Survival time prediction in patients with high-grade serous ovarian cancer based on 18F-FDG PET/CT- derived inter-tumor heterogeneity metrics.

BACKGROUND: The presence of heterogeneity is a significant attribute within the context of ovarian cancer. This study aimed to assess the predictive accuracy of models utilizing quantitative 18F-FDG PET/CT derived inter-tumor heterogeneity metrics in determining progression-free survival (PFS) and overall survival (OS) in patients diagnosed with high-grade serous ovarian cancer (HGSOC). Additionally, the study investigated the potential correlation between model risk scores and the expression levels of p53 and Ki-67. METHODS: A total of 292 patients diagnosed with HGSOC were retrospectively enrolled at Shengjing Hospital of China Medical University (median age: 54 ± 9.4 years). Quantitative inter-tumor heterogeneity metrics were calculated based on conventional measurements and texture features of primary and metastatic lesions in 18F-FDG PET/CT. Conventional models, heterogeneity models, and integrated models were then constructed to predict PFS and OS. Spearman's correlation coefficient (ρ) was used to evaluate the correlation between immunohistochemical scores of p53 and Ki-67 and model risk scores. RESULTS: The C-indices of the integrated models were the highest for both PFS and OS models. The C-indices of the training set and testing set of the integrated PFS model were 0.898 (95% confidence interval [CI]: 0.881-0.914) and 0.891 (95% CI: 0.860-0.921), respectively. For the integrated OS model, the C-indices of the training set and testing set were 0.894 (95% CI: 0.871-0.917) and 0.905 (95% CI: 0.873-0.936), respectively. The integrated PFS model showed the strongest correlation with the expression levels of p53 (ρ = 0.859, p < 0.001) and Ki-67 (ρ = 0.829, p < 0.001). CONCLUSIONS: The models based on 18F-FDG PET/CT quantitative inter-tumor heterogeneity metrics exhibited good performance for predicting the PFS and OS of patients with HGSOC. p53 and Ki-67 expression levels were strongly correlated with the risk scores of the integrated predictive models.

Cortical surface analysis for focal cortical dysplasia diagnosis by using PET images.

Focal cortical dysplasia (FCD) is a neurological disorder distinguished by faulty brain cell structure and development. Repetitive and uncontrollable seizures may be linked to FCD's aberrant cortical thickness, gyrification, and sulcal depth. Quantitative cortical surface analysis is a crucial alternative to ineffective visual inspection. This study recruited 42 subjects including 22 FCD patients who underwent surgery and 20 healthy controls (HC). For the FCD patients, T1-weighted and PET images were obtained by a PET-MRI scanner, and the confirmed epileptogenic zone (EZ) was collected from postsurgical follow-up. For the HCs, CT and PET images were obtained by a PET-CT scanner. Cortical thickness, gyrification index, and sulcal depth were calculated using a computational anatomical toolbox (CAT12). A cluster-based analysis is carried out to determine each FCD patient's aberrant cortical surface. After parcellating the cerebral cortex into 68 regions by the Desikan-Killiany atlas, a region of interest (ROI) analysis was conducted to know whether the feature in the FCD group is significantly different from that in the HC group. Finally, the features of all ROIs were utilised to train a support vector machine classifier (SVM). The classification performance is evaluated by the leave-one-out cross-validation. The cluster-based analysis can localize the EZ cluster with the highest accuracy of 54.5 % (12/22) for cortical thickness, 40.9 % (9/22) and 13.6 % (3/22) for sulcal depth and gyrification, respectively. Moderate concordance (Kappa, 0.6) is observed between the confirmed EZs and identified clusters by using the cortical thickness. Fair concordance (Kappa, 0.3) and no concordance (Kappa, 0.1) is found by using sulcal depth and gyrification. Significant differences are found in 46 of 68 regions (67.7 %) for the three measures. The trained SVM classifier achieved a prediction accuracy of 95.5 % for the cortical thickness, while the sulcal depth and the gyrification obtained 86.0 % and 81.5 %. Cortical thickness, as determined by quantitative cortical surface analysis of PET data, has a greater ability than sulcal depth and gyrification to locate aberrant EZ clusters in FCD. Surface measures might be different in many regions for FCD and HC. By integrating machine learning and cortical morphologies features, individual prediction of FCD seems to be feasible.

A radiomics based method for prediction of prostate cancer Gleason score using enlarged region of interest.

BACKGROUND: Prostate cancer (PCa) is one of the most common cancers in men worldwide, and its timely diagnosis and treatment are becoming increasingly important. MRI is in increasing use to diagnose cancer and to distinguish between non-clinically significant and clinically significant PCa, leading to more precise diagnosis and treatment. The purpose of this study is to present a radiomics-based method for determining the Gleason score (GS) for PCa using tumour heterogeneity on multiparametric MRI (mp-MRI). METHODS: Twenty-six patients with biopsy-proven PCa were included in this study. The quantitative T2 values, apparent diffusion coefficient (ADC) and signal enhancement rates (α) were calculated using multi-echo T2 images, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), for the annotated region of interests (ROI). After texture feature analysis, ROI range expansion and feature filtering was performed. Then obtained data were put into support vector machine (SVM), K-Nearest Neighbor (KNN) and other classifiers for binary classification. RESULTS: The highest classification accuracy was 73.96% for distinguishing between clinically significant (Gleason 3 + 4 and above) and non-significant cancers (Gleason 3 + 3) and 83.72% for distinguishing between Gleason 3 + 4 from Gleason 4 + 3 and above, which was achieved using initial ROIs drawn by the radiologists. The accuracy improved when using expanded ROIs to 80.67% using SVM and 88.42% using Bayesian classification for distinguishing between clinically significant and non-significant cancers and Gleason 3 + 4 from Gleason 4 + 3 and above, respectively. CONCLUSIONS: Our results indicate the research significance and value of this study for determining the GS for prostate cancer using the expansion of the ROI region.

Epileptogenic Zone Localization in Refractory Epilepsy by FDG-PET: The Comparison of SPM and SPM-CAT With Different Parameter Settings.

Refractory epilepsy is a complex case of epileptic disease. The quantitative analysis of fluorodeoxyglucose positron emission tomography (FDG-PET) images complements visual assessment and helps localize the epileptogenic zone (EZ) for better curative treatment. Statistical parametric mapping (SPM) and its computational anatomy toolbox (SPM-CAT) are two commonly applied tools in neuroimaging analysis. This study compares SPM and SPM-CAT with different parameters to find the optimal approach for localizing EZ in refractory epilepsy. The current study enrolled 45 subjects, including 25 refractory epilepsy patients and 20 healthy controls. All of the 25 patients underwent surgical operations. Pathological results and the postoperative outcome evaluation by the Engel scale were likewise presented. SPM and SPM-CAT were used to assess FDG-PET images with three different uncorrected p-values and the corresponding cluster sizes (k), as in voxels in the cluster, namely p < 0.0002, k > 25; p < 0.001, k > 100; p < 0.005, and k > 200. When combining three settings, SPM and SPM-CAT yielded overall positive finding scores of 96.0% (24/25) and 100.0% (25/25) respectively. However, for the individual setting, SPM-CAT achieved the diverse positive finding scores of 96.0% (24/25), 96.0% (24/25), and 88.0% (22/24), which are higher than those of SPM [88.0% (22/25), 76.0% (19/25), and 72.0% (18/25)]. SPM and SPM-CAT localized EZ correctly with 28.0% (7/25) and 64.0% (16/25), respectively. SPM-CAT with parameter settings p < 0.0002 and k > 25 yielded a correct localization at 56.0% (14/25), which is slightly higher than that for the other two settings (48.0 and 20.0%). Moderate concordance was found between the confirmed and pre-surgical EZs, identified by SPM-CAT (kappa value = 0.5). Hence, SPM-CAT is more efficient than SPM in localizing EZ for refractory epilepsy by quantitative analysis of FDG-PET images. SPM-CAT with the setting of p < 0.0002 and k > 25 might perform as an objective complementary tool to the visual assessment for EZ localization.

Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI.

Accurately measuring arterial input function (AIF) is essential for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). We used the indicator dilution principle to evaluate the accuracy of AIF measured directly from an artery following a low-dose contrast media ultrafast DCE-MRI. In total, 15 patients with biopsy-confirmed localized prostate cancers were recruited. Cardiac MRI (CMRI) and ultrafast DCE-MRI were acquired on a Philips 3 T Ingenia scanner. The AIF was measured at iliac arties following injection of a low-dose (0.015 mmol/kg) gadolinium (Gd) contrast media. The cardiac output (CO) from CMRI (COCMRI) was calculated from the difference in ventricular volume at diastole and systole measured on the short axis of heart. The CO from DCE-MRI (CODCE) was also calculated from the AIF and dose of the contrast media used. A correlation test and Bland-Altman plot were used to compare COCMRI and CODCE. The average (±standard deviation [SD]) area under the curve measured directly from local AIF was 0.219 ± 0.07 mM·min. The average (±SD) COCMRI and CODCE were 6.52 ± 1.47 L/min and 6.88 ± 1.64 L/min, respectively. There was a strong positive correlation (r = 0.82, P < .01) and good agreement between COCMRI and CODCE. The CODCE is consistent with the reference standard COCMRI. This indicates that the AIF can be measured accurately from an artery with ultrafast DCE-MRI following injection of a low-dose contrast media.

A compact solution for estimation of physiological parameters from ultrafast prostate dynamic contrast enhanced MRI.

The Tofts pharmacokinetic model requires multiple calculations for analysis of dynamic contrast enhanced (DCE) MRI. In addition, the Tofts model may not be appropriate for the prostate. This can result in error propagation that reduces the accuracy of pharmacokinetic measurements. In this study, we present a compact solution allowing estimation of physiological parameters K trans and v e from ultrafast DCE acquisitions, without fitting DCE-MRI data to the standard Tofts pharmacokinetic model. Since the standard Tofts model can be simplified to the Patlak model at early times when contrast efflux from the extravascular extracellular space back to plasma is negligible, K trans can be solved explicitly for a specific time. Further, v e can be estimated directly from the late steady-state signal using the derivative form of Tofts model. Ultrafast DCE-MRI data were acquired from 18 prostate cancer patients on a Philips Achieva 3T-TX scanner. Regions-of-interest (ROIs) for prostate cancer, normal tissue, gluteal muscle, and iliac artery were manually traced. The contrast media concentration as function of time was calculated over each ROI using gradient echo signal equation with pre-contrast tissue T1 values, and using the 'reference tissue' model with a linear approximation. There was strong correlation (r = 0.88-0.91, p  < 0.0001) between K trans extracted from the Tofts model and K trans estimated from the compact solution for prostate cancer and normal tissue. Additionally, there was moderate correlation (r = 0.65-0.73, p  < 0.0001) between extracted versus estimated v e. Bland-Altman analysis showed moderate to good agreement between physiological parameters extracted from the Tofts model and those estimated from the compact solution with absolute bias less than 0.20 min-1 and 0.10 for K trans and v e, respectively. The compact solution may decrease systematic errors and error propagation, and could increase the efficiency of clinical workflow. The compact solution requires high temporal resolution DCE-MRI due to the need to adequately sample the early phase of contrast media uptake.

Diagnosis of Prostate Cancer by Use of MRI-Derived Quantitative Risk Maps: A Feasibility Study.

OBJECTIVE. The purpose of this study was to develop a new quantitative image analysis tool for estimating the risk of cancer of the prostate by use of quantitative multiparametric MRI (mpMRI) metrics. MATERIALS AND METHODS. Thirty patients with biopsy-confirmed prostate cancer (PCa) who underwent preoperative 3-T mpMRI were included in the study. Quantitative mpMRI metrics-apparent diffusion coefficient (ADC), T2, and dynamic contrast-enhanced (DCE) signal enhancement rate (α)-were calculated on a voxel-by-voxel basis for the whole prostate and coregistered. A normalized risk value (0-100) for each mpMRI parameter was obtained, with high risk values associated with low T2 and ADC and high signal enhancement rate. The final risk score was calculated as a weighted sum of the risk scores (ADC, 40%; T2, 40%; DCE, 20%). Data from five patients were used as training set to find the threshold for predicting PCa. In the other 25 patients, any region with a minimum of 30 con-joint voxels (≈ 4.8 mm2) with final risk score above the threshold was considered positive for cancer. Lesion-based and sector-based analyses were performed by matching prostatectomyverified malignancy and PCa predicted with the risk analysis tool. RESULTS. The risk map tool had sensitivity of 76.6%, 89.2%, and 100% for detecting all lesions, clinically significant lesions (≥ Gleason 3 + 4), and index lesions, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value for PCa detection for all lesions in the sector-based analysis were 78.9%, 88.5%, 84.4%, and 84.1%, respectively, with an ROC AUC of 0.84. CONCLUSION. The risk analysis tool is effective for detecting clinically significant PCa with reasonable sensitivity and specificity in both peripheral and transition zones.

Revisiting quantitative multi-parametric MRI of benign prostatic hyperplasia and its differentiation from transition zone cancer.

PURPOSE: This study investigates the multiparametric MRI (mpMRI) appearance of different types of benign prostatic hyperplasia (BPH) and whether quantitative mpMRI is effective in differentiating between prostate cancer (PCa) and BPH. MATERIALS AND METHODS: Patients (n = 60) with confirmed PCa underwent preoperative 3T MRI. T2-weighted, multi-echo T2-weighted, diffusion weighted and dynamic contrast enhanced images (DCE) were obtained prior to undergoing prostatectomy. PCa and BPH (cystic, glandular or stromal) were identified in the transition zone and matched with MRI. Quantitative mpMRI metrics: T2, ADC and DCE-MRI parameters using an empirical mathematical model were measured. RESULTS: ADC values were significantly lower (p < 0.001) in PCa compared to all BPH types and can differentiate between PCa and BPH with high accuracy (AUC = 0.87, p < 0.001). T2 values were significantly lower (p < 0.001) in PCa compared to cystic BPH only, while glandular (p = 0.27) and stromal BPH (p = 0.99) showed no significant difference from PCa. BPH mimics PCa in the transition zone on DCE-MRI evidenced by no significant difference between them. mpMRI values of glandular (ADC = 1.31 ± 0.22 µm2/ms, T2 = 115.7 ± 37.3 ms) and cystic BPH (ADC = 1.92 ± 0.43 µm2/ms, T2 = 242.8 ± 117.9 ms) are significantly different. There was no significant difference in ADC (p = 0.72) and T2 (p = 0.46) between glandular and stromal BPH. CONCLUSIONS: Multiparametric MRI and specifically quantitative ADC values can be used for differentiating PCa and BPH, improving PCa diagnosis in the transition zone. However, DCE-MRI metrics are not effective in distinguishing PCa and BPH. Glandular BPH are not hyperintense on ADC and T2 as previously thought and have similar quantitative mpMRI measurements to stromal BPH. Glandular and cystic BPH appear differently on mpMRI and are histologically different.

Feasibility of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using Low-Dose Gadolinium: Comparative Performance With Standard Dose in Prostate Cancer Diagnosis.

OBJECTIVES: This study investigates whether administration of low doses of gadolinium-based contrast agent (GBCA) for dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) can be as effective as a standard dose in distinguishing prostate cancer (PCa) from benign tissue. In addition, we evaluated the combination of kinetic parameters from the low- and high-dose injection as a new diagnostic marker. MATERIALS AND METHODS: Patients (n = 17) with histologically confirmed PCa underwent preoperative 3 T MRI. Dynamic contrast-enhanced MRI images were acquired at 8.3-second temporal resolution with a low dose (0.015 mmol/kg) and close to the standard dose (0.085 mmol/kg) of gadobentate dimeglumine bolus injections. Low-dose images were acquired for 3.5 minutes, followed by a 5-minute gap before acquiring standard dose images for 8.3 minutes. The data were analyzed qualitatively to investigate whether lesions could be detected based on early focal enhancement and quantitatively by fitting signal intensity as a function of time with an empirical mathematical model to obtain a maximum enhancement projection (MEP) and signal enhancement rate (α). RESULTS: Both low- and standard-dose DCE-MRI showed similar sensitivity (13/26 = 50%) and lesion conspicuity score (4.0 ± 1.0 vs 4.2 ± 0.9; P = 0.317) for PCa diagnosis on qualitative analysis. Prostate cancer showed significantly increased α compared with benign tissue for low (9.98 ± 5.84 vs 5.12 ± 2.95 s) but not for standard (4.27 ± 2.20 vs 3.35 ± 1.48 s) dose. The ratio of low-dose α to standard-dose α was significantly greater (P = 0.02) for PCa (2.8 ± 2.3) than for normal prostate (1.6 ± 0.9), suggesting changes in water exchange and T2* effects associated with cancer. In addition, decreases in the percentage change in T1 relaxation rate as a function of increasing contrast media concentration (ie, the "saturation effect") can also contribute to the observed differences in high-dose and low-dose α. Area under the receiver operating characteristic curve for differentiating PCa from benign tissue using α was higher for low dose (0.769) compared with standard dose (0.625). There were no significant differences between MEP calculated for PCa and normal tissue at the low and standard doses. Moderate significant Pearson correlation for DCE parameters, MEP (r = 0.53) and α (r = 0.58), was found between low and standard doses of GBCA. CONCLUSIONS: These preliminary results suggest that DCE-MRI with a low GBCA dose distinguishes PCa from benign prostate tissue more effectively than does the standard GBCA dose, based on signal enhancement rate. Diagnostic accuracy is similar on qualitative assessment. Prostate cancer diagnosis may be feasible with DCE-MRI with low-dose GBCA. In addition, comparison of enhancement kinetics after low and high doses of contrast media may provide diagnostically useful information.

Magnetic resonance spectroscopy detects differential lipid composition in mammary glands on low fat, high animal fat versus high fructose diets.

The effects of consumption of different diets on the fatty acid composition in the mammary glands of SV40 T-antigen (Tag) transgenic mice, a well-established model of human triple-negative breast cancer, were investigated with magnetic resonance spectroscopy and spectroscopic imaging. Female C3(1) SV40 Tag transgenic mice (n = 12) were divided into three groups at 4 weeks of age: low fat diet (LFD), high animal fat diet (HAFD), and high fructose diet (HFruD). MRI scans of mammary glands were acquired with a 9.4 T scanner after 8 weeks on the diet. 1H spectra were acquired using point resolved spectroscopy (PRESS) from two 1 mm3 boxes on each side of inguinal mammary gland with no cancers, lymph nodes, or lymph ducts. High spectral and spatial resolution (HiSS) images were also acquired from nine 1-mm slices. A combination of Gaussian and Lorentzian functions was used to fit the spectra. The percentages of poly-unsaturated fatty acids (PUFA), mono-unsaturated fatty acids (MUFA), and saturated fatty acids (SFA) were calculated from each fitted spectrum. Water and fat peak height images (maps) were generated from HiSS data. The results showed that HAFD mice had significantly lower PUFA than both LFD (p < 0.001) and HFruD (p < 0.01) mice. The mammary lipid quantity calculated from 1H spectra was much larger in HAFD mice than in LFD (p = 0.03) but similar to HFruD mice (p = 0.10). The average fat signal intensity over the mammary glands calculated from HiSS fat maps was ~60% higher in HAFD mice than in LFD (p = 0.04) mice. The mean or median of calculated parameters for the HFruD mice were between those for LFD and HAFD mice. Therefore, PRESS spectroscopy and HiSS MRI demonstrated water and fat composition changes in mammary glands due to a Western diet, which was low in potassium, high in sodium, animal fat, and simple carbohydrates. Measurements of PUFA with MRI could be used to evaluate cancer risk, improve cancer detection and diagnosis, and guide preventative therapy.

Performance of Ultrafast DCE-MRI for Diagnosis of Prostate Cancer.

RATIONALE AND OBJECTIVES: This study aimed to test high temporal resolution dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for different zones of the prostate and evaluate its performance in the diagnosis of prostate cancer (PCa). Determine whether the addition of ultrafast DCE-MRI improves the performance of multiparametric MRI. MATERIALS AND METHODS: Patients (n = 20) with pathologically confirmed PCa underwent preoperative 3T MRI with T2-weighted, diffusion-weighted, and high temporal resolution (~2.2 seconds) DCE-MRI using gadoterate meglumine (Guerbet, Bloomington, IN) without an endorectal coil. DCE-MRI data were analyzed by fitting signal intensity with an empirical mathematical model to obtain parameters: percent signal enhancement, enhancement rate (α), washout rate (ß), initial enhancement slope, and enhancement start time along with apparent diffusion coefficient (ADC) and T2 values. Regions of interests were placed on sites of prostatectomy verified malignancy (n = 46) and normal tissue (n = 71) from different zones. RESULTS: Cancer (α = 6.45 ± 4.71 s-1, ß = 0.067 ± 0.042 s-1, slope = 3.78 ± 1.90 s-1) showed significantly (P <.05) faster signal enhancement and washout rates than normal tissue (α = 3.0 ± 2.1 s-1, ß = 0.034 ± 0.050 s-1, slope = 1.9 ± 1.4 s-1), but showed similar percentage signal enhancement and enhancement start time. Receiver operating characteristic analysis showed area under the curve for DCE parameters was comparable to ADC and T2 in the peripheral (DCE 0.67-0.82, ADC 0.80, T2 0.89) and transition zones (DCE 0.61-0.72, ADC 0.69, T2 0.75), but higher in the central zone (DCE 0.79-0.88, ADC 0.45, T2 0.45) and anterior fibromuscular stroma (DCE 0.86-0.89, ADC 0.35, T2 0.12). Importantly, combining DCE with ADC and T2 increased area under the curve by ~30%, further improving the diagnostic accuracy of PCa detection. CONCLUSION: Quantitative parameters from empirical mathematical model fits to ultrafast DCE-MRI improve diagnosis of PCa. DCE-MRI with higher temporal resolution may capture clinically useful information for PCa diagnosis that would be missed by low temporal resolution DCE-MRI. This new information could improve the performance of multiparametric MRI in PCa detection.

Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments.

Differences between region-of-interest (ROI) and pixel-by-pixel analysis of dynamic contrast enhanced (DCE) MRI data were investigated in this study with computer simulations and pre-clinical experiments. ROIs were simulated with 10, 50, 100, 200, 400, and 800 different pixels. For each pixel, a contrast agent concentration as a function of time, C(t), was calculated using the Tofts DCE-MRI model with randomly generated physiological parameters (K trans and v e) and the Parker population arterial input function. The average C(t) for each ROI was calculated and then K trans and v e for the ROI was extracted. The simulations were run 100 times for each ROI with new K trans and v e generated. In addition, white Gaussian noise was added to C(t) with 3, 6, and 12 dB signal-to-noise ratios to each C(t). For pre-clinical experiments, Copenhagen rats (n = 6) with implanted prostate tumors in the hind limb were used in this study. The DCE-MRI data were acquired with a temporal resolution of ~5 s in a 4.7 T animal scanner, before, during, and after a bolus injection (<5 s) of Gd-DTPA for a total imaging duration of ~10 min. K trans and v e were calculated in two ways: (i) by fitting C(t) for each pixel, and then averaging the pixel values over the entire ROI, and (ii) by averaging C(t) over the entire ROI, and then fitting averaged C(t) to extract K trans and v e. The simulation results showed that in heterogeneous ROIs, the pixel-by-pixel averaged K trans was ~25% to ~50% larger (p < 0.01) than the ROI-averaged K trans. At higher noise levels, the pixel-averaged K trans was greater than the 'true' K trans, but the ROI-averaged K trans was lower than the 'true' K trans. The ROI-averaged K trans was closer to the true K trans than pixel-averaged K trans for high noise levels. In pre-clinical experiments, the pixel-by-pixel averaged K trans was ~15% larger than the ROI-averaged K trans. Overall, with the Tofts model, the extracted physiological parameters from the pixel-by-pixel averages were larger than the ROI averages. These differences were dependent on the heterogeneity of the ROI.