Positive Enhancement Integral Research Articles

Morfo-dinamske i funkcionalne karakteristike MRI dojki u proceni duktlanog karcinoma in situ i invazivnog duktalnog karcinoma kod postmenopauzalnih ispitanica

Introduction: Most frequent histologic types of breast cancer include invasive ductal carcinoma (IDC) and its most common precursor: ductal carcinoma in situ (DCIS). Based on morpho-dynamic and functional parameters: lesion size, initial signal intensity enhancement (wash-in), time-intensity curve (TIC) type, apparent diffusion coefficient (ADC), and positive enhancement integral (PEI), breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows the differentiation between IDC and in situ lesions in postmenopausal female patients. Methods: A single-center retrospective study included 40 postmenopausal female patients with histopathologically confirmed diagnosis (DCIS n1 =20; IDC n2 =20), examined with full diagnostic protocol (FDP) on 1.5T and 3T MRI scanners. The SPSS 21.0 software package was used for the statistical analysis of the defined parameters in two predefined subgroups. Results: Tumor size was significantly larger (p<0.001) in patients with DCIS. The IDC group showed significantly higher wash-in and PEI values (p<0.001). ADC was significantly higher (p<0.001) in DCIS. There was a statistically significant difference (p<0.05) in the TIC type distribution: TIC type 2 was predominant in patients with DCIS, while the TIC type 3 was predominant in patients with IDC. Conclusion: Based on predefined morpho-dynamic and functional parameters, breast MRI may allow the differentiation between the two types of breast ductal carcinoma: IDC and DCIS. However, histopathological confirmation remains the "golden standard" in differentiation, taking the nature of the disease into account.

The differential diagnosis of lung precursor glandular lesions, micro-invasive adenocarcinoma, and invasive adenocarcinoma using low dose spectral computed tomography perfusion imaging.

Few studies about the association between computed tomography (CT) perfusion imaging parameters and invasiveness in lung adenocarcinoma (LUAD) have been conducted using low dose spectral CT perfusion imaging. The purpose of this study was to investigate application of spectral revolution CT low-dose perfusion imaging in the differential diagnosis of different pathological subtypes of LUAD. This was a cross-sectional study based on historical data from January 2018 to May 2019 in Peking University Cancer Hospital & Institute. A total of 62 cases were enrolled, including 2 cases of atypical adenomatous hyperplasia (AAH), 3 cases of adenocarcinoma in situ (AIS), 4 cases of minimally invasive adenocarcinoma (MIA), and 53 cases of invasive adenocarcinoma (IAC), all confirmed with pathology. The inclusion and exclusion criteria were regulated. Using Revolution low-dose CT perfusion imaging (GE, USA), the CT perfusion parameters of hemodynamics were obtained: blood flow (BF), blood volume (BV), impulse residue function time of arrival (IRF TO), maximum slope of increase (MSI), mean transit time (MTT), permeability surface area product (PS), positive enhancement integral (PEI), and maximum enhancement time (Tmax). Univariate analysis of variance (ANOVA) or Kruskal-Wallis test was used to compare the differences of CT perfusion quantitative parameters among AAH, AIS, MIA, and IAC. Mann-Whitney test was used to compare the difference of CT perfusion imaging parameters between preinvasive lesions (AAH and AIS) and invasive lung cancer (MIA and IAC). Statistically significant differences in IRF TO were observed in LUAD with different invasiveness, namely, among AIS, MIA, and IAC groups (0.56±0.74 vs. 0.54±1.08 vs. 4.39±2.19, P=0.004). Statistically significant differences in IRF TO were also observed between pre-invasive lesions group (AAH and AIS) and invasive lung cancer group (MIA and IAC) (1.12±1.27 vs. 3.75±2.79, P=0.031), and between AAH + AIS + MIA groups and IAC group (0.83±1.13 vs. 4.12±2.69, P<0.001). There were no statistically significant differences in other CT perfusion parameters of hemodynamics among different pathological subtypes of LUAD (P>0.05). The low-dose perfusion parameter IRF TO of revolution CT has the potential to be employed in the differential diagnosis of different pathological subtypes of LUAD.

Endometrioid adenocarcinoma: combined multiparametric MRI and tumour marker HE4 to evaluate tumour grade and lymphovascular space invasion

To assess the value of semi-quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and quantitative diffusion-weighted imaging parameters combined with human epididymis protein 4 (HE4) in predicting the pathological grade and lymphovascular space invasion (LVSI) of endometrioid adenocarcinoma (EAC). Between October 2018 and December 2021, 60 women (mean age, 55 [range, 32-77] years) with EAC underwent preoperative pelvic MRI and HE4 level measurements. The positive enhancement integral (PEI), time to peak, maximum slope of increase (MSI), and maximum slope of decrease were measured by manually drawing a region of interest on the neoplastic tissue. The receiver operating characteristic curve was used to calculate the diagnostic efficiency of the single parameter and combined factors. Lower apparent diffusion coefficients (ADCs) were observed in high-grade tumours (G3) than in low-grade tumours (G1/G2). PEI, MSI, and HE4 levels were higher in the high-grade tumours than in the low-grade tumours (p<0.05). The area under the curve (AUC) for G3 diagnosis using multiparametric MRI combined with HE4 was 0.929. ADC values were significantly lower in the EAC with LVSI than in those without LVSI. Tumours with LVSI showed higher PEI and HE4 levels than those without LVSI (p<0.05). The AUC for LVSI-positive diagnosis using multiparametric MRI combined with HE4 was 0.814. Semi-quantitative DCE-MRI, ADC values, and serum HE4 levels can be used to predict tumour grade and LVSI, and the prediction efficiency of multiparametric MRI combined with serum HE4 is better than that of any single factor.

Value for combination of T1WI star-VIBE with TWIST-VIBE dynamic contrast-enhanced MRI in distinguishing lung nodules.

With the increasing detection rate of lung nodules, the qualitative problem of lung nodules has become one of the key clinical issues. This study aims to evaluate the value of combining dynamic contrast-enhanced (DCE) MRI based on time-resolved imaging with interleaved stochastic trajectories-volume interpolated breath hold examination (TWIST-VIBE) with T1 weighted free-breathing star-volumetric interpolated breath hold examination (T1WI star-VIBE) in identifying benign and malignant lung nodules. We retrospectively analyzed 79 adults with undetermined lung nodules before the operation. All nodules of patients included were classified into malignant nodules (n=58) and benign nodules (n=26) based on final diagnosis. The unenhanced T1WI-VIBE, the contrast-enhanced T1WI star-VIBE, and the DCE curve based on TWIST-VIBE were performed. The corresponding qualitative [wash-in time, wash-out time, time to peak (TTP), arrival time (AT), positive enhancement integral (PEI)] and quantitative parameters [volume transfer constant (Ktrans), interstitium-to-plasma rate constant (Kep), and fractional extracellular space volume (Ve)] were evaluated. Besides, the diagnostic efficacy (sensitivity and specificity) of enhanced CT and MRI were compared. There were significant differences in unenhanced T1WI-VIBE hypo-intensity, and type of A, B, C DCE curve type between benign and malignant lung nodules (all P<0.001). Pulmonary malignant nodules had a shorter wash-out time than benign nodules (P=0.001), and the differences of the remaining parameters were not statistically significant (all P>0.05). After T1WI star-VIBE contrast-enhanced MRI, the image quality was further improved. Compared with enhanced CT scan, the sensitivity (82.76% vs 80.50%) and the specificity (69.23% vs 57.10%) based on MRI were higher than that of CT (both P<0.001). T1WI star-VIBE and dynamic contrast-enhanced MRI based on TWIST-VIBE were helpful to improve the image resolution and provide more information for clinical differentiation between benign and malignant lung nodules.

Assessment of Early Response to Lung Cancer Chemotherapy by Semiquantitative Analysis of Dynamic Contrast-Enhanced MRI.

Objective To evaluate the early chemotherapy response in patients with lung cancer using semiquantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). Methods Twenty-two patients with lung cancer treated with chemotherapy were subjected to DCE-MRI at two time points: before starting treatment and after one week of therapy. The image data were collected by DCE-MRI, and the semiquantitative parameters including positive enhancement integral (PEI), signal enhancement ratio (SER), maximum slope of increase (MSI), and time to peak (TTP) were calculated. After chemotherapy, the parameters and relevant variations between the responders and nonresponders were compared with Mann–Whitney U tests. Student's t-test for paired samples was used to evaluate the temporal changes between pre- and posttreatment images. Results The patients were categorized as 13 responders and 9 nonresponders based on the tumor response evaluation. After chemotherapy, the PEI, SER, and MSI were significantly increased in responders compared with the pretreatment values (P < 0.05), while no obvious decrease in TTP was observed (P > 0.05). However, 9 nonresponders showed no significant changes in PEI, SER, MSI, and TTP values, as compared with those of pretreatment (P > 0.05). Moreover, the increase of PEI was more dramatically in responders than in nonresponders (P < 0.05), but no significantly differences were observed in SER, MSI, and TTP (P > 0.05). Conclusion Semiquantitative analysis of DCE-MRI could provide a reliable noninvasive method for assessing early chemotherapy response in lung cancer patients.

Abstract P1-08-03: Deep learning for early prediction of neoadjuvant chemotherapy response in triple negative breast cancers

Abstract Introduction: Neoadjuvant chemotherapy (NACT) is becoming standard of care for presurgical treatment of triple negative breast cancer (TNBC) patients. Achievement of pathological complete response (pCR) after NACT is associated with improved outcomes. There is currently an unmet need in development of imaging and clinical tools for prediction of pCR to NACT in TNBC. We investigated use of deep learning convolution neural networks (CNNs) for early prediction of pCR in a TNBC cohort on the basis of MRI acquired before the initiation and at the midpoint, after completion of four cycles of NACT (C4). Materials and Methods: Baseline and C4 MRIs of 112 TNBC patients were collected from an ongoing prospective clinical trial (NCT02276443). Four patients were excluded because they underwent different treatment for the second regimen. Among the 108 patients, 52 patients (48%) had pCR confirmed at surgery. Positive enhancement integral (PEI) derived from the early phases of DCE MRI, and apparent diffusion coefficients (ADC) derived from DWI MRI (b = 100 and 800 s/mm2), were used for our investigation. The images were aligned and the tumor regions were cropped from all images. All tumor patches were normalized between [0, 1], and were padded to form matrices of the same size of 192×192×64 for PEI, or the size of 192×192×16 for ADC. The CNN was constructed using stacked 3D convolution and MaxPooling layers. It consisted of up to four channels for the inputs (baseline and C4 PEI and ADC). Features extracted from each channel were concatenated and regressed for pCR prediction via three densely connected layers. Binary cross-entropy was used as the loss function for CNN training, and the loss was optimized using an Adam optimizer with the initial learning rate of 0.0001. Because of the currently limited sample size, four-fold cross-validation was used for CNN training and evaluation. The patients were divided into four groups, each group had 27 patients and the pCR:non-pCR ratio was controlled as 13:14. For each fold, one group was reserved as the independent testing group, and the other three groups were combined for network training and internal validation. Receiver operating characteristic (ROC) curve was plotted for each fold of testing, and area under the curve (AUC) was calculated. Final performance of the CNN was determined by averaging the AUCs of the four testing folds. Additionally, to test the prediction efficacy of each input, we trained the CNN under the same settings but used PEI or ADC only as input, and the results were compared. Results: The CNN trained with PEI only achieved an average AUC of 0.65 ± 0.09. The second CNN trained with ADC only achieved an average AUC of 0.72 ± 0.07. The third CNN trained with both PEI and ADC achieved an average AUC of 0.73 ± 0.06. Conclusion and Discussion: Using baseline and mid-treatment MRIs, deep learning CNN showed promising performance to predict pCR in the early course of NACT. The prediction AUC for the independent testing groups was largely improved by using ADC to train the network, indicating that ADC can have more critical information than PEI in assisting pCR prediction during the early course of NACT. Future work includes curation of a larger patient data for network training and evaluation to improve the prediction performance and further validate generalization of the network. We will also explore more advanced network structures, through which the prediction performance can be improved. Four-fold cross-validation AUCs of the network using different data as inputs.PEIADCPEI+ADCFold 10.570.640.66Fold 20.760.800.77Fold 30.660.700.68Fold 40.590.740.79Average0.65 ± 0.090.72 ± 0.070.73 ± 0.06 Citation Format: Zijian Zhou, Nabil A Elshafeey, David E Rauch, Beatriz E Adrada, Rosalind P Candelaria, Mary S Guirguis, Wei Yang, Medine Boge, Rania M Mohamed, Gary J Whitman, Deanna L Lane, Huong C Le-Petross, Jessica WT Leung, Lumarie Santiago, Marion E Scoggins, David A Spak, Miral M Patel, Frances Perez, Debu Tripathy, Vicente Valero, Clinton Yam, Stacy Moulder, Jason B White, Jong Bum Son, Mark D Pagel, Jingfei Ma. Deep learning for early prediction of neoadjuvant chemotherapy response in triple negative breast cancers [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-03.

Low-dose spectral CT perfusion imaging of lung cancer quantitative analysis in different pathological subtypes.

BackgroundTo explore the value of the quantitative parameters of low-dose computed tomography (CT) perfusion in the diagnosis of lung cancers of different pathological types.MethodsEighty-five patients with lung cancer confirmed by pathology underwent enhanced spectral CT imaging with a General Electric (GE) Revolution Xtream CT scanner, including 7 patients with lung squamous cell carcinoma, 8 patients with small cell carcinoma, 67 patients with lung adenocarcinoma, and 3 patients with other pathologies. The low-dose CT perfusion parameters [blood flow (BF), blood volume (BV), time of arrival (IRF TO), maximum slope of increase (MSI), mean transit time (MTT), positive enhancement integral (PEI), time to peak (TTP) and time to maximum (Tmax)] were calculated and compared among the first three groups. One-way analysis of variance (ANOVA) or the Kruskal-Wallis test was used to compare the quantitative parameters among the three groups, and the Bonferroni method was used to correct for multiple comparisons.ResultsAmong the quantitative parameters, MSI was significantly different among the three lung cancers (adenocarcinoma vs. squamous cell carcinoma vs. small cell carcinoma: 11.37±8.74 vs. 2.35±0.88 vs. 1.40±0.26, respectively; P=0.016). The MSI of lung adenocarcinoma was lower than that of non-adenocarcinoma (P=0.001), and the MSI of small cell carcinoma was lower than that of non-small cell carcinoma (P=0.014). There were no significant differences in the other parameters among these three groups (P>0.05).ConclusionsLow-dose CT perfusion parameters may have a certain value in classifying the pathological type of lung cancer.

The value of MRI in identifying pancreatic neuroendocrine tumour G3 and carcinoma G3

To explore the magnetic resonance imaging (MRI) differences between pancreatic neuroendocrine tumour grade 3 (pNET-G3) and pancreatic neuroendocrine carcinoma grade 3 (pNEC-G3). Between 2009 and 2019, 31 patients underwent pNEN-G3 resection with preoperative MRI in two local hospitals in China. The 31 patients were assigned to a pNET-G3 group (n=13) or a pNEC-G3 group (n=18). The MRI findings between the groups were compared. There was no statistically significant difference between the two groups in lesion size, clinical characteristics, or laboratory indexes. The lesions showed high or slightly higher signal on diffusion-weighted imaging and decreased apparent diffusion coefficient (ADC) values, which differed between the two groups (p=0.013). The difference between the groups regarding positive enhancement integral, arterial phase and portal phase signal enhancement ratio were statistically significant; however, the delayed phase signal enhancement ratio was not significantly different. pNET-G3 and pNEC-G3 showed different characteristics on MRI. In particular, the ADC value and dynamic enhanced imaging could have an important role in distinguishing between the two.

Abstract PD6-06: Radiomic phenotypes from dynamic contrast-enhanced MRI (DCE-MRI) parametric maps for early prediction of response to neoadjuvant systemic therapy (NAST) in triple negative breast cancer (TNBC) patients

Abstract Background and Purpose:Early and accurate assessment ofbreast cancer response to NAST is important for patient management. In this study, we investigated the value of radiomic phenotypes derived from semi-quantitative and quantitative DCE-MRI parametric maps for early prediction of NASTresponse in TNBC patients. MATERIALS AND METHODS:This IRB approved study included 74 patients with stage I-III TNBC who were enrolled in the prospective ARTEMIS trial (NCT02276443). Pathologic complete response (pCR) and non-pCR were assessed by surgical histopathology after NAST (pCR=34; non-pCR=40).MRI scans were obtained at 3 time points during the NAST treatment with every 2-week anthracycline-based chemotherapy (AC): at baseline (BSL=74), post-2 cycles of AC (C2= 27) and post-4 cycles of AC (C4= 27). Patients went on to receive taxane-based chemotherapy prior to surgery. Tumor regions of interest (ROIs) were segmented by a breast radiologist at the early-phase subtractions of DCE-MRI scans using in-house developed software, followed by co-registration of the ROIs with quantitative (Ktrans, Veand Kep), and semi-quantitative DCE parametric maps (Maximum Slope Increase (MSI), Positive Enhancement Integral (PEI) and Peak Signal Enhancement Ratio (SER)).A total of 93 first order radiomic features were extracted from the tumor ROIs of each time point semi-quantitative DCE parametric map, while a total of 390 extracted radiomic features (first order-histogram features and second order grey-level-co-occurrence matrix) were extracted from each quantitative DCE parametric map using an in-house developed Matlab software.Radiomic features at each time point and changes between the 3 time points were compared between pCR and non-pCR using Wilcoxon Rank Sum test and Fisher’s exact test. Area under the receiver operating characteristics curve (AUC) was used to determine which features predicted pCR.Logistic regression was performed for feature selection, and used to build the radiomic phenotype model. The model performance was assessed by leave-one-out cross validation and 3-fold cross validation. RESULTS:Thirty-three radiomic features from PEI map were significantly different between pCR and non-pCR. The PEI most significant features were changesbetween BSL and C4 in skewness, mean and median (AUC=0.87, 0.85 and 0.87, p=&amp;lt;0.001, 0.001 and 0.002 respectively). Additionally, 31 MSI features were significantly different between pCR and non-pCR. The top 2 features were the interscan-change in skewness between BSL and C2 (AUC=0.80, P=0.007) and C4 standard deviation (AUC=0.80, P=0.006). Four BSL Veradiomic features were statistically significant between pCR and non-pCR with the best being range of difference variance (AUC=0.64, P=0.03). One BSL Kepfeature (Angular-Variance of Information measure of correlation-2) was able to differentiate pCR from non-pCR (AUC=0.64, P=0.04). Five C4-Ktrans features were able to differentiate pCR and non-pCR, with the most significant being mean value (AUC=0.86, P=0.001). BSL-Kepradiomic model built from 24 features (AUC=0.80, p=0.003) and combined (Ktrans, Veand Kep)C2-radiomic model consisting of 20 features (AUC=0.97, p=0.01) showed the best performance for prediction of pCR. CONCLUSIONS:Radiomic phenotypes form DCE-MRI parametric maps were useful for differentiation between pCR and non-pCR and showed promise as noninvasive imaging biomarkers for early prediction of NAST response in TNBC. Potentially, DCE-MRI radiomic features may be used for development of diagnostic predictive model for early noninvasive assessment of NAST treatment response in TNBC patients. Citation Format: Nabil Elshafeey, Beatriz E Adrada, Rosalind P Candelaria, Abeer H Abdelhafez, Benjamin C Musall, Jia Sun, Medine Boge, Rania M.M Mohamed, Hagar S Mahmoud, Jong Bum Son, Aikaterini Kotrosou, Shu Zhang, Jessica Leung, Deanna Lane, Marion Scoggins, David Spak, Elsa Arribas, Lumarie Santiago, Gary J. Whitman, Huong T Le-Petross, Tanya W Moseley, Jason B White, Elizabeth Ravenberg, Ken-Pin Hwang, Peng Wei, Jennifer K Litton, Lei Huo, Debu Tripathy, Vicente Valero, Alastair M Thompson, Stacy Moulder, Wei T Yang, Mark D Pagel, Jingfei Ma, Gaiane M Rauch. Radiomic phenotypes from dynamic contrast-enhanced MRI (DCE-MRI) parametric maps for early prediction of response to neoadjuvant systemic therapy (NAST) in triple negative breast cancer (TNBC) patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-06.

Staging MRI of uterine malignant mixed Müllerian tumors versus endometrial carcinomas with emphasis on dynamic enhancement characteristics.

To determine whether staging pelvic magnetic resonance imaging (MRI) can distinguish malignant mixed Müllerian tumor (MMMT) from EC. Thirty-seven treatment-naïve patients with histologically proven uterine MMMT and 42 treatment-naïve patients with EC, treated at our institution, were included in our retrospective study. Staging pelvic MRI scans were reviewed for tumor size, prolapse through cervical os, and other features. Time-intensity curves for tumor and surrounding myometrium regions of interest were generated, and positive enhancement integral (PEI), maximum slope of increase (MSI), and signal enhancement ratio (SER) were measured. The Fisher's exact test or Wilcoxon rank-sum test was used to compare characteristics between disease groups. Multivariate and univariate logistic regression models were used to distinguish MMMT from EC. Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate prediction ability. MMMTs were larger than ECs with higher rate of tumor prolapse and more heterogeneous tumor enhancement compared to ECs. During the late phase of contrast enhancement, 100% of ECs, but only 84% of MMMTs, had lower signal intensity than the myometrium. Threshold PEI ratio ≥ 0.67 predict MMMT with 76% sensitivity, 84%, specificity and 0.83 AUC. Threshold SER ≤ 125 predict MMMT with 90% sensitivity, 50% specificity, and 0.72 AUC. MMMTs may show more frequent tumor prolapse, more heterogeneous enhancement, delayed iso- or hyper-enhancement, higher PEI ratios, and lower tumor SERs compared with EC. MRI can be used as a biomarker to distinguish MMMT from EC based on the enhancement pattern.

Histogram analysis of dynamic contrast enhancement MRI in predicting long-term treatment effect of cervical cancer after chemoradiotherapy

Objective To investigate the potential value of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting long-term survival of cervical cancer treated by concurrent chemoradiotherapy. Methods Uterine cervical cancer patients who received chemo-radiation therapy during 2008 to 2014 in a single institute were retrospectively collected and followed up. Pre-treatment DCE-MRI images were retrieved to generate four hemodynamic related parameters including positive enhancement integral (PEI), maximum slop of increase (MSI), maximum slope of decrease (MSD) and signal enhancement ratio (SER). Region of interests were drawn manually on T2WI along the rim of tumors on each slice, and were then registered to DCE-MRI parametric maps. Histogram analysis software was used to calculate the mean, median, maximum, minimum,10th percentile, 90th percentile, kurtosis and skewness values for each DCE-MRI parameters. Median follow-up time was 54.7 months (range 2.1 to 94.6 months).Uni-and multivariable Cox regression analyses were used to evaluate correlation between the above values and the disease free survival (DFS). Kaplan-Meier curve was used to evaluate survival time. Results Of the 75 patients, 16 of them died from cervical cancer, 3 patients had metastasis, and 1 patient continued to progress. Median, mean, 10th and 90th percentile from MSI, and minimum, kurtosis, skewness were the influencing factors of disease-free survival of cervical cancer with concurrent chemoradiotherapy(P<0.1) Pearson relationship analysis and multivariable Cox regression analysis was performed which indicated PEI kurtosis value of cervical cancer was an independent influencing factor for cervical cancer without disease survival (hazard ratio 1.658, P=0.001). Conclusion PEI kurtosis was an independent factor for DFS of locally advanced cervical cancer treated with concurrent chemoradiotherapy. Key words: Uterine cervical neoplasms; Long-time survival; Magnetic resonance imaging; Histogram analysis

Evaluation of quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging in qualitative diagnosis of hepatic masses

BackgroundTo explore the value of parameters of multiphase dynamic contrast-enhanced magnetic resonance imaging (MDCE-MRI) in the qualitative diagnosis of hepatic masses.MethodsEighty patients with hepatic masses were retrospectively analyzed. All the patients underwent MDCE-MRI at 3.0 T MR before treatment. Mean enhancement time (MET), positive enhancement integral (PEI), a maximum slope of increase (MSI), and a maximum slope of decrease (MSD) were measured.ResultsThere were significant differences between benign and malignant hepatic masses with respect to MET, PEI, and MSI values. The PEI and MSI values between hemangiomas, hepatocellular carcinomas (HCCs), cholangiocarcinomas, and metastatic tumors had significant differences. The MSD value between metastatic tumors, HCCs, and hemangiomas were significantly different. The area under the curve (AUC) values of the receiver operator characteristic curves for MET, PEI, and MSI were 0.70, 0.72, and 0.80, respectively. The specificity of MET, PEI, and MSI were all 77%, and the sensitivities of MSI was the highest, of which was 82.40%. Logistic regression analysis showed the regression equation to be P = 1/[1 + e0.008 × 1 + 0.007 × 2–6.707], and taking the Youden index maximum points as a diagnostic point was 0.2946.ConclusionSome parameters of MDCE-MRI have significant roles in differentiating hepatic masses.

Can dual-energy CT replace perfusion CT for the functional evaluation of advanced hepatocellular carcinoma?

To determine the degree of relationship between iodine concentrations derived from dual-energy CT (DECT) and perfusion CT parameters in patients with advanced HCC under treatment. In this single-centre IRB approved study, 16 patients with advanced HCC treated with sorafenib or radioembolization who underwent concurrent dynamic perfusion CT and multiphase DECT using a single source, fast kV switching DECT scanner were included. Written informed consent was obtained for all patients. HCC late-arterial and portal iodine concentrations, blood flow (BF)-related and blood volume (BV)-related perfusion parameters maps were calculated. Mixed-effects models of the relationship between iodine concentrations and perfusion parameters were computed. An adjusted p value (Bonferroni method) < 0.05 was considered significant. Mean HCC late-arterial and portal iodine concentrations were 22.7±12.7 mg/mL and 18.7±8.3 mg/mL, respectively. Late-arterial iodine concentration was significantly related to BV (mixed-effects model F statistic (F)=28.52, p<0.0001), arterial BF (aBF, F=17.62, p<0.0001), hepatic perfusion index (F=28.24, p<0.0001), positive enhancement integral (PEI, F=66.75, p<0.0001) and mean slope of increase (F=32.96, p<0.0001), while portal-venous iodine concentration was mainly related to BV (F=29.68, p<0.0001) and PEI (F=66.75, p<0.0001). In advanced HCC lesions, DECT-derived late-arterial iodine concentration is strongly related to both aBF and BV, while portal iodine concentration mainly reflects BV, offering DECT the ability to evaluate both morphological and perfusion changes. • Late-arterial iodine concentration is highly related to arterial BF and BV. • Portal iodine concentration mainly reflects tumour blood volume. • Dual-energy CT offers significantly decreased radiation dose compared with perfusion CT.

Rectal perfusion parameters normalised to tumour-free rectal wall can predict response to neoadjuvant chemoradiotherapy

To evaluate absolute changes in quantitative and semi-quantitative perfusion parameters using a newer approach of comparing these parameters with tumour-free normal rectal wall (i.e., relative/normalised change) in predicting complete pathological response to chemoradiotherapy. Perfusion parameters measured before and after treatment of 10 patients with histopathologically proven rectal cancer that showed complete treatment response (Group 1) were compared with 10 patients with residual tumour on histopathology following treatment (Group 2). Quantitative perfusion MRI parameters (Ktrans: volume transfer coefficient reflecting vascular permeability, Kep: flux rate constant, Ve: extracellular volume ratio reflecting vascular permeability, integral of area under the curve (IAUC); Toft model) were quantified by manually delineating a region of interest in the upper, mid and lower third of the tumour (1 cm2), in addition similar parameters were obtained from the normal rectal wall at least 1 cm away from the potential resection margin, absolute as well as relative perfusion values normalised to that of the normal rectal wall were evaluated. The differences in absolute and normalised qualitative parameters were compared within each group using paired t-tests and between each group using analysis of variance (ANOVA). Wash-in, wash-out, positive enhancement integral (PEI), Ktrans, IAUC in the complete pathological responders when compared to the adjacent normal rectal wall showed ratios approaching 1, suggesting that rectal perfusion is similar to the adjacent normal rectal wall in complete pathological responders. The difference in the normalised values in the responders and non-responders was statistically significant. Perfusion parameters can be used in predicting response to treatment, when normalised to the adjacent normal rectal wall.

Dynamic contrast-enhanced magnetic resonance imaging for pretreatment prediction of early chemo-radiotherapy response in larynx and hypopharynx carcinoma.

PurposeThis study is to investigate the use of dynamic contrast-enhanced magnetic resonance imaging in predicting early response to CRT (chemo-radiotherapy) in patients with larynx and hypopharynx carcinoma from primary tumors.MethodSixty-two patients with larynx and hypopharynx carcinoma underwent two DCE-MRI studies: a baseline exam before any treatmentanda post-treatment exam 3 weeks after CRT. At the end of treatment, patients were classified as responders, or non-responders according to the Response Evaluation Criteria in Solid Tumors criteria (RECIST). The time intensity curves (TIC) were extracted and processed to obtain time to peak (TTP), maximum slope of increase (MSI), maximum slope of decrease (MSD) and positive enhancement integral (PEI), and the semi-quantitative MRI parameters were compared and analyzed between the two groups.ResultsFifty-four and 8 patients were included the responder and non-responder groups. It was observed that the MSI, MSD, and PEI were significantly lower post-treatment than pre-treatment(P < 0.05). The pretreatment MSI, MSD, and PEI parameters of responders were significantly higher than those of non-responders (P< 0.05). The post-treatment MSI, MSD, and PEI parameters of responders were significantly lower than those of non-responders (P< 0.05). Based on ROC curve analysis, at a threshold of 154.81 for pretreatment MSI, the corresponding AUC, sensitivity, and specificity were 0.882, 89.3% and 73.5%, respectively.ConclusionThe semi-quantitative DCE-MRI may aid in the prediction of early response to CRT in patients with larynx and hypopharynx carcinoma.

Correlation study between intravoxel incoherent motion MRI and dynamic contrast-enhanced MRI in head and neck squamous cell carcinoma: Evaluation in primary tumors and metastatic nodes

ObjectiveTo correlate intravoxel incoherent motion (IVIM) imaging and dynamic contrast-enhanced (DCE) MRI in head and neck squamous cell carcinoma (HNSCC). MethodsForty untreated patients with HNSCC were included retrospectively in the study. Perfusion fraction f, diffusion coefficient D and perfusion-related diffusion coefficient D* were extracted by bi-exponential fitting of IVIM data. Semi-quantitative DCE-MRI parameters, including positive enhancement integral (PEI) and maximum slope of increase (MSI), were calculated. The relationships between all variables were assessed by Spearman's test for correlation. Results27 primary tumors (PTs) and 23 lymph nodes (LNs) were analyzed. The residual sum of squares (RSS), used to assess the fit quality, was significantly different between PTs and LNs, with the last showing lower values. In LNs, D* and the product D*×f were positively related to both nPEI and nMSI, while no significant correlation was found in PTs. ConclusionEvident relationships between D* and D*×f and DCE-MRI perfusion measurements were found in LNs, while no significant association emerged in PTs. This presumably is due to the poorer agreement between the experimental data and curve fitting for PTs, as compared to LNs. Additional work is warranted to improve the reliability of the IVIM parameter estimations in primary HNSCCs.

Semi-quantitative assessment of the presence and Child–Pugh class of hepatitis B related cirrhosis by using liver lobe-based dynamic contrast-enhanced MRI

To determine whether liver lobe-based DCE-MRI can be used to detect the presence and Child-Pugh class of hepatitis B-related cirrhosis. Fifty-six cirrhotic patients with hepatitis B and 20 healthy participants underwent liver DCE-MRI, and the positive enhancement integral (PEI), time to peak (TTP), maximum slope of increase (MSI) and maximum slope of decrease (MSD) of the left lateral liver lobe (LLL), left medial liver lobe (LML), right liver lobe (RL), and caudate lobe (CL) were measured and analysed statistically to evaluate cirrhosis. TTP values of the LLL, LML, RL and CL were positively correlated with the Child-Pugh class of cirrhosis (r=0.452 to 0.55, all p<0.05). PEI values of the LLL, LML, RL and CL, as well as the MSI of the CL and the MSD of the RL, were inversely correlated with the Child-Pugh class (r=-0.349 to -0.72, all p<0.05). PEI values of the LLL and CL, or TTP values of the RL had the most area under receiver operating characteristic curve (AUC) of 0.99 for identifying the presence of liver cirrhosis. The PEI of the RL had the largest AUC of 0.975 and 0.78 for distinguishing the Child-Pugh class A of cirrhosis from class B-C and class A-B of cirrhosis from class C, respectively. Liver lobe-based DCE-MRI parameters are associated with the presence and Child-Pugh class of hepatitis B-related cirrhosis.

Optimization of Region of Interest Drawing for Quantitative Analysis: Differentiation Between Benign and Malignant Breast Lesions on Contrast-Enhanced Sonography.

This study was performed to evaluate the diagnostic utility of quantitative analysis of benign and malignant breast lesions using contrast-enhanced sonography. Contrast-enhanced sonography using the perflubutane-based contrast agent Sonazoid (Daiichi Sankyo, Tokyo, Japan) was performed in 94 pathologically proven palpable breast mass lesions, which could be depicted with B-mode sonography. Quantitative analyses using the time-intensity curve on contrast-enhanced sonography were performed in 5 region of interest (ROI) types (manually traced ROI and circular ROIs of 5, 10, 15, and 20 mm in diameter). The peak signal intensity, initial slope, time to peak, positive enhancement integral, and wash-out ratio were investigated in each ROI. There were significant differences between benign and malignant lesions in the time to peak (P < .05), initial slope (P < .001), and positive enhancement integral (P < .05) for the manual ROI. Significant differences were found between benign and malignant lesions in the time to peak (P < .05) for the 5-mm ROI; the time to peak (P < .05) and initial slope (P< .05) for the 10-mm ROI; absolute values of the peak signal intensity (P< .05), time to peak (P< .01), and initial slope (P< .005) for the 15-mm ROI; and the time to peak (P < .05) and initial slope (P < .05) for the 20-mm ROI. There were no statistically significant differences in any wash-out ratio values for the 5 ROI types. Kinetic analysis using contrast-enhanced sonography is useful for differentiation between benign and malignant breast lesions.

Dynamic Contrast Enhanced Magnetic Resonance Perfusion Imaging in High-Risk Smokers and Smoking-Related COPD: Correlations with Pulmonary Function Tests and Quantitative Computed Tomography

The study aimed to prospectively evaluate correlations between dynamic contrast-enhanced (DCE) MR perfusion imaging, pulmonary function tests (PFT) and volume quantitative CT in smokers with or without chronic obstructive pulmonary disease (COPD) and to determine the value of DCE-MR perfusion imaging and CT volumetric imaging on the assessment of smokers. According to the ATS/ERS guidelines, 51 male smokers were categorized into five groups: At risk for COPD (n = 8), mild COPD (n = 9), moderate COPD (n = 12), severe COPD (n = 10), and very severe COPD (n = 12). Maximum slope of increase (MSI), positive enhancement integral (PEI), etc. were obtained from MR perfusion data. The signal intensity ratio (RSI) of the PDs and normal lung was calculated (RSI = SIPD/SInormal). Total lung volume (TLV), total emphysema volume (TEV) and emphysema index (EI) were obtained from volumetric CT data. For “at risk for COPD,” the positive rate of PDs on MR perfusion images was higher than that of abnormal changes on non-enhanced CT images (p < 0.05). Moderate-to-strong positive correlations were found between all the PFT parameters and SIPD, or RSI (r range 0.445∼0.683, p ≤ 0.001). TEV and EI were negatively correlated better with FEV1/FVC than other PFT parameters (r range −0.48 –−0.63, p < 0.001). There were significant differences in RSI and SIPD between “at risk for COPD” and “very severe COPD,” and between “mild COPD” and “very severe COPD”. Thus, MR perfusion imaging may be a good approach to identify early evidence of COPD and may have potential to assist in classification of COPD.

Positive enhancement integral values in dynamic contrast enhanced magnetic resonance imaging of breast carcinoma: Ductal carcinoma in situ vs. invasive ductal carcinoma

ObjectivesThe aim of this study was to contribute to the standardization of the numeric positive enhancement integral (PEI) values in breast parenchyma, ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) and to evaluate the significance of the difference in PEI values between IDC and parenchyma, DCIS and parenchyma and IDC and DCIS. Materials and MethodsIn the prospective trial, we analyzed the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of 60 consecutive patients with histologically confirmed unilateral DCIS (n=30) and IDC (n=30) and defined the PEI values (range; mean±SD) for the lesions and the breast parenchyma. Tumor-to-non-tumor (T/NT) ratios were calculated for DCIS and IDC and compared. PEI color maps (PEICM) were created. The differences in PEI values between IDC and parenchyma and between DCIS and parenchyma were tested according to t-test. Analysis of variance (ANOVA) was used to test the differences between the mean PEI values of parenchyma, DCIS and IDC. ResultsIDC showed highly statistically different PEI numeric values compared to breast parenchyma (748.7±32.2 vs. 74.6±17.0; p<0.0001). The same applied to the differences in the group of patients with DCIS (428.0±25.0 vs. 66.0±10.6; p<0.0001). The difference between IDC, DCIS and parenchyma were also considered highly statistically significant (p<0.0001) and so were the T/NT ratios for IDC and DCIS (10.1±2.4 vs. 6.6±1.4; p<0.0001). ConclusionsPEI numeric values may contribute to differentiation between invasive and in situ breast carcinoma.