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OBJECTIVE: Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The objective of this study was to investigate the utility of radiomics and machine learning (ML) to differentiate RN from recurrence in patients with brain metastases treated with SRS. METHODS: Patients with brain metastases treated with SRS who developed either RN or tumor reccurence were retrospectively identified. Image preprocessing and radiomic feature extraction were performed using ANTsPy and PyRadiomics, yielding 105 features from MRI T1-weighted post-contrast (T1c), T2, and fluid-attenuated inversion recovery (FLAIR) images. Univariate analysis assessed significance of individual features. Multivariable analysis employed various classifiers on features identified as most discriminative through feature selection. ML models were evaluated through cross-validation, selecting the best model based on area under the receiver operating characteristic (ROC) curve (AUC). Specificity, sensitivity, and F1 score were computed. RESULTS: Sixty-six lesions from 55 patients were identified. On univariate analysis, 27 features from the T1c sequence were statistically significant, while no features were significant from the T2 or FLAIR sequences. For clinical variables, only immunotherapy use after SRS was significant. Multivariable analysis of features from the T1c sequence yielded an AUC of 76.2% (standard deviation [SD] ± 12.7%), with specificity and sensitivity of 75.5% (± 13.4%) and 62.3% (± 19.6%) in differentiating radionecrosis from recurrence. CONCLUSIONS: Radiomics with ML may assist the diagnostic ability of distinguishing RN from tumor recurrence after SRS. Further work is needed to validate this in a larger multi-institutional cohort and prospectively evaluate it's utility in patient care.
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Neoplasias Encefálicas , Aprendizado de Máquina , Imageamento por Ressonância Magnética , Necrose , Recidiva Local de Neoplasia , Lesões por Radiação , Humanos , Neoplasias Encefálicas/secundário , Neoplasias Encefálicas/radioterapia , Neoplasias Encefálicas/diagnóstico por imagem , Feminino , Masculino , Lesões por Radiação/diagnóstico por imagem , Lesões por Radiação/etiologia , Lesões por Radiação/patologia , Pessoa de Meia-Idade , Necrose/diagnóstico por imagem , Recidiva Local de Neoplasia/diagnóstico por imagem , Recidiva Local de Neoplasia/patologia , Estudos Retrospectivos , Imageamento por Ressonância Magnética/métodos , Idoso , Radiocirurgia , Adulto , Diagnóstico Diferencial , Idoso de 80 Anos ou mais , RadiômicaRESUMO
Background and Aims: Acute kidney injury (AKI) is a frequent complication of severe trauma associated with high mortality. The aim of this study was to evaluate the diagnostic ability of plasma and urine neutrophil gelatinase-associated lipocalin (NGAL) as an early marker of AKI assessed by RIFLE criteria as reference in trauma patients in intensive care unit (ICU). Material and Methods: This was a prospective observational study. Four hundred and eighteen patients admitted in the trauma ICU with age ≥18 years without known renal diseases were followed-up (serum creatinine, urine output, and estimated glomerular filtration rate) for 5 consecutive days. As per RIFLE criteria, 70 patients were broadly classified as AKI and rest of the patients (n = 348) as non-AKI. Plasma and urine samples of AKI (n = 70) and non-AKI (n = 70) patients were further assessed for 3 consecutive days following admission. Results: Mean plasma NGAL (pNGAL) was significantly elevated in AKI patients as compared with non-AKI patients; on admission: 204.08 versus 93.74 ng/mL (P = 0.01); at 24 h: 216.73 versus 94.63 ng/mL (P = 0.01); and 48 h: 212.77 versus 86.32 ng/mL (P = 0.01). Mean urine NGAL (uNGAL) at 48 h was also significantly elevated: 15.45 ng/mL in AKI patients as compared with 13.48 ng/mL in non-AKI patients (P = 0.01). Plasma and urine NGAL levels were significantly associated with increased mortality. Conclusion: pNGAL had good predictive value on admission (area under the receiver operative characteristic [AUROC] 0.84), at 24 h (AUROC 0.88) and 48 h (AUROC 0.87), while uNGAL had moderate performance at 24 h (AUROC 0.61) and 48 h (AUROC 0.71). pNGAL can be used as an early and potent diagnostic and predictive marker of AKI and mortality in critically ill trauma patients.
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BACKGROUND: Radiomic descriptors from magnetic resonance imaging (MRI) are promising for disease diagnosis and characterization but may be sensitive to differences in imaging parameters. OBJECTIVE: To evaluate the repeatability and robustness of radiomic descriptors within healthy brain tissue regions on prospectively acquired MRI scans; in a test-retest setting, under controlled systematic variations of MRI acquisition parameters, and after postprocessing. STUDY TYPE: Prospective. SUBJECTS: Fifteen healthy participants. FIELD STRENGTH/SEQUENCE: A 3.0 T, axial T2 -weighted 2D turbo spin-echo pulse sequence, 181 scans acquired (2 test/retest reference scans and 12 with systematic variations in contrast weighting, resolution, and acceleration per participant; removing scans with artifacts). ASSESSMENT: One hundred and forty-six radiomic descriptors were extracted from a contiguous 2D region of white matter in each scan, before and after postprocessing. STATISTICAL TESTS: Repeatability was assessed in a test/retest setting and between manual and automated annotations for the reference scan. Robustness was evaluated between the reference scan and each group of variant scans (contrast weighting, resolution, and acceleration). Both repeatability and robustness were quantified as the proportion of radiomic descriptors that fell into distinct ranges of the concordance correlation coefficient (CCC): excellent (CCC > 0.85), good (0.7 ≤ CCC ≤ 0.85), moderate (0.5 ≤ CCC < 0.7), and poor (CCC < 0.5); for unprocessed and postprocessed scans separately. RESULTS: Good to excellent repeatability was observed for 52% of radiomic descriptors between test/retest scans and 48% of descriptors between automated vs. manual annotations, respectively. Contrast weighting (TR/TE) changes were associated with the largest proportion of highly robust radiomic descriptors (21%, after processing). Image resolution changes resulted in the largest proportion of poorly robust radiomic descriptors (97%, before postprocessing). Postprocessing of images with only resolution/acceleration differences resulted in 73% of radiomic descriptors showing poor robustness. DATA CONCLUSIONS: Many radiomic descriptors appear to be nonrobust across variations in MR contrast weighting, resolution, and acceleration, as well in test-retest settings, depending on feature formulation and postprocessing. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.
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Encéfalo , Imageamento por Ressonância Magnética , Encéfalo/diagnóstico por imagem , Humanos , Estudos ProspectivosRESUMO
Purpose To evaluate ability of radiomic (computer-extracted imaging) features to distinguish non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT. Materials and Methods For this retrospective study, screening or standard diagnostic noncontrast CT images were collected for 290 patients (mean age, 68 years; range, 18-92 years; 125 men [mean age, 67 years; range, 18-90 years] and 165 women [mean age, 68 years; range, 33-92 years]) from two institutions between 2007 and 2013. Histopathologic analysis was available for one nodule per patient. Corresponding nodule of interest was identified on axial CT images by a radiologist with manual annotation. Nodule shape, wavelet (Gabor), and texture-based (Haralick and Laws energy) features were extracted from intra- and perinodular regions. Features were pruned to train machine learning classifiers with 145 patients. In a test set of 145 patients, classifier results were compared against a convolutional neural network (CNN) and diagnostic readings of two radiologists. Results Support vector machine classifier with intranodular radiomic features achieved an area under the receiver operating characteristic curve (AUC) of 0.75 on the test set. Combining radiomics of intranodular with perinodular regions improved the AUC to 0.80. On the same test set, CNN resulted in an AUC of 0.76. Radiologist readers achieved AUCs of 0.61 and 0.60, respectively. Conclusion Radiomic features from intranodular and perinodular regions of nodules can distinguish non-small cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.
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Adenocarcinoma/diagnóstico por imagem , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Granuloma/diagnóstico por imagem , Pneumopatias/diagnóstico por imagem , Neoplasias Pulmonares/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Nódulo Pulmonar Solitário/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Diagnóstico Diferencial , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Máquina de Vetores de SuporteRESUMO
BACKGROUND: In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). METHODS: A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+, HER2-) and triple-negative or HER2+ (TN/HER2+) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance. RESULTS: Among all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR+, HER2- group using DLDA and 0.93 ± 0.018 within the TN/HER2+ group using a naive Bayes classifier. In HR+, HER2- breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2+ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors. CONCLUSIONS: Through a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.
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Biomarcadores Tumorais/genética , Imageamento por Ressonância Magnética/métodos , Receptor ErbB-2/genética , Neoplasias de Mama Triplo Negativas/diagnóstico por imagem , Adulto , Protocolos de Quimioterapia Combinada Antineoplásica/administração & dosagem , Mama/diagnóstico por imagem , Mama/patologia , Meios de Contraste/administração & dosagem , Feminino , Humanos , Aprendizado de Máquina , Pessoa de Meia-Idade , Terapia Neoadjuvante , Neoplasias de Mama Triplo Negativas/tratamento farmacológico , Neoplasias de Mama Triplo Negativas/genética , Neoplasias de Mama Triplo Negativas/patologiaRESUMO
OBJECTIVE: Despite 90 % of glioblastoma (GBM) recurrences occurring in the peritumoral brain zone (PBZ), its contribution in patient survival is poorly understood. The current study leverages computerized texture (i.e. radiomic) analysis to evaluate the efficacy of PBZ features from pre-operative MRI in predicting long- (>18 months) versus short-term (<7 months) survival in GBM. METHODS: Sixty-five patient examinations (29 short-term, 36 long-term) with gadolinium-contrast T1w, FLAIR and T2w sequences from the Cancer Imaging Archive were employed. An expert manually segmented each study as: enhancing lesion, PBZ and tumour necrosis. 402 radiomic features (capturing co-occurrence, grey-level dependence and directional gradients) were obtained for each region. Evaluation was performed using threefold cross-validation, such that a subset of studies was used to select the most predictive features, and the remaining subset was used to evaluate their efficacy in predicting survival. RESULTS: A subset of ten radiomic 'peritumoral' MRI features, suggestive of intensity heterogeneity and textural patterns, was found to be predictive of survival (p = 1.47 × 10-5) as compared to features from enhancing tumour, necrotic regions and known clinical factors. CONCLUSION: Our preliminary analysis suggests that radiomic features from the PBZ on routine pre-operative MRI may be predictive of long- versus short-term survival in GBM. KEY POINTS: ⢠Radiomic features from peritumoral regions can capture glioblastoma heterogeneity to predict outcome. ⢠Peritumoral radiomics along with clinical factors are highly predictive of glioblastoma outcome. ⢠Identifying prognostic markers can assist in making personalized therapy decisions in glioblastoma.
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Neoplasias Encefálicas/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Glioblastoma/diagnóstico por imagem , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Neoplasias Encefálicas/mortalidade , Feminino , Glioblastoma/mortalidade , Humanos , Imageamento por Ressonância Magnética/métodos , Masculino , Pessoa de Meia-Idade , Recidiva Local de Neoplasia/epidemiologia , Prognóstico , Reprodutibilidade dos Testes , Medição de Risco , Taxa de Sobrevida , Adulto JovemRESUMO
BACKGROUND: With a wide array of multi-modal, multi-protocol, and multi-scale biomedical data being routinely acquired for disease characterization, there is a pressing need for quantitative tools to combine these varied channels of information. The goal of these integrated predictors is to combine these varied sources of information, while improving on the predictive ability of any individual modality. A number of application-specific data fusion methods have been previously proposed in the literature which have attempted to reconcile the differences in dimensionalities and length scales across different modalities. Our objective in this paper was to help identify metholodological choices that need to be made in order to build a data fusion technique, as it is not always clear which strategy is optimal for a particular problem. As a comprehensive review of all possible data fusion methods was outside the scope of this paper, we have focused on fusion approaches that employ dimensionality reduction (DR). METHODS: In this work, we quantitatively evaluate 4 non-overlapping existing instantiations of DR-based data fusion, within 3 different biomedical applications comprising over 100 studies. These instantiations utilized different knowledge representation and knowledge fusion methods, allowing us to examine the interplay of these modules in the context of data fusion. The use cases considered in this work involve the integration of (a) radiomics features from T2w MRI with peak area features from MR spectroscopy for identification of prostate cancer in vivo, (b) histomorphometric features (quantitative features extracted from histopathology) with protein mass spectrometry features for predicting 5 year biochemical recurrence in prostate cancer patients, and (c) volumetric measurements on T1w MRI with protein expression features to discriminate between patients with and without Alzheimers' Disease. RESULTS AND CONCLUSIONS: Our preliminary results in these specific use cases indicated that the use of kernel representations in conjunction with DR-based fusion may be most effective, as a weighted multi-kernel-based DR approach resulted in the highest area under the ROC curve of over 0.8. By contrast non-optimized DR-based representation and fusion methods yielded the worst predictive performance across all 3 applications. Our results suggest that when the individual modalities demonstrate relatively poor discriminability, many of the data fusion methods may not yield accurate, discriminatory representations either. In summary, to outperform the predictive ability of individual modalities, methodological choices for data fusion must explicitly account for the sparsity of and noise in the feature space.
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Doença de Alzheimer/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Espectroscopia de Ressonância Magnética/métodos , Neoplasias da Próstata/diagnóstico por imagem , Algoritmos , Diagnóstico por Computador/métodos , Humanos , Masculino , Reconhecimento Automatizado de Padrão/métodos , Sensibilidade e Especificidade , Análise de SobrevidaRESUMO
Medulloblastoma (MB) is the most frequent malignant brain tumor in children with extensive heterogeneity that results in varied clinical outcomes. Recently, MB was categorized into four molecular subgroups, WNT, SHH, Group 3, and Group 4. While SHH and Group 4 are known for their intermediate prognosis, studies have reported wide disparities in patient outcomes within these subgroups. This study aims to create a radiomic prognostic signature, medulloblastoma radiomics risk (mRRisk), to identify the risk levels within the SHH and Group 4 subgroups, individually, for reliable risk stratification. Our hypothesis is that this signature can comprehensively capture tumor characteristics that enable the accurate identification of the risk level. In total, 70 MB studies (48 Group 4, and 22 SHH) were retrospectively curated from three institutions. For each subgroup, 232 hand-crafted features that capture the entropy, surface changes, and contour characteristics of the tumor were extracted. Features were concatenated and fed into regression models for risk stratification. Contrasted with Chang stratification that did not yield any significant differences within subgroups, significant differences were observed between two risk groups in Group 4 (p = 0.04, Concordance Index (CI) = 0.82) on the cystic core and non-enhancing tumor, and SHH (p = 0.03, CI = 0.74) on the enhancing tumor. Our results indicate that radiomics may serve as a prognostic tool for refining MB risk stratification, towards improved patient care.
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Objective: Neighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity network (MSN) is a promising approach to elucidate cortical network patterns underlying complex cognitive functions. We hypothesized that MSNs could capture intricate changes in cortical patterns related to neighborhood disadvantage and cognitive function, potentially explaining some of the risk for later life cognitive impairment among individuals who live in disadvantaged contexts. Methods: This cross-sectional study included cognitively unimpaired participants (n=524, age=62.96±8.377, gender (M:F)=181:343, ADI(L:H) =450,74) from the Wisconsin Alzheimer's Disease Research Center or Wisconsin Registry for Alzheimer's Prevention. Neighborhood disadvantage status was obtained using the Area Deprivation Index (ADI). Cognitive performance was assessed through six tests evaluating memory, executive functioning, and the modified preclinical Alzheimer's cognitive composite (mPACC). Morphological Similarity Networks (MSN) were constructed for each participant based on the similarity in distribution of cortical thickness of brain regions, followed by computation of local and global network features. We used linear regression to examine ADI associations with cognitive scores and MSN features. The mediating effect of MSN features on the relationship between ADI and cognitive performance was statistically assessed. Results: Neighborhood disadvantage showed negative association with category fluency, implicit learning speed, story recall and mPACC scores, indicating worse cognitive function among those living in more disadvantaged neighborhoods. Local network features of frontal and temporal brain regions differed based on ADI status. Centrality of left lateral orbitofrontal region showed a partial mediating effect between association of neighborhood disadvantage and story recall performance. Conclusion: Our findings suggest differences in local cortical organization by neighborhood disadvantage, which also partially mediated the relationship between ADI and cognitive performance, providing a possible network-based mechanism to, in-part, explain the risk for poor cognitive functioning associated with disadvantaged neighborhoods. Future work will examine the exposure to neighborhood disadvantage on structural organization of the brain.
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Purpose: AI algorithms have shown impressive performance in segmenting geographic atrophy (GA) from fundus autofluorescence (FAF) images. However, selection of artificial intelligence (AI) architecture is an important variable in model development. Here, we explore 12 distinct AI architecture combinations to determine the most effective approach for GA segmentation. Methods: We investigated various AI architectures, each with distinct combinations of encoders and decoders. The architectures included three decoders-FPN (Feature Pyramid Network), UNet, and PSPNet (Pyramid Scene Parsing Network)-and serve as the foundation framework for segmentation task. Encoders including EfficientNet, ResNet (Residual Networks), VGG (Visual Geometry Group) and Mix Vision Transformer (mViT) have a role in extracting optimum latent features for accurate GA segmentation. Performance was measured through comparison of GA areas between human and AI predictions and Dice Coefficient (DC). Results: The training dataset included 601 FAF images from AREDS2 study and validation included 156 FAF images from the GlaxoSmithKline study. The mean absolute difference between grader measured and AI predicted areas ranged from -0.08 (95% CI = -1.35, 1.19) to 0.73 mm2 (95% CI = -5.75,4.29) and DC between 0.884-0.993. The best-performing models were UNet and FPN frameworks with mViT, and the least-performing models were PSPNet framework. Conclusions: The choice of AI architecture impacts GA segmentation performance. Vision transformers with FPN and UNet architectures demonstrate stronger suitability for this task compared to Convolutional Neural Network- and PSPNet-based models. Selecting an AI architecture must be tailored to the specific goals of the project, and developers should consider which architecture is ideal for their project.
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Aprendizado Profundo , Atrofia Geográfica , Degeneração Macular , Humanos , Atrofia Geográfica/diagnóstico , Degeneração Macular/diagnóstico , Degeneração Macular/fisiopatologia , Algoritmos , Angiofluoresceinografia/métodos , Redes Neurais de Computação , Idoso , Feminino , MasculinoRESUMO
High-grade glioma (HGG) is an aggressive brain tumor. Sex is an important factor that differentially affects survival outcomes in HGG. We used an end-to-end deep learning approach on hematoxylin and eosin (H&E) scans to (i) identify sex-specific histopathological attributes of the tumor microenvironment (TME), and (ii) create sex-specific risk profiles to prognosticate overall survival. Surgically resected H&E-stained tissue slides were analyzed in a two-stage approach using ResNet18 deep learning models, first, to segment the viable tumor regions and second, to build sex-specific prognostic models for prediction of overall survival. Our mResNet-Cox model yielded C-index (0.696, 0.736, 0.731, and 0.729) for the female cohort and C-index (0.729, 0.738, 0.724, and 0.696) for the male cohort across training and three independent validation cohorts, respectively. End-to-end deep learning approaches using routine H&E-stained slides, trained separately on male and female patients with HGG, may allow for identifying sex-specific histopathological attributes of the TME associated with survival and, ultimately, build patient-centric prognostic risk assessment models.
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Neoplasias Encefálicas , Aprendizado Profundo , Glioma , Caracteres Sexuais , Microambiente Tumoral , Humanos , Glioma/patologia , Glioma/mortalidade , Feminino , Masculino , Prognóstico , Neoplasias Encefálicas/patologia , Neoplasias Encefálicas/mortalidade , Pessoa de Meia-Idade , Gradação de Tumores , Adulto , IdosoRESUMO
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate nn-Unet-based segmentation models for automated delineation of medulloblastoma (MB) tumors on multi-institutional MRI scans. Materials and Methods This retrospective study included 78 pediatric patients (52 male, 26 female), with ages ranging from 2-18 years, with MB tumors from three different sites (28 from Hospital A, 18 from Hospital B, 32 from Hospital C), who had data from three clinical MRI protocols (gadolinium-enhanced T1-weighted, T2-weighted, FLAIR) available. The scans were retrospectively collected from the year 2000 until May 2019. Reference standard annotations of the tumor habitat, including enhancing tumor, edema, and cystic core + nonenhancing tumor subcompartments, were performed by two experienced neuroradiologists. Preprocessing included registration to age-appropriate atlases, skull stripping, bias correction, and intensity matching. The two models were trained as follows: (1) transfer learning nn-Unet model was pretrained on an adult glioma cohort (n = 484) and fine-tuned on MB studies using Models Genesis, and (2) direct deep learning nn-Unet model was trained directly on the MB datasets, across five-fold cross-validation. Model robustness was evaluated on the three datasets when using different combinations of training and test sets, with data from 2 sites at a time used for training and data from the third site used for testing. Results Analysis on the 3 test sites yielded Dice scores of 0.81, 0.86, 0.86 and 0.80, 0.86, 0.85 for tumor habitat; 0.68, 0.84, 0.77 and 0.67, 0.83, 0.76 for enhancing tumor; 0.56, 0.71, 0.69 and 0.56, 0.71, 0.70 for edema; and 0.32, 0.48, 0.43 and 0.29, 0.44, 0.41 for cystic core + nonenhancing tumor for the transfer learning-and direct-nn-Unet models, respectively. The models were largely robust to site-specific variations. Conclusion nn-Unet segmentation models hold promise for accurate, robust automated delineation of MB tumor subcompartments, potentially leading to more effective radiation therapy planning in pediatric MB. ©RSNA, 2024.
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PURPOSE: Isocitrate dehydrogenase-mutant (IDH-mt) gliomas are incurable primary brain tumors characterized by a slow-growing phase over several years followed by a rapid-growing malignant phase. We hypothesized that tumor volume growth rate (TVGR) on MRI may act as an earlier measure of clinical benefit during the active surveillance period. EXPERIMENTAL DESIGN: We integrated three-dimensional volumetric measurements with clinical, radiologic, and molecular data in a retrospective cohort of IDH-mt gliomas that were observed after surgical resection in order to understand tumor growth kinetics and the impact of molecular genetics. RESULTS: Using log-linear mixed modeling, the entire cohort (n = 128) had a continuous %TVGR per 6 months of 10.46% [95% confidence interval (CI), 9.11%-11.83%] and a doubling time of 3.5 years (95% CI, 3.10-3.98). High molecular grade IDH-mt gliomas, defined by the presence of homozygous deletion of CDKN2A/B, had %TVGR per 6 months of 19.17% (95% CI, 15.57%-22.89%) which was significantly different from low molecular grade IDH-mt gliomas with a growth rate per 6 months of 9.54% (95% CI, 7.32%-11.80%; P < 0.0001). Using joint modeling to comodel the longitudinal course of TVGR and overall survival, we found each one natural logarithm tumor volume increase resulted in more than a 3-fold increase in risk of death (HR = 3.83; 95% CI, 2.32-6.30; P < 0.0001). CONCLUSIONS: TVGR may be used as an earlier measure of clinical benefit and correlates well with the WHO 2021 molecular classification of gliomas and survival. Incorporation of TVGR as a surrogate endpoint into future prospective studies of IDH-mt gliomas may accelerate drug development.
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Neoplasias Encefálicas , Glioma , Humanos , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/patologia , Estudos Retrospectivos , Estudos Prospectivos , Carga Tumoral , Homozigoto , Conduta Expectante , Deleção de Sequência , Mutação , Glioma/diagnóstico por imagem , Glioma/genética , Glioma/metabolismo , Isocitrato Desidrogenase/genéticaRESUMO
Recent advances in artificial intelligence have greatly impacted the field of medical imaging and vastly improved the development of computational algorithms for data analysis. In the field of pediatric neuro-oncology, radiomics, the process of obtaining high-dimensional data from radiographic images, has been recently utilized in applications including survival prognostication, molecular classification, and tumor type classification. Similarly, radiogenomics, or the integration of radiomic and genomic data, has allowed for building comprehensive computational models to better understand disease etiology. While there exist excellent review articles on radiomics and radiogenomic pipelines and their applications in adult solid tumors, in this review article, we specifically review these computational approaches in the context of pediatric medulloblastoma tumors. Based on our systematic literature research via PubMed and Google Scholar, we provide a detailed summary of a total of 15 articles that have utilized radiomic and radiogenomic analysis for survival prognostication, tumor segmentation, and molecular subgroup classification in the context of pediatric medulloblastoma. Lastly, we shed light on the current challenges with the existing approaches as well as future directions and opportunities with using these computational radiomic and radiogenomic approaches for pediatric medulloblastoma tumors.
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Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.
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Neoplasias Encefálicas , Neoplasias Meníngeas , Meningioma , Adulto , Encéfalo/diagnóstico por imagem , Encéfalo/patologia , Neoplasias Encefálicas/diagnóstico por imagem , Criança , Humanos , Imageamento por Ressonância Magnética/métodos , Meningioma/diagnóstico por imagem , Meningioma/patologiaRESUMO
Localized disease heterogeneity on imaging extracted via radiomics approaches have recently been associated with disease prognosis and treatment response. Traditionally, radiomics analyses leverage texture operators to derive voxel- or region-wise feature values towards quantifying subtle variations in image appearance within a region-of-interest (ROI). With the goal of mining additional voxel-wise texture patterns from radiomic "expression maps", we introduce a new RADIomic Spatial TexturAl descripTor (RADISTAT). This was driven by the hypothesis that quantifying spatial organization of texture patterns within an ROI could allow for better capturing interactions between different tissue classes present in a given region; thus enabling more accurate characterization of disease or response phenotypes. RADISTAT involves: (a) robustly identifying sub-compartments of low, intermediate, and high radiomic expression (i.e. heterogeneity) in a feature map and (b) quantifying spatial organization of sub-compartments via graph interactions. RADISTAT was evaluated in two clinically challenging problems: (1) discriminating nodal/distant metastasis from metastasis-free rectal cancer patients on post-chemoradiation T2w MRI, and (2) distinguishing tumor progression from pseudo-progression in glioblastoma multiforme using post-chemoradiation T1w MRI. Across over 800 experiments, RADISTAT yielded a consistent discriminatory signature for tumor progression (GBM) and disease metastasis (RCa); where its sub-compartments were associated with pathologic tissue types (fibrosis or tumor, determined via fusion of MRI and pathology). In a multi-institutional setting for both clinical problems, RADISTAT resulted in higher classifier performance (11% improvement in AUC, on average) compared to radiomic descriptors. Furthermore, combining RADISTAT with radiomic descriptors resulted in significantly improved performance compared to using radiomic descriptors alone.
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Glioblastoma , Humanos , Imageamento por Ressonância Magnética/métodos , PrognósticoRESUMO
The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect. Specifically, we present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect. This involves non-rigidly aligning the patients' MRI scans to a healthy atlas via diffeomorphic registration. The resulting inverse mapping is used to obtain the deformation field magnitudes in the normal parenchyma. These measurements are then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (COLLAGE), which captures the morphological heterogeneity and infiltration within the tumor confines, on MRI scans. In this work, we extensively evaluated r-DepTH for survival risk-stratification on a total of 207 GBM cases from 3 different cohorts (Cohort 1 ( n1 = 53 ), Cohort 2 ( n2 = 75 ), and Cohort 3 ( n3 = 79 )), where each of these three cohorts was used as a training set for our model separately, and the other two cohorts were used for testing, independently, for each training experiment. When employing Cohort 1 for training, r-DepTH yielded Concordance indices (C-indices) of 0.7 and 0.65, hazard ratios (HR) and Confidence Intervals (CI) of 10 (6 - 19) and 5 (3 - 8) on Cohorts 2 and 3, respectively. Similarly, training on Cohort 2 yielded C-indices of 0.6 and 0.7, HR and CI of 1 (0.7 - 2) and 3 (2 - 5) on Cohorts 1 and 3, respectively. Finally, training on Cohort 3 yielded C-indices of 0.75 and 0.63, HR and CI of 24 (10 - 57) and 12 (6 - 21) on Cohorts 1 and 2, respectively. Our results show that r-DepTH descriptor may serve as a comprehensive and a robust MRI-based prognostic marker of disease aggressiveness and survival in solid tumors.
Assuntos
Neoplasias Encefálicas , Glioblastoma , Anisotropia , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/patologia , Estudos de Coortes , Glioblastoma/diagnóstico por imagem , Glioblastoma/patologia , Humanos , Imageamento por Ressonância Magnética/métodos , PrognósticoRESUMO
Introduction: Medulloblastoma (MB) is a malignant, heterogenous brain tumor. Advances in molecular profiling have led to identifying four molecular subgroups of MB (WNT, SHH, Group 3, Group 4), each with distinct clinical behaviors. We hypothesize that (1) aggressive MB tumors, growing heterogeneously, induce pronounced local structural deformations in the surrounding parenchyma, and (b) these local deformations as captured on Gadolinium (Gd)-enhanced-T1w MRI are independently associated with molecular subgroups, as well as overall survival in MB patients. Methods: In this work, a total of 88 MB studies from 2 institutions were analyzed. Following tumor delineation, Gd-T1w scan for every patient was registered to a normal age-specific T1w-MRI template via deformable registration. Following patient-atlas registration, local structural deformations in the brain parenchyma were obtained for every patient by computing statistics from deformation magnitudes obtained from every 5mm annular region, 0 < d < 60 mm, where d is the distance from the tumor infiltrating edge. Results: Multi-class comparison via ANOVA yielded significant differences between deformation magnitudes obtained for Group 3, Group 4, and SHH molecular subgroups, observed up to 60-mm outside the tumor edge. Additionally, Kaplan-Meier survival analysis showed that the local deformation statistics, combined with the current clinical risk-stratification approaches (molecular subgroup information and Chang's classification), could identify significant differences between high-risk and low-risk survival groups, achieving better performance results than using any of these approaches individually. Discussion: These preliminary findings suggest there exists significant association of our tumor-induced deformation descriptor with overall survival in MB, and that there could be an added value in using the proposed radiomic descriptor along with the current risk classification approaches, towards more reliable risk assessment in pediatric MB.