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1.
Artigo em Inglês | MEDLINE | ID: mdl-32572562

RESUMO

PURPOSE: In vivo measurement of the spatial distribution of neurofibrillary tangle pathology is critical for early diagnosis and disease monitoring of Alzheimer's disease (AD). METHODS: Forty-nine participants were scanned with 18F-PI-2620 PET to examine the distribution of this novel PET ligand throughout the course of AD: 36 older healthy controls (HC) (age range 61 to 86), 11 beta-amyloid+ (Aß+) participants with cognitive impairment (CI; clinical diagnosis of either mild cognitive impairment or AD dementia, age range 57 to 86), and 2 participants with semantic variant primary progressive aphasia (svPPA, age 66 and 78). Group differences in brain regions relevant in AD (medial temporal lobe, posterior cingulate cortex, and lateral parietal cortex) were examined using standardized uptake value ratios (SUVRs) normalized to the inferior gray matter of the cerebellum. RESULTS: SUVRs in target regions were relatively stable 60 to 90 min post-injection, with the exception of very high binders who continued to show increases over time. Robust elevations in 18F-PI-2620 were observed between HC and Aß+ CI across all AD regions. Within the HC group, older age was associated with subtle elevations in target regions. Mildly elevated focal uptake was observed in the anterior temporal pole in one svPPA patient. CONCLUSION: Preliminary results suggest strong differences in the medial temporal lobe and cortical regions known to be impacted in AD using 18F-PI-2620 in patients along the AD trajectory. This work confirms that 18F-PI-2620 holds promise as a tool to visualize tau aggregations in AD.

2.
Artigo em Inglês | MEDLINE | ID: mdl-32556481

RESUMO

PURPOSE: To evaluate the performance of deep learning (DL) classifiers in discriminating normal and abnormal 18F-FACBC (fluciclovine, Axumin®) PET scans based on the presence of tumor recurrence and/or metastases in patients with prostate cancer (PC) and biochemical recurrence (BCR). METHODS: A total of 251 consecutive 18F-fluciclovine PET scans were acquired between September 2017 and June 2019 in 233 PC patients with BCR (18 patients had 2 scans). PET images were labeled as normal or abnormal using clinical reports as the ground truth. Convolutional neural network (CNN) models were trained using two different architectures, a 2D-CNN (ResNet-50) using single slices (slice-based approach) and the same 2D-CNN and a 3D-CNN (ResNet-14) using a hundred slices per PET image (case-based approach). Models' performances were evaluated on independent test datasets. RESULTS: For the 2D-CNN slice-based approach, 6800 and 536 slices were used for training and test datasets, respectively. The sensitivity and specificity of this model were 90.7% and 95.1%, and the area under the curve (AUC) of receiver operating characteristic curve was 0.971 (p < 0.001). For the case-based approaches using both 2D-CNN and 3D-CNN architectures, a training dataset of 100 images and a test dataset of 28 images were randomly allocated. The sensitivity, specificity, and AUC to discriminate abnormal images by the 2D-CNN and 3D-CNN case-based approaches were 85.7%, 71.4%, and 0.750 (p = 0.013) and 71.4%, 71.4%, and 0.699 (p = 0.053), respectively. CONCLUSION: DL accurately classifies abnormal 18F-fluciclovine PET images of the pelvis in patients with BCR of PC. A DL classifier using single slice prediction had superior performance over case-based prediction.

3.
J Digit Imaging ; 33(2): 447-455, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-31659587

RESUMO

The high-background glucose metabolism of normal gray matter on [18F]-fluoro-2-D-deoxyglucose (FDG) positron emission tomography (PET) of the brain results in a low signal-to-background ratio, potentially increasing the possibility of missing important findings in patients with intracranial malignancies. To explore the strategy of using a deep learning classifier to aid in distinguishing normal versus abnormal findings on PET brain images, this study evaluated the performance of a two-dimensional convolutional neural network (2D-CNN) to classify FDG PET brain scans as normal (N) or abnormal (A). METHODS: Two hundred eighty-nine brain FDG-PET scans (N; n = 150, A; n = 139) resulting in a total of 68,260 images were included. Nine individual 2D-CNN models with three different window settings for axial, coronal, and sagittal axes were trained and validated. The performance of these individual and ensemble models was evaluated and compared using a test dataset. Odds ratio, Akaike's information criterion (AIC), and area under curve (AUC) on receiver-operative-characteristic curve, accuracy, and standard deviation (SD) were calculated. RESULTS: An optimal window setting to classify normal and abnormal scans was different for each axis of the individual models. An ensembled model using different axes with an optimized window setting (window-triad) showed better performance than ensembled models using the same axis and different windows settings (axis-triad). Increase in odds ratio and decrease in SD were observed in both axis-triad and window-triad models compared with individual models, whereas improvements of AUC and AIC were seen in window-triad models. An overall model averaging the probabilities of all individual models showed the best accuracy of 82.0%. CONCLUSIONS: Data ensemble using different window settings and axes was effective to improve 2D-CNN performance parameters for the classification of brain FDG-PET scans. If prospectively validated with a larger cohort of patients, similar models could provide decision support in a clinical setting.

4.
J Nucl Med ; 61(4): 546-551, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-31628216

RESUMO

18F-DCFPyL (2-(3-{1-carboxy-5-[(6-18F-fluoropyridine-3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid) is a promising PET radiopharmaceutical targeting prostate-specific membrane antigen (PSMA). We present our experience with this single-academic-center prospective study evaluating the positivity rate of 18F-DCFPyL PET/CT in patients with biochemical recurrence (BCR) of prostate cancer (PC). Methods: We prospectively enrolled 72 men (52-91 y old; mean ± SD, 71.5 ± 7.2) with BCR after primary definitive treatment with prostatectomy (n = 42) or radiotherapy (n = 30). The presence of lesions compatible with PC was evaluated by 2 independent readers. Fifty-nine patients had scans concurrent with at least one other conventional scan: bone scanning (24), CT (21), MR (20), 18F-fluciclovine PET/CT (18), or 18F-NaF PET (14). Findings from 18F-DCFPyL PET/CT were compared with those from other modalities. Impact on patient management based on 18F-DCFPyL PET/CT was recorded from clinical chart review. Results: 18F-DCFPyL PET/CT had an overall positivity rate of 85%, which increased with higher prostate-specific antigen (PSA) levels (ng/mL): 50% (PSA < 0.5), 69% (0.5 ≤ PSA < 1), 100% (1 ≤ PSA < 2), 91% (2 ≤ PSA < 5), and 96% (PSA ≥ 5). 18F-DCFPyL PET detected more lesions than conventional imaging. For anatomic imaging, 20 of 41 (49%) CT or MRI scans had findings congruent with 18F-DCFPyL, whereas 18F-DCFPyL PET was positive in 17 of 41 (41%) cases with negative CT or MRI findings. For bone imaging, 26 of 38 (68%) bone or 18F-NaF PET scans were congruent with 18F-DCFPyL PET, whereas 18F-DCFPyL PET localized bone lesions in 8 of 38 (21%) patients with negative results on bone or 18F-NaF PET scans. In 8 of 18 (44%) patients, 18F-fluciclovine PET had located the same lesions as did 18F-DCFPyL PET, whereas 5 of 18 (28%) patients with negative 18F-fluciclovine findings had positive 18F-DCFPyL PET findings and 1 of 18 (6%) patients with negative 18F-DCFPyL findings had uptake in the prostate bed on 18F-fluciclovine PET. In the remaining 4 of 18 (22%) patients, 18F-DCFPyL and 18F-fluciclovine scans showed different lesions. Lastly, 43 of 72 (60%) patients had treatment changes after 18F-DCFPyL PET and, most noticeably, 17 of these patients (24% total) had lesion localization only on 18F-DCFPyL PET, despite negative results on conventional imaging. Conclusion: 18F-DCFPyL PET/CT is a promising diagnostic tool in the work-up of biochemically recurrent PC, given the high positivity rate as compared with Food and Drug Administration-approved currently available imaging modalities and its impact on clinical management in 60% of patients.

5.
Radiology ; 293(2): 451-459, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31526257

RESUMO

Background Primary tumor maximum standardized uptake value is a prognostic marker for non-small cell lung cancer. In the setting of malignancy, bone marrow activity from fluorine 18-fluorodeoxyglucose (FDG) PET may be informative for clinical risk stratification. Purpose To determine whether integrating FDG PET radiomic features of the primary tumor, tumor penumbra, and bone marrow identifies lung cancer disease-free survival more accurately than clinical features alone. Materials and Methods Patients were retrospectively analyzed from two distinct cohorts collected between 2008 and 2016. Each tumor, its surrounding penumbra, and bone marrow from the L3-L5 vertebral bodies was contoured on pretreatment FDG PET/CT images. There were 156 bone marrow and 512 tumor and penumbra radiomic features computed from the PET series. Randomized sparse Cox regression by least absolute shrinkage and selection operator identified features that predicted disease-free survival in the training cohort. Cox proportional hazards models were built and locked in the training cohort, then evaluated in an independent cohort for temporal validation. Results There were 227 patients analyzed; 136 for training (mean age, 69 years ± 9 [standard deviation]; 101 men) and 91 for temporal validation (mean age, 72 years ± 10; 91 men). The top clinical model included stage; adding tumor region features alone improved outcome prediction (log likelihood, -158 vs -152; P = .007). Adding bone marrow features continued to improve performance (log likelihood, -158 vs -145; P = .001). The top model integrated stage, two bone marrow texture features, one tumor with penumbra texture feature, and two penumbra texture features (concordance, 0.78; 95% confidence interval: 0.70, 0.85; P < .001). This fully integrated model was a predictor of poor outcome in the independent cohort (concordance, 0.72; 95% confidence interval: 0.64, 0.80; P < .001) and a binary score stratified patients into high and low risk of poor outcome (P < .001). Conclusion A model that includes pretreatment fluorine 18-fluorodeoxyglucose PET texture features from the primary tumor, tumor penumbra, and bone marrow predicts disease-free survival of patients with non-small cell lung cancer more accurately than clinical features alone. © RSNA, 2019 Online supplemental material is available for this article.


Assuntos
Medula Óssea/diagnóstico por imagem , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Neoplasias Pulmonares/diagnóstico por imagem , Tomografia Computadorizada com Tomografia por Emissão de Pósitrons , Idoso , Medula Óssea/patologia , Carcinoma Pulmonar de Células não Pequenas/patologia , Feminino , Fluordesoxiglucose F18 , Humanos , Neoplasias Pulmonares/patologia , Masculino , Valor Preditivo dos Testes , Prognóstico , Compostos Radiofarmacêuticos , Estudos Retrospectivos , Medição de Risco
6.
Tomography ; 5(1): 145-153, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30854452

RESUMO

We identified computational imaging features on 18F-fluorodeoxyglucose positron emission tomography (PET) that predict recurrence/progression in non-small cell lung cancer (NSCLC). We retrospectively identified 291 patients with NSCLC from 2 prospectively acquired cohorts (training, n = 145; validation, n = 146). We contoured the metabolic tumor volume (MTV) on all pretreatment PET images and added a 3-dimensional penumbra region that extended outward 1 cm from the tumor surface. We generated 512 radiomics features, selected 435 features based on robustness to contour variations, and then applied randomized sparse regression (LASSO) to identify features that predicted time to recurrence in the training cohort. We built Cox proportional hazards models in the training cohort and independently evaluated the models in the validation cohort. Two features including stage and a MTV plus penumbra texture feature were selected by LASSO. Both features were significant univariate predictors, with stage being the best predictor (hazard ratio [HR] = 2.15 [95% confidence interval (CI): 1.56-2.95], P < .001). However, adding the MTV plus penumbra texture feature to stage significantly improved prediction (P = .006). This multivariate model was a significant predictor of time to recurrence in the training cohort (concordance = 0.74 [95% CI: 0.66-0.81], P < .001) that was validated in a separate validation cohort (concordance = 0.74 [95% CI: 0.67-0.81], P < .001). A combined radiomics and clinical model improved NSCLC recurrence prediction. FDG PET radiomic features may be useful biomarkers for lung cancer prognosis and add clinical utility for risk stratification.


Assuntos
Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Neoplasias Pulmonares/diagnóstico por imagem , Recidiva Local de Neoplasia/diagnóstico por imagem , Adulto , Idoso , Idoso de 80 Anos ou mais , Carcinoma Pulmonar de Células não Pequenas/patologia , Feminino , Fluordesoxiglucose F18 , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Estimativa de Kaplan-Meier , Neoplasias Pulmonares/patologia , Masculino , Pessoa de Meia-Idade , Recidiva Local de Neoplasia/patologia , Estadiamento de Neoplasias , Variações Dependentes do Observador , Tomografia Computadorizada com Tomografia por Emissão de Pósitrons/métodos , Valor Preditivo dos Testes , Prognóstico , Modelos de Riscos Proporcionais , Compostos Radiofarmacêuticos , Reprodutibilidade dos Testes , Estudos Retrospectivos , Medição de Risco/métodos
8.
Clin Nucl Med ; 43(1): 1-8, 2018 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-29076913

RESUMO

OBJECTIVE: To evaluate the predictive value of interim PET (iPET) in diffuse large B-cell lymphoma (DLBCL) using 5 different imaging interpretation criteria: Deauville 5-point scale criteria, International Harmonization Project (IHP) criteria, Response Evaluation Criteria In Solid Tumors (RECIST) 1.1, European Organization for Research and Treatment of Cancer, and PET Response Criteria in Solid Tumors (PERCIST) 1.0. METHODS: We retrospectively reviewed records from 38 patients with DLBCL who underwent baseline and iPET at our institution. Imaging was interpreted according to the previously mentioned criteria. Results were correlated with end-of-treatment response, based on reports at the end of treatment radiological examinations, overall survival (OS), and progression-free survival (PFS) to assess and compare the predictive value of iPET according to each criterion. We also evaluated the concordance between different criteria. RESULTS: The Deauville and PERCIST criteria were the most reliable for predicting end-of-treatment response, reporting an accuracy of 81.6%. They also correlated with OS and PFS (P = 0.0004 and P = 0.0001, and P = 0.0007 and P = 0.0002, for Deauville and PERCIST, respectively). Interim PET according to European Organization for Research and Treatment of Cancer also predicted the end-of-treatment response with an accuracy of 73.7% and had a significant correlation with OS (P = 0.007) and PFS (P = 0.007). In contrast, the IHP criteria and RECIST did not predict outcomes: the accuracy for end-of-treatment response was 34.2% and 36.8%, respectively, with no significant correlation with OS or PFS (P = 0.182 and P = 0.357, and P = 0.341 and P = 0.215, for OS and PFS, respectively). CONCLUSIONS: The predictive value of iPET in DLBCL patients is most reliable using the Deauville and PERCIST criteria. Criteria that rely on anatomical characteristics, namely, RECIST and IHP criteria, are less accurate in predicting patient outcomes in DLBCL.


Assuntos
Interpretação de Imagem Assistida por Computador , Linfoma Difuso de Grandes Células B/diagnóstico por imagem , Tomografia por Emissão de Pósitrons , Tomografia Computadorizada por Raios X , Adulto , Idoso , Idoso de 80 Anos ou mais , Intervalo Livre de Doença , Feminino , Fluordesoxiglucose F18 , Humanos , Linfoma Difuso de Grandes Células B/terapia , Masculino , Pessoa de Meia-Idade , Critérios de Avaliação de Resposta em Tumores Sólidos , Estudos Retrospectivos , Adulto Jovem
9.
PET Clin ; 7(4): 369-80, 2012 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-27157644

RESUMO

Diagnostic imaging plays an important role in the staging, restaging, and treatment monitoring in head and neck cancer (HNC). MR imaging and computed tomography (CT) are the primary imaging modalities for the assessment of this type of tumor; however, they have been proved to be ineffective in some cases. (18)F-2-fluoro-2-deoxy-D-glucose (FDG) PET/CT and more recently PET/MR imaging are increasingly becoming a standard part of the management of HNC. The purpose of this article is to discuss the indications and benefits of (18)F-FDG PET/CT and PET/MR imaging in the management of patients with HNC.

10.
J Healthc Eng ; 2(1): 97-110, 2011 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-22844575

RESUMO

In exploring an approach to decision support based on information extracted from a clinical database, we developed mortality prediction models of intensive care unit (ICU) patients who had acute kidney injury (AKI) and compared them against the Simplified Acute Physiology Score (SAPS). We used MIMIC, a public de-identified database of ICU patients admitted to Beth Israel Deaconess Medical Center, and identified 1400 patients with an ICD9 diagnosis of AKI and who had an ICU stay > 3 days. Multivariate regression models were built using the SAPS variables from the first 72 hours of ICU admission. All the models developed on the training set performed better than SAPS (AUC = 0.64, Hosmer-Lemeshow p < 0.001) on an unseen test set; the best model had an AUC = 0.74 and Hosmer-Lemeshow p = 0.53. These findings suggest that local customized modeling might provide more accurate predictions. This could be the first step towards an envisioned individualized point-of-care probabilistic modeling using one's clinical database.

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