scholarly journals Diagnostic and Prognostic Potential of 18F-FET PET in the Differential Diagnosis of Glioma Recurrence and Treatment-Induced Changes After Chemoradiation Therapy

2021 ◽  
Vol 11 ◽  
Author(s):  
Monica Celli ◽  
Paola Caroli ◽  
Elena Amadori ◽  
Donatella Arpa ◽  
Lorena Gurrieri ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Prognostic Factors ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Chemoradiation Therapy ◽  
Time Activity ◽  
Fet Pet ◽  
Cutoff Values ◽  
Glioma Recurrence ◽  
Induced Changes

BackgroundMRI-based differential diagnosis of glioma recurrence (GR) and treatment-induced changes (TICs) remain elusive in up to 30% of treated glioma patients. We aimed to determine 18F-FET PET diagnostic performance in this clinical scenario, its outcome dependency on established prognostic factors, optimal 18F-FET semi-quantitative thresholds, and whether 18F-FET parameters may instantly predict progression-free survival (PFS) and overall survival (OS).MethodsWe retrospectively analyzed 45 glioma patients treated with chemoradiation therapy (32 males; mean age: 51 years, glioma grade: n=26 WHO4; n=15 WHO3; n=4 WHO2) who underwent 18F-FET PET to resolve differential diagnosis of GR and TICs raised by MRI performed in the preceding 2 weeks and depicting any of the following changes in their radiation field: volumetric increase of contrast-enhancing lesions; new contrast-enhancing lesion; significant increase in T2/FLAIR non-enhancing lesion without reducing corticosteroids. 18F-FET PET outcome relied on evaluation of maximum tumor-to-brain ratio (TBRmax), time-to-peak (TTP), and time-activity curve pattern (TAC). Metabolic tumor volume (MTV) and total tumor metabolism (TTM) were calculated for prognostic purposes. Standard of reference was repeat MRI performed 4–6 weeks after the previous MRI. Non-parametric statistics tested 18F-FET-based parameters for dependency on established prognostic markers. ROC curve analysis determined optimal cutoff values for 18F-FET semi-quantitative parameters. 18F-FET parameters and prognostic factors were evaluated for PFS and OS by Kaplan-Meier, univariate, and multivariate analyses.Results18F-FET PET sensitivity, specificity, positive predictive value, negative predictive value were 86.2, 81.3, 89.3, 76.5%, respectively; higher diagnostic accuracy was yielded in IDH-wild-type glioma patients compared to IDH-mutant glioma patients (sensitivity: 81.8 versus 88.9%; specificity: 80.8 versus 81.8%). KPS was the only prognostic factor differing according to 18F-FET PET outcome (negative versus positive). Optimal 18F-FET cutoff values for GR were TBRmax ≥ 2.1, SUVmax ≥ 3.5, and TTP ≤ 29 min. PFS differed based on 18F-FET outcome and related metrics and according to KPS; a different OS was observed according to KPS only. On multivariate analysis, 18F-FET PET outcome was the only significant PFS factor; KPS and age the only significant OS factors.Conclusion18F-FET PET demonstrated good diagnostic performance. 18F-FET PET outcome and metrics were significantly predictive only for PFS.

scholarly journals FET PET Radiomics for Differentiating Pseudoprogression from Early Tumor Progression in Glioma Patients Post-Chemoradiation

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3835
Author(s):  
Philipp Lohmann ◽  
Mai A. Elahmadawy ◽  
Robin Gutsche ◽  
Jan-Michael Werner ◽  
Elena K. Bauer ◽  
...  
Keyword(s):  
Tumor Progression ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Data Augmentation ◽  
Response Assessment ◽  
Lower Sensitivity ◽  
Fet Pet ◽  
Early Tumor ◽  
Dynamic Fet Pet

Currently, a reliable diagnostic test for differentiating pseudoprogression from early tumor progression is lacking. We explored the potential of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) positron emission tomography (PET) radiomics for this clinically important task. Thirty-four patients (isocitrate dehydrogenase (IDH)-wildtype glioblastoma, 94%) with progressive magnetic resonance imaging (MRI) changes according to the Response Assessment in Neuro-Oncology (RANO) criteria within the first 12 weeks after completing temozolomide chemoradiation underwent a dynamic FET PET scan. Static and dynamic FET PET parameters were calculated. For radiomics analysis, the number of datasets was increased to 102 using data augmentation. After randomly assigning patients to a training and test dataset, 944 features were calculated on unfiltered and filtered images. The number of features for model generation was limited to four to avoid data overfitting. Eighteen patients were diagnosed with early tumor progression, and 16 patients had pseudoprogression. The FET PET radiomics model correctly diagnosed pseudoprogression in all test cohort patients (sensitivity, 100%; negative predictive value, 100%). In contrast, the diagnostic performance of the best FET PET parameter (TBRmax) was lower (sensitivity, 81%; negative predictive value, 80%). The results suggest that FET PET radiomics helps diagnose patients with pseudoprogression with a high diagnostic performance. Given the clinical significance, further studies are warranted.

scholarly journals Diagnostic Performance of Endoscopic Ultrasound-Artificial Intelligence Using Deep Learning Analysis of Gallbladder Polypoid Lesions: A Feasibility Study

2021 ◽  
Author(s):  
Sung Ill Jang ◽  
Young Jae Kim ◽  
Eui Joo Kim ◽  
Huapyong Kang ◽  
Seung Jin Shon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Endoscopic Ultrasound ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Validation Cohort ◽  
External Validation ◽  
Diagnostic Modality ◽  
Internal Validation

Abstract Endoscopic ultrasound (EUS) is the most accurate diagnostic modality for polypoid lesions of the gallbladder (GB), but is limited by subjective interpretation. We evaluated the diagnostic performance of deep learning-based artificial intelligence (AI) in differentiating polypoid lesions using EUS images. The diagnostic performance of the EUS-AI system with ResNet50 architecture was evaluated via three processes: training, internal validation, and testing. The diagnostic performance was also verified using an external validation cohort and compared with the performance of EUS endoscopists. In the AI development cohort, the diagnostic performance of EUS-AI including sensitivity, specificity, positive predictive value, negative predictive value and accuracy. For the differential diagnosis of neoplastic and non-neoplastic GB polyps, these values for EUS-AI were 77.8%, 91.6%, 57.9%, 96.5%, and 89.8%, respectively. In the external validation cohort, the differential diagnosis of neoplastic and non-neoplastic GB polyps, these values were 60.3%, 77.4%, 36.2%, 90.2%, and 74.4%, respectively, for EUS-AI; they were 74.2%, 44.9%, 75.4%, 46.2%, and 65.3%, respectively, for the endoscopists. The accuracy of the EUS-AI was between the accuracies of mid-level (66.7%) and expert EUS endoscopists (77.5%). This EUS-AI system showed favorable performance for the diagnosis of neoplastic GB polyps, with a performance comparable to that of EUS endoscopists.

Intraoperative magnetic resonance spectroscopy for identification of residual tumor during low-grade glioma surgery

2013 ◽  
Vol 118 (6) ◽  
pp. 1191-1198 ◽  
Author(s):  
M. Necmettin Pamir ◽  
Koray Özduman ◽  
Erdem Yıldız ◽  
Aydın Sav ◽  
Alp Dinçer
Keyword(s):  
Differential Diagnosis ◽  
Predictive Value ◽  
Residual Tumor ◽  
Low Grade Glioma ◽  
Pathological Examination ◽  
Histopathological Diagnosis ◽  
Low Grade ◽  
Resection Cavity ◽  
Induced Changes ◽  
Surgically Induced

Object The authors had previously shown that 3-T intraoperative MRI (ioMRI) detects residual tumor tissue during low-grade glioma and that it helps to increase the extent of resection. In a proportion of their cases, however, the ioMRI disclosed T2-hyperintense areas at the tumor resection border after the initial resection attempt and prompted a differential diagnosis between residual tumor and nontumoral changes. To guide this differential diagnosis the authors used intraoperative long-TE single-voxel proton MR spectroscopy (ioMRS) and tested the correlation of these findings with findings from pathological examination of resected tissue. Methods Patients who were undergoing surgery for hemispheric or insular WHO Grade II gliomas and were found to have T2 changes around the resection cavity at the initial ioMRI were prospectively examined with ioMRS and biopsies were taken from corresponding localizations. In 14 consecutive patients, the ioMRS diagnosis in 20 voxels of interest was tested against the histopathological diagnosis. Intraoperative diffusion-weighted imaging (ioDWI) was also performed, as a part of the routine imaging, to rule out surgically induced changes, which could also appear as T2 hyperintensity. Results Presence of tumor was documented in 14 (70%) of the 20 T2-hyperintense areas by histopathological examination. The sensitivity of ioMRS for identifying residual tumor was 85.7%, the specificity was 100%, the positive predictive value was 100%, and the negative predictive value was 75%. The specificity of ioDWI for surgically induced changes was high (100%), but the sensitivity was only 60%. Conclusions This is the first clinical series to indicate that ioMRS can be used to differentiate residual tumor from nontumoral changes around the resection cavity, with high sensitivity and specificity.

scholarly journals Coagulation and Fibrinolysis Biomarkers as Potential Indicators for the Diagnosis and Classification of Ovarian Hyperstimulation Syndrome

2021 ◽  
Vol 8 ◽  
Author(s):  
Shuai Li ◽  
Yaqi Qian ◽  
Yue Pei ◽  
Kaiqi Wu ◽  
Shiming Lu
Keyword(s):  
Differential Diagnosis ◽  
Predictive Value ◽  
Ovarian Hyperstimulation Syndrome ◽  
Control Group ◽  
Ovarian Hyperstimulation ◽  
Inhibitor Complex ◽  
Cutoff Values ◽  
Diagnosis And Classification ◽  
Severe Ohss

Background: Accurate diagnosis and classification of ovarian hyperstimulation syndrome (OHSS) is important for its management. We employed a new high-sensitivity chemiluminescence immunoassay to detect the thrombin-antithrombin complex (TAT), plasmin alpha2-plasmin inhibitor complex (PIC), soluble thrombomodulin (sTM), and tissue plasminogen activator-inhibitor complex (TPAI-C), and evaluated their diagnostic and classification performance for OHSS.Methods: A total of 106 women were enrolled, including 51 patients with OHSS (25 mild or moderate OHSS, 26 severe OHSS), and 55 without OHSS (control group). TAT, PIC, sTM, and TPAI-C levels were measured using the Sysmex HISCL5000 automated analyzer.Results: Compared to the control group, TAT, PIC, and TPAI-C levels were significantly higher (P < 0.001, P < 0.001, P < 0.001, respectively), whereas the sTM level was significantly lower (P < 0.001) in the patients with OHSS. The receiver operating characteristic was used to evaluate the diagnostic efficiency. For the diagnosis of OHSS, the area under the curves (AUCs) for TAT, PIC, sTM, and TPAI-C were 0.991, 0.973, 0.809, and 0.722, respectively. In particular, the sensitivity, specificity, positive predictive value, and negative predictive value for TAT and PIC were all above 90%. For the differential diagnosis of mild–moderate and severe OHSS, the AUCs for TAT, PIC, and TPAI-C were 0.736, 0.735, and 0.818, respectively. The cutoff values of TAT, PIC, and TPAI-C for the differential diagnosis of mild–moderate and severe OHSS were 11.5 ng/mL, 2.4 μg/mL, and 5.8 ng/mL, respectively. Based on these cutoff values, eight cases of mild–moderate OHSS exceeded the cutoff values, two of which developed to severe OHSS in the following days. However, of the remaining 17 cases of mild–moderate OHSS patients with negative biomarkers, none subsequently developed severe OHSS.Conclusions: TAT, PIC, sTM, and TPAI-C can be used as sensitive biomarkers in the diagnosis of OHSS. Meanwhile, TAT, PIC, and TPAI-C also displayed remarkable potential in the classification of OHSS. In addition, the levels of TAT, PIC, and TPAI-C above the cutoff values in patients with mild–moderate OHSS might predict a high risk of developing severe OHSS.

scholarly journals Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jung Su Lee ◽  
Jihye Yun ◽  
Sungwon Ham ◽  
Hyunjung Park ◽  
Hyunsu Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Previous History ◽  
Clinical Factor ◽  
Endoscopic Images ◽  
Simplex Virus

AbstractThe endoscopic features between herpes simplex virus (HSV) and cytomegalovirus (CMV) esophagitis overlap significantly, and hence the differential diagnosis between HSV and CMV esophagitis is sometimes difficult. Therefore, we developed a machine-learning-based classifier to discriminate between CMV and HSV esophagitis. We analyzed 87 patients with HSV esophagitis and 63 patients with CMV esophagitis and developed a machine-learning-based artificial intelligence (AI) system using a total of 666 endoscopic images with HSV esophagitis and 416 endoscopic images with CMV esophagitis. In the five repeated five-fold cross-validations based on the hue–saturation–brightness color model, logistic regression with a least absolute shrinkage and selection operation showed the best performance (sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve: 100%, 100%, 100%, 100%, 100%, and 1.0, respectively). Previous history of transplantation was included in classifiers as a clinical factor; the lower the performance of these classifiers, the greater the effect of including this clinical factor. Our machine-learning-based AI system for differential diagnosis between HSV and CMV esophagitis showed high accuracy, which could help clinicians with diagnoses.

scholarly journals Limited predictive value of illness perceptions for short-term poor recovery in musculoskeletal pain. A multi-center longitudinal study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
E. J. de Raaij ◽  
H. Wittink ◽  
J. F. Maissan ◽  
P. Westers ◽  
R. W. J. G. Ostelo
Keyword(s):  
Prognostic Factors ◽  
Pain Intensity ◽  
Musculoskeletal Pain ◽  
Physical Functioning ◽  
Predictive Value ◽  
Illness Perceptions ◽  
Illness Perception ◽  
Added Value ◽  
Poor Recovery ◽  
Significant Difference

Abstract Background Musculoskeletal pain (MSP) is recognized worldwide as a major cause of increased years lived with disability. In addition to known generic prognostic factors, illness perceptions (IPs) may have predictive value for poor recovery in MSP. We were interested in the added predictive value of baseline IPs, over and above the known generic prognostic factors, on clinical recovery from MSP. Also, it is hypothesized there may be overlap between IPs and domains covered by the Four-Dimensional Symptom Questionnaire (4DSQ), measuring distress, depression, anxiety and somatization. The aim of this study is twofold; 1) to assess the added predictive value of IPs for poor recovery and 2) to assess differences in predictive value for poor recovery between the Brief Illness Perception Questionnaire - Dutch Language Version (Brief IPQ-DLV) and the 4DSQ. Methods An eligible sample of 251 patients with musculoskeletal pain attending outpatient physical therapy were included in a multi-center longitudinal cohort study. Pain intensity, physical functioning and Global Perceived Effect were the primary outcomes. Hierarchical logistic regression models were used to assess the added value of baseline IPs for predicting poor recovery. To investigate the performance of the models, the levels of calibration (Hosmer-Lemeshov test) and discrimination (Area under the Curve (AUC)) were assessed. Results Baseline ‘Treatment Control’ added little predictive value for poor recovery in pain intensity [Odds Ratio (OR) 0.80 (Confidence Interval (CI) 0.66–0.97), increase in AUC 2%] and global perceived effect [OR 0.78 (CI 0.65–0.93), increase in AUC 3%]. Baseline ‘Timeline’ added little predictive value for poor recovery in physical functioning [OR 1.16 (CI 1.03–1.30), increase in AUC 2%]. There was a non-significant difference between AUCs in predictive value for poor recovery between the Brief IPQ-DLV and the 4DSQ. Conclusions Based on the findings of this explorative study, assessing baseline IPs, over and above the known generic prognostic factors, does not result in a substantial improvement in the prediction of poor recovery. Also, no recommendations can be given for preferring either the 4DSQ or the Brief IPQ-DLV to assess psychological factors.

scholarly journals Predictive Evaluation on Cytological Sample of Metastatic Melanoma: The Role of BRAF Immunocytochemistry in the Molecular Era

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1110
Author(s):  
Andrea Ronchi ◽  
Marco Montella ◽  
Federica Zito Marino ◽  
Michele Caraglia ◽  
Anna Grimaldi ◽  
...  
Keyword(s):  
Metastatic Melanoma ◽  
Molecular Analysis ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Cost Effective ◽  
Needle Aspiration ◽  
Braf V600e ◽  
Mutational Status ◽  
Needle Aspiration Cytology ◽  
Sensitivity Specificity

Background: Cutaneous malignant melanoma is an aggressive neoplasm. In advanced cases, the therapeutic choice depends on the mutational status of BRAF. Fine needle aspiration cytology (FNA) is often applied to the management of patients affected by melanoma, mainly for the diagnosis of metastases. The evaluation of BRAF mutational status by sequencing technique on cytological samples may be inconvenient, as it is a time and biomaterial-consuming technique. Recently, BRAF immunocytochemistry (ICC) was applied for the evaluation of BRAF V600E mutational status. Although it may be useful mainly in cytological samples, data about BRAF ICC on cytological samples are missing. Methods: We performed BRAF ICC on a series of 50 FNA samples of metastatic melanoma. BRAF molecular analysis was performed on the same cytological samples or on the corresponding histological samples. Molecular analysis was considered the gold standard. Results: BRAF ICC results were adequate in 49 out of 50 (98%) cases, positive in 15 out of 50 (30%) cases and negative in 34 out of 50 (68%) of cases. Overall, BRAF ICC sensitivity, specificity, positive predictive value and negative predictive value results were 88.2%, 100%, 100% and 94.1%, respectively. The diagnostic performance of BRAF ICC results was perfect when molecular evaluation was performed on the same cytological samples. Hyperpigmentation represents the main limitation of the technique. Conclusions: BRAF ICC is a rapid, cost-effective method for detecting BRAF V600E mutation in melanoma metastases, applicable with high diagnostic performance to cytological samples. It could represent the first step to evaluate BRAF mutational status in cytological samples, mainly in poorly cellular cases.

A Rapid Amniotic Fluid Interleukin-6 Assessment for the Identification of Intra-Amniotic Inflammation in Women with Preterm Labor and Intact Membranes

2021 ◽  
pp. 1-6
Author(s):  
Teresa Cobo ◽  
Victoria Aldecoa ◽  
Magdalena Holeckova ◽  
Ctirad Andrys ◽  
Xavier Filella ◽  
...  
Keyword(s):  
Amniotic Fluid ◽  
Predictive Model ◽  
Predictive Value ◽  
Preterm Labor ◽  
Diagnostic Performance ◽  
Clinical Signs ◽  
Entire Study ◽  
Microbial Invasion ◽  
Automated Method ◽  
Spontaneous Delivery

<b><i>Objectives:</i></b> A multivariable predictive model has recently been developed with good accuracy to predict spontaneous preterm delivery within 7 days in women with preterm labor (PTL) and intact membranes. However, this model measures amniotic fluid (AF) interleukin (IL)-6 concentrations using the ELISA method, thereby limiting clinical implementation. The main objectives of this study were to validate the automated immunoassay as a quantitative method to measure AF IL-6 in women with PTL and to evaluate the diagnostic performance of AF IL-6 alone and as part of a multivariable predictive model to predict spontaneous delivery in 7 days with this automated method. <b><i>Study Design:</i></b> This is a retrospective observational study in women with PTL below 34 weeks who underwent amniocentesis to rule out microbial invasion of the amniotic cavity. Women with clinical signs of chorioamnionitis, cervical length measurement at admission &#x3e;5th centile, maternal age &#x3c;18 years, and no consent to perform amniocentesis for this indication were excluded. The local Institutional Review Boards approved the study (HCB/2019/0940). <b><i>Analysis of AF IL-6 Concentrations:</i></b> AF IL-6 concentrations were measured using an automated Cobas e602 electrochemiluminescence immunoanalyzer and Human IL-6 Quantikine ELISA kit. <b><i>Results:</i></b> Of the entire study group (<i>n</i> = 100), 38 women spontaneously delivered within 7 days after admission. Both laboratory methods showed good agreement (intraclass correlation coefficient: 0.937 (95% confidence interval [CI] 0.908–0.957); <i>p</i> &#x3c; 0.001). Diagnostic performance of AF IL-6 to predict spontaneous delivery within 7 days when it was included in the multivariable predictive model showed an area under the receiver operating characteristic curve of 0.894 (95% CI 0.799–0.955), sensitivity of 97%, specificity of 74%, positive predictive value of 73%, negative predictive value of 97%, positive likelihood ratio (LR) of 3.7, and negative LR of 0.045. <b><i>Conclusion:</i></b> While both analytical methods were comparable for measuring AF IL-6 concentrations in women with PTL, the Cobas immunoanalyzer provided rapid diagnosis of intra-amniotic inflammation within minutes. The predictive model showed a good diagnostic performance to target women at high risk of spontaneous delivery within 7 days.

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