarch 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOctober 2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;SRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovember 2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMay 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJanuary 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAugust 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eApril 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJuly 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u0026thinsp;+\u0026thinsp;CT\u0026thinsp;+\u0026thinsp;cyberknife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeptember 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApril 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovember 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecember 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJanuary 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFebruary 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJanuary 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAugust 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJune 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVestibular schwannoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCyberknife\u0026thinsp;+\u0026thinsp;SRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eApril 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJuly 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLung cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMay 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMay 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJune 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAugust 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade III Glioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJune 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOctober 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade III Hemangiopericytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJuly 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOctober 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAugust 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAugust 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNovember 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade III Papillary meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecember 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApril 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarch 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJune 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eApril 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNovember 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMay 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJuly 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMay 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMay 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2016 \u0026ndash; March 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJuly 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMay 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJuly 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1981- June 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade I Pilocytic astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAugust 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFebruary 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOctober 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarch 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOctober 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade III Glioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecember 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFebruary 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecember 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJuly 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecember 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeptember 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecember 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFebruary 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecember 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApril 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJanuary 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNovember 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNovember 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV medulloblastoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarch 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010-2013-2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecember 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNovember 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMay 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJanuary 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMay 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Oligodendroglioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewhole brain RT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAugust 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFebruary 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovember 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovember 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarch 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary 2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOctober 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary 2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNovember 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Oligodendroglioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTUPP protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarch 2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeptember 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOvarian cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarch 2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFebruary 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Ependimoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eApril 2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCT, Chemotherapy; STR, Stereotactic Radiation Therapy; GBM, Glioblastoma multiforme\u003c/p\u003e \u003cp\u003eThe radiological diagnosis was then correlated with the histological findings from the second-look analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the 40 patients for whom all multimodal MRI modalities suggested tumor recurrence, histological examination confirmed recurrence in 27 patients (67%), while 7 patients (18%) had recurrence associated with radiation necrosis, and 6 patients (15%) had pure radiation necrosis without evidence of tumor progression. This resulted in a 15% false-positive rate, indicating that while multimodal MRI demonstrated good sensitivity for detecting recurrence, it had limitations in distinguishing pure recurrence from radiation necrosis.\u003c/p\u003e \u003cp\u003eConversely, among the 12 patients for whom all multimodal MRI modalities suggested radiation necrosis, histopathological analysis confirmed pure radiation necrosis in only 1 patient (8%). The remaining cases revealed pure tumor recurrence in 8 patients (67%) and tumor recurrence associated with radiation necrosis in 3 patients (25%), highlighting the low sensitivity of multimodal MRI for detecting radiation necrosis. Additionally, in both cases where multimodal MRI was inconclusive and FDG-PET favored tumor recurrence, histopathological examination confirmed the presence of recurrence associated with radiation necrosis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between second look histological diagnosis and radiological features\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistological Diagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecond Look\u003c/p\u003e \u003cp\u003eHistological Diagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRadiological Features\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Astrocytoma\u0026thinsp;+\u0026thinsp;RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRN without recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u0026thinsp;+\u0026thinsp;RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u0026thinsp;+\u0026thinsp;RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVestibular schwannoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVestibular schwannoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade II Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLung cancer metastasis\u0026thinsp;+\u0026thinsp;RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRN without recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade III Glioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade III Glioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade III Hemangiopericytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRN without recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRN without recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade III Papillary meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade III Papillary meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u0026thinsp;+\u0026thinsp;RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edubious\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRN without recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade I Pilocytic astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade I Pilocytic astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u0026thinsp;+\u0026thinsp;RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade III Glioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u0026thinsp;+\u0026thinsp;RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRN without recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast cancer metastasis\u0026thinsp;+\u0026thinsp;RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u0026thinsp;+\u0026thinsp;RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV medulloblastoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV medulloblastoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtypical Meningioma\u0026thinsp;+\u0026thinsp;RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtypical Meningioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade II Oligodendroglioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Oligodendroglioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u0026thinsp;+\u0026thinsp;RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edubious\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u0026thinsp;+\u0026thinsp;RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade IV GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade II Oligodendroglioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Oligodendroglioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOvarian cancer metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRN without recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade II Ependimoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade II Ependimoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eGBM, Glioblastoma multiforme; RN, radiation necrosis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing the collected data, we generated a ROC curve to evaluate the diagnostic performance of multimodal MRI in distinguishing tumor recurrence from radiation necrosis. The Area Under the Curve (AUC) was 0.45, indicating that multimodal MRI performed worse than chance in differentiating the two conditions. This poor diagnostic performance suggests a high rate of both false positives and false negatives, limiting its reliability in clinical decision-making. The study cohort-derived cut-off values were compared with previously validated thresholds in the literature, revealing no significant differences, thus eliminating the need for external validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study highlights the significant limitations of multimodal MRI in differentiating tumor recurrence from radiation necrosis. While multimodal MRI demonstrated good sensitivity for detecting tumor recurrence, its false-positive rate of 15% indicates that a subset of patients diagnosed with recurrence had pure radiation necrosis, leading to potential overtreatment. Conversely, the low sensitivity (8%) for detecting radiation necrosis suggests that some patients classified as having RN based on imaging alone may in fact harbor recurrent tumor tissue, reinforcing the risk of undertreatment. These findings are consistent with prior studies that have reported similar challenges in distinguishing these two conditions using advanced imaging techniques [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Previous literature suggests that while DSC perfusion, DWI, and MRS can provide valuable adjunctive information, their specificity remains limited due to overlapping imaging characteristics between recurrent tumor and radiation necrosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For instance, some studies have shown that while elevated rCBV values on perfusion MRI are commonly associated with tumor recurrence, radiation necrosis can also exhibit increased perfusion due to inflammatory neovascularization [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, ADC values on DWI, though useful, have substantial overlap between these two conditions, making them unreliable as standalone diagnostic tools [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, our findings emphasize the importance of histopathological confirmation in cases of diagnostic uncertainty. Biopsy alone is often unreliable, as it may sample an area of radiation necrosis rather than viable tumor tissue, leading to misdiagnosis. This limitation is particularly relevant in our study, where a substantial proportion of cases revealed a mixed pathology of radiation necrosis coexisting with tumor recurrence. The AUC of 0.45 observed in our study further underscores the poor performance of multimodal MRI, reinforcing the necessity of histological evaluation for definitive diagnosis.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eDespite the valuable insights provided by this study, several limitations should be acknowledged. First, the sample size of 54 patients, though sufficient for preliminary analysis, remains relatively small for a study assessing diagnostic accuracy. A larger, multicenter cohort would be beneficial to improve the generalizability of our findings. Second, all imaging analyses were conducted using a 1.5T MRI scanner, which may have lower spatial resolution and sensitivity compared to 3T MRI systems commonly used in advanced neuroimaging studies. Third, while histopathological analysis following surgical resection was considered the gold standard, there remains an inherent risk of sampling bias even with extensive tissue examination. Additionally, the study focused exclusively on patients who underwent surgical resection, potentially introducing selection bias, as patients with stable or regressing lesions managed conservatively were not included. Finally, while we implemented a standardized protocol for MRI acquisition and interpretation, interobserver variability in image analysis was not explicitly assessed, which may influence the reproducibility of our findings.\u003c/p\u003e \u003cp\u003eGiven these limitations, future studies should focus on larger, multicenter cohorts, incorporate higher-resolution MRI protocols, and explore the role of emerging diagnostic techniques, such as radiomics and machine learning-based texture analysis, to enhance diagnostic precision. Furthermore, a comparative evaluation of metabolic imaging techniques, such as FET-PET or FDG-PET, in combination with multimodal MRI, could provide additional insights into refining the differentiation between tumor recurrence and radiation necrosis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eMultimodal MRI has significant limitations in distinguishing recurrence from RN. Surgical resection remains the most reliable diagnostic method and should be integrated into a multidisciplinary approach for managing post-radiotherapy lesions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eConceptualization, M. Maggio; Methodology, M. Maggio and D.S. Zimatore and G. Ingravallo; Vali\u0026shy;dation, F. Signorelli; Formal Analysis, F. Signorelli; Investigation, M. Maggio; Resources, M. Maggio; Data Curation, M. Maggio; Writing\u0026mdash;Original Draft Preparation, M. Maggio; Writ\u0026shy;ing\u0026mdash;Review and Editing, F. Signorelli and R. Messina; Vi\u0026shy;sualization, L. de Gennaro; Supervision, M.T. Bozzi; Project Administration, R. Messina. All authors have read and approved the submit\u0026shy;ted version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003eThis study was approved by the local Ethic Committee. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Informed consent was obtained from all individual participants and/or from minor patients\u0026rsquo; parents included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKim TH, Yun TJ, Park CK, Kim TM, Kim JH, Sohn CH, Won JK, Park SH, Kim IH, Choi SH (2017) Combined use of susceptibility weighted magnetic resonance imaging sequences and dynamic susceptibility contrast perfusion weighted imaging to improve the accuracy of the differential diagnosis of recurrence and radionecrosis in high-grade glioma patients. 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Neurosurg Clin N Am 31(4):575\u0026ndash;587. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.nec.2020.06.007\u003c/span\u003e\u003cspan address=\"10.1016/j.nec.2020.06.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang B, Zhao B, Zhang Y, Ge M, Zhao P, Na S, Li C, Pang Q, Xu S, Liu Y Absolute CBV for the differentiation of recurrence and radionecrosis of brain metastases after gamma knife radiotherapy: a comparison with relative CBV. Clin Radiol 73(8):758.e1-758.e7. doi: 10.1016/j.crad.2018.04.006\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Brain Neoplasms/diagnostic imaging, Radiation Injuries/diagnosis, Magnetic Resonance, Imaging/methods, Neurosurgical Procedures/methods, Biopsy/methods, Treatment Outcome","lastPublishedDoi":"10.21203/rs.3.rs-6527725/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6527725/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eDifferentiating tumor recurrence from radiation necrosis (RN) after radiotherapy in brain tumors remains a diagnostic challenge. Multimodal MRI (mMRI), combining diffusion-weighted imaging (DWI), perfusion-weighted imaging, and MR spectroscopy (MRS), is commonly used to improve diagnostic accuracy, though its reliability is still debated. This study evaluates the diagnostic performance of mMRI and the role of surgical resection in establishing a definitive diagnosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively and prospectively included patients with brain tumors who developed new or enlarging contrast-enhancing lesions on follow-up MRI after stereotactic radiosurgery (SRS) or whole-brain radiotherapy (WBRT). All patients underwent mMRI (DWI, perfusion, and MRS) and were assessed by a multidisciplinary team. Surgical resection was performed based on clinical and radiological findings, and histopathology provided the definitive diagnosis. Diagnostic performance metrics and ROC analysis were calculated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFifty-four patients were included. mMRI suggested recurrence in 40 cases (74%), RN in 12 (22%), and was inconclusive in 2 (4%). Histology confirmed pure tumor recurrence in 27 (50%), mixed recurrence and RN in 9 (17%), and pure RN in 6 (11%). Among cases with mMRI-suggested RN, only 1 (8%) was confirmed as pure RN. Both inconclusive cases had FDG-PET-confirmed recurrence with mixed pathology. mMRI showed a 15% false-positive rate for recurrence and a 92% false-negative rate for RN. The area under the ROC curve was 0.45.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003emMRI has significant limitations in distinguishing recurrence from RN. Surgical resection remains the most reliable diagnostic method and should be integrated into a multidisciplinary approach for managing post-radiotherapy lesions.\u003c/p\u003e","manuscriptTitle":"Distinguishing Brain Tumor Recurrence from Radiation Necrosis: Diagnostic Limitations of Multimodal MRI and the Role of Surgical Resection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 03:34:14","doi":"10.21203/rs.3.rs-6527725/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"120b5390-7616-496f-b523-ce49c417a301","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-07T10:53:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 03:34:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6527725","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6527725","identity":"rs-6527725","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}