Tumor Apparent Diffusion Coefficient Research Articles

Evaluating the Prognostic Impact of Apparent Diffusion Coefficient in Definitive Radiotherapy for Gleason Score 7 Prostate Cancer Patients.

To investigate the utility of diffusion-weighted magnetic resonance imaging (DW-MRI) in evaluating Gleason score (GS) 7 tumors before definitive radiotherapy (RT) and to explore its association with clinicopathological factors and treatment outcomes. Clinical data of 266 prostate cancer (PCa) patients with biopsy-confirmed GS 7 who underwent RT were retrospectively analyzed. Pretreatment DW-MRI was utilized to measure apparent diffusion coefficient (ADC) values of primary tumors. Treatment outcomes, including biochemical disease-free survival (bDFS) and prostate cancer-specific survival (PCSS), were assessed. Statistical analyses were conducted to determine the correlation between tumor ADC values, clinicopathological factors, and treatment outcomes. Tumors with a GS of 3 + 4 had significantly higher ADC values than those with a GS of 4 + 3 (0.746 ± 0.150 vs. 0.702 ± 0.157 × 10-³ mm²/s; p < 0.001). Median follow-up time was 8.6 years, and the 7-year rates for bDFS and PCSS were 89.1% and 95.3%, respectively. Lower tumor ADC values were significantly correlated with higher GS and increased risk of disease progression. A primary tumor ADC cutoff value of 0.682 × 10-³ mm²/s was identified for predicting disease progression. Patients with higher ADC values exhibited significantly better 7-year bDFS rates (92.8% vs. 83.2%; p = 0.02). However, GS 4 + 3 tumors independently predicted poorer bDFS and PCSS outcomes. In the multivariable analysis, only GS 4 + 3 tumor was predictive for worse bDFS and PCSS. Tumor ADC values are a reliable biomarker for differentiating between GS 3 + 4 and 4 + 3 tumors in the GS 7 category. Tumors exhibiting lower ADC values have been associated to higher risk factors and an increased likelihood of disease progression, particularly in GS 3 + 4 tumors where GS upgrading could happen.

The Diagnostic Value of 3.0 T Magnetic Resonance Imaging Combined with Carbohydrate Antigen 125 and Human Epididymis Protein 4 in Epithelial Ovarian Cancer

Background: Epithelial ovarian cancer (EOC) is among the top 5 causes of cancer-associated death in women. We explored the diagnostic value of 3.0 T magnetic resonance imaging (MRI) combined with carbohydrate antigen 125 (CA125) and human epididymis protein 4 (HE4) on EOC. Methods: Among 380 ovarian space-occupying patients in this retrospective analysis, 344 were included, 59 were excluded, 25 had incomplete clinical data, and 260 were finally included as the subjects. Patients were classified into the EOC (110 patients with EOC) and N-EOC (150 patients without EOC) groups. The levels of interleukin-6 (IL-6), C-reactive protein (CRP), follicle stimulating hormone (FSH), luteinizing hormone (LH), anti-mullerian hormone (AMH), CA125, and HE4 were determined. The apparent diffusion coefficient (ADC) value and the clinical diagnostic value of MRI, CA125, HE4, and their combination were analyzed. Results: There were significant differences in tumor family history, gravidity, parity, IL-6, CRP, FSH, LH, AMH, and ADC value between EOC and N-EOC patients (all p &lt; 0.05). EOC patients exhibited highly-expressed CA125 and HE4 (p &lt; 0.01). CA125/HE4 expression was correlated with the clinicopathological features of EOC, while the ADC value was correlated with the EOC tumor diameter (all p &lt; 0.05). MRI [area under the curve (AUC) = 0.77], CA125 (AUC = 0.85) and HE4 (AUC = 0.90) had certain diagnostic value for EOC (all p &lt; 0.05). Conclusions: The prevalence of EOC in ovarian space-occupying patients with highly-expressed CA125 and HE4 is higher. MRI combined with CA125 and HE4 has high clinical diagnostic value for EOC.

NIMG-36. PERFUSION, DIFFUSION, AND ANATOMICAL MRI BIOMARKERS FOR ASSESSING HISTOLOGICALLY-CONFIRMED MALIGNANT TRANSFORMATION IN MOLECULAR SUBTYPES OF IDH-MUTANT GLIOMAS

Abstract BACKGROUND Isocitrate dehydrogenase-mutant (IDHm) gliomas often initially present as low-grade (grade 2) gliomas but are expected to later progress into more aggressive higher-grade (grade 3-4) gliomas through malignant transformation (MT). MT is often determined non-invasively by emergence of contrast-enhancement on post-contrast T1-weighted magnetic resonance imaging (MRI), but contrast-enhancement is an imperfect surrogate for histologically-confirmed MT. This study explored perfusion, diffusion, and anatomical MRI biomarkers besides contrast-enhancement to study histologically-confirmed MT in IDHm 1p/19q-intact astrocytomas (IDHm-A) and IDHm 1p/19q-co-deleted oligodendrogliomas (IDHm-O). METHODS N=64 patients with initial histopathological grade 2 IDH-mutant glioma diagnosis who underwent repeated tissue sampling and were classified as histologically-confirmed MT (n=35) or non-MT (n=29) were retrospectively studied. Pre-surgical anatomical MRI (n=64) and, if available, diffusion-weighted imaging (n=61) and dynamic susceptibility contrast perfusion MRI (n=53) were analyzed. Tumor volumes of interest were segmented on the whole T2/FLAIR-hyperintense tumor. Measurable contrast enhancement (&amp;gt;1000mm3), tumor volume, sphericity, median apparent diffusion coefficient (ADC), and normalized relative cerebral blood volume (nrCBV) were compared between MT vs. non-MT IDHm-A and IDHm-O. RESULTS 81% of contrast-enhancing IDHm-A and 100% of contrast-enhancing IDHm-O underwent MT, while 41% of MT IDHm-A and 62% of MT IDHm-O were non-enhancing. Tumor volumes were significantly larger in the MT vs. non-MT groups for both IDHm-A (P=0.02) and IDHm-O (P=0.04). Whole tumor nrCBV was significantly higher (P=0.002), whole tumor ADC trended lower (P=0.06), and non-enhancing tumor sphericity was significantly lower (P=0.03) in MT vs. non-MT IDHm-A. There were no significant differences in ADC, nrCBV, nor sphericity when comparing MT vs. non-MT IDHm-O (P&amp;gt;0.05). CONCLUSIONS Many MT IDHm-gliomas remain non-enhancing. Tumor volumes can determine MT for both IDHm-A and IDHm-O. Diffusion and perfusion MRI show differences for MT in IDHm-A but not IDHm-O, which may reflect the different tumor biology of these IDHm molecular subtypes and their need for separate imaging biomarkers.

The Efficacy of MRI-Based ADC Measurements in Detecting Axillary Lymph Node Metastasis: Evaluation of a Prospective Study.

Objective: This study aimed to evaluate the efficacy of MRI-based Apparent Diffusion Coefficient (ADC) measurements in detecting axillary lymph node metastasis in breast cancer patients. By comparing preoperative MRI findings with intraoperative sentinel lymph node biopsy (SLNB) and postoperative pathological results, we sought to explore the potential of ADC values as a non-invasive alternative to axillary interventions. Methods: A total of 104 female patients diagnosed with breast cancer between 2019 and 2021 were included in this prospective study. ADC values of axillary lymph nodes, tumors, and muscle tissues were measured using a 3T MRI system. The correlation between these measurements and pathological outcomes was analyzed. Statistical analyses, including t-tests, ANOVA, and ROC curve analysis, were employed to assess the diagnostic performance of ADC values. Results: The results indicated that, while the mean ADC values of metastatic lymph nodes were lower than those of benign nodes, the sensitivity and specificity of MRI-based ADC measurements were inferior to the expected standards. The tumor ADC value and the tumor-to-lymph node ADC ratio were found to be more reliable indicators of metastasis than the lymph node ADC value alone. The diagnostic power of the tumor ADC value was significant, with a sensitivity of 75% and a specificity of 73%. Conclusions: MRI-based ADC measurements, particularly the tumor ADC value and the tumor-to-lymph node ADC ratio, show promise as potential non-invasive markers for axillary lymph node metastasis in breast cancer patients. However, the current results suggest that ADC measurements cannot yet replace SLNB in clinical practice.

Predicting the Malignancy Grade of Soft Tissue Sarcomas on MRI Using Conventional Image Reading and Radiomics

Objectives: This study aims to investigate MRI features predicting the grade of STS malignancy using conventional image reading and radiomics. Methods: Pretherapeutic imaging data regarding size, tissue heterogeneity, peritumoral changes, necrosis, hemorrhage, and cystic degeneration were evaluated in conventional image reading. Furthermore, the tumors’ apparent diffusion coefficient (ADC) values and radiomics features were extracted and analyzed. A random forest machine learning algorithm was trained and evaluated based on the extracted features. Results: A total of 139 STS cases were included in this study. The mean tumor ADC and the ratio between tumor ADC to healthy muscle ADC were significantly lower in high-grade tumors (p = 0.001 and 0.005, respectively). Peritumoral edema (p &lt; 0.001) and peritumoral contrast enhancement (p &lt; 0.001) were significantly more extensive in high-grade tumors. Tumor heterogeneity was significantly increased in high-grade sarcomas, particularly in T2w- and contrast-enhanced sequences using conventional image reading (p &lt; 0.001) as well as in the radiomics analysis (p &lt; 0.001). Our trained random forest machine learning model predicted high-grade status with an area under the curve (AUC) of 0.97 and an F1 score of 0.93. Biopsy-underestimated tumors exhibited differences in tumor heterogeneity and peritumoral changes. Conclusions: Tumor heterogeneity is a key characteristic of high-grade STSs, which is discernible through conventional imaging reading and radiomics analysis. Higher STS grades are also associated with low ADC values, peritumoral edema, and peritumoral contrast enhancement.

Association between programmed cell death ligand-1 expression in patients with cervical cancer and apparent diffusion coefficient values: a promising tool for patient´s immunotherapy selection.

To investigate the associations between apparent diffusion coefficient (ADC) values extracted from three different region of interest (ROI) position approaches and programmed cell death ligand-1 (PD-L1) expression, and evaluate the performance of the nomogram established based on ADC values and clinicopathological parameters in predicting PD-L1 expression in cervical cancer (CC) patients. Through retrospective recruitment, a training cohort of 683 CC patients was created, and a validation cohort of 332 CC patients was prospectively recruited. ROIs were delineated using three different methods to measure the mean ADC (ADCmean), single-section ADC (ADCss), and the minimum ADC of tumors (ADCmin). Logistic regression was employed to identify independent factors related to PD-L1 expression. A nomogram was drawn based on ADC values combined with clinicopathological features, its discrimination and calibration performances were estimated using the area under the curve (AUC) of receiver operating characteristic and calibration curve. The clinical benefits were evaluated by decision curve analysis. The ADCmin independently correlated with PD-L1 expression. The nomogram constructed with ADCmin and other independent clinicopathological-related factors: FIGO staging, pathological grade, parametrial invasion, and lymph node status demonstrated excellent diagnostic performance (AUC = 0.912 and 0.903, respectively), good calibration capacities, and greater net benefits compared to the clinicopathological model in both the training and validation cohorts. ADCmin independently correlated PD-L1 expression, and the nomogram established with ADCmin and clinicopathological independent prognostic factors had a strong predictive performance for PD-L1 expression, thereby serving as a promising tool for selecting cases eligible for immunotherapy. The minimum ADC can serve as a reliable imaging biomarker related to PD-L1 expression; the established nomogram combines the minimum ADC and clinicopathological factors that can assist clinical immunotherapy decisions.

Navigating the gray zone: Machine learning can differentiate malignancy in PI-RADS 3 lesions

IntroductionThe objective of this study is to predict the probability of prostate cancer in PI-RADS 3 lesions using machine learning methods that incorporate clinical and mpMRI parameters. MethodsThe study included patients who had PI-RADS 3 lesions detected on mpMRI and underwent fusion biopsy between January 2020 and January 2024. Radiological parameters (Apparent diffusion coefficient (ADC), tumour ADC/contralateral ADC ratio, Ktrans value, periprostatic adipose tissue thickness, lesion size, prostate volume) and clinical parameters (age, body mass index, total prostate specific antigen, free PSA, PSA density, systemic inflammatory index, neutrophil-lymphocyte ratio [NLR], platelet lymphocyte ratio, lymphocyte monocyte ratio) were documented. The probability of prostate cancer prediction in PI-RADS 3 lesions was calculated using 6 different machine-learning models, with the input parameters being the aforementioned variables. ResultsOf the 235 participants in the trial, 61 had malignant fusion biopsy pathology and 174 had benign pathology. Among 6 different machine learning algorithms, the random forest model had the highest accuracy (0.86±0.04; 95% CI 0.85–0.87), F1 score (0.91±0.03; 95% CI 0.91–0.92) and AUC value (0.92±0.06; 95% CI 0.88–0.90). In SHAP analysis based on random forest model, tumour ADC, tumour ADC/contralateral ADC ratio and PSA density were the 3 most successful parameters in predicting malignancy. On the other hand, systemic inflammatory index and neutrophil lymphocyte ratio showed higher accuracy in predicting malignancy than total PSA, age, free PSA/total PSA and lesion size in SHAP analysis. ConclusionAmong the machine learning models we developed, especially the random forest model can predict malignancy in PI-RADS 3 lesions and prevent unnecessary biopsy. This model can be used in clinical practice with multicentre studies including more patients.

Tumor Apparent Diffusion Coefficient is Associated with Early Recurrence of Intrahepatic Cholangiocarcinoma

Rationale and ObjectivesIdentifying intrahepatic cholangiocarcinoma (iCCA) patients who are at high risk for early recurrence (ER) can guide personalized treatment strategy and improve survival. This study aimed to investigate the value of preoperative MRI, especially diffusion-weighted imaging, in predicting ER, including in patients receiving neoadjuvant therapy. Materials and methodsThis study included 175 pathologically-confirmed iCCA patients who underwent curative resection (114 men, 61 women; mean age 59.0±9.56 years). MRI features, particularly apparent diffusion coefficient (ADC), were analyzed and compared between ER and non-ER cases. Survival analyses of ER were evaluated using Cox regression and Kaplan-Meier analysis. ResultsER occurred in 54.3% (95/175) of patients. Multivariate logistic regression analysis identified tumor ADC as the only independent predictor of ER (odds ratio=0.034, P<0.001), with AUCs of 0.758 (95%CI 0.664, 0.836) in the testing cohort and 0.779 (95%CI 0.622, 0.893) in the validation cohort. The optimal ADC threshold was 1.273×10-3mm2/s. Tumor ADC was comparable to the AJCC 8th staging system in predicting ER (AUC 0.758 vs 0.650 in testing cohort and 0.779 vs 0.661 in validation cohort). Multivariate Cox analysis identified high tumor burden score (HR=1.109, P=0.009), non-smooth margin (HR=2.265, P=0.008) and tumor ADC (HR=0.111, P<0.001) as independent risk factors for ER. Lower ADC values were linked to shorter RFS in both testing and validation cohorts (P<0.001 and 0.0219), as well as in patients receiving neoadjuvant therapy (P=0.003). ConclusionPreoperative MRI, particularly ADC, can help predict ER in iCCA, regardless of the application of neoadjuvant therapy, comparable to the AJCC 8th staging system.

Differentiation between multifocal CNS lymphoma and glioblastoma based on MRI criteria

PurposeDifferentiating between glioblastoma (GB) with multiple foci (mGB) and multifocal central nervous system lymphoma (mCNSL) can be challenging because these cancers share several features at first appearance on magnetic resonance imaging (MRI). The aim of this study was to explore morphological differences in MRI findings for mGB versus mCNSL and to develop an interpretation algorithm with high diagnostic accuracy.MethodsIn this retrospective study, MRI characteristics were compared between 50 patients with mGB and 50 patients with mCNSL treated between 2015 and 2020. The following parameters were evaluated: size, morphology, lesion location and distribution, connections between the lesions on the fluid-attenuated inversion recovery sequence, patterns of contrast enhancement, and apparent diffusion coefficient (ADC) values within the tumor and the surrounding edema, as well as MR perfusion and susceptibility weighted imaging (SWI) whenever available.ResultsA total of 187 mCNSL lesions and 181 mGB lesions were analyzed. The mCNSL lesions demonstrated frequently a solid morphology compared to mGB lesions, which showed more often a cystic, mixed cystic/solid morphology and a cortical infiltration. The mean measured diameter was significantly smaller for mCNSL than mGB lesions (p < 0.001). Tumor ADC ratios were significantly smaller in mCNSL than in mGB (0.89 ± 0.36 vs. 1.05 ± 0.35, p < 0.001). The ADC ratio of perilesional edema was significantly higher (p < 0.001) in mCNSL than in mGB. In SWI / T2*-weighted imaging, tumor-associated susceptibility artifacts were more often found in mCNSL than in mGB (p < 0.001).ConclusionThe lesion size, ADC ratios of the lesions and the adjacent tissue as well as the vascularization of the lesions in the MR-perfusion were found to be significant distinctive patterns of mCNSL and mGB allowing a radiological differentiation of these two entities on initial MRI. A diagnostic algorithm based on these parameters merits a prospective validation.

Application of proton density fat fraction imaging in risk stratification of prostate cancer.

Prostate cancer (PCa) as one of the most prevalent malignancies in men. We introduced a non-invasive quantitative measurement of intraprostatic fat content based on magnetic resonance proton density fat fraction (PDFF) imaging. The study aims to determine the fat fraction (FF) of PCa using proton density magnetic resonance imaging (MRI), gather clinical and routine MRI characteristics, and identify risk factors for high-risk PCa through multifactorial logistic regression. Clinical and imaging data from 191 pathologically confirmed PCa patients were collected. Patients were stratified based on Gleason score (GS), with 63 in the intermediate- and low-risk group (GS =3+3, 3+4) and 128 in the high-risk group (GS ≥4+3). All patients underwent routine prostate MRI and FF imaging. Clinical and imaging data related to PCa were analyzed, including age, body mass index (BMI), prostate volume (PV) measured by MRI, smoking history, alcohol history, diabetes history, serum prostate-specific antigen (PSA) level, apparent diffusion coefficient (ADC) value, T2 signal intensity (T2SI), Prostate Imaging Reporting and Data System 2.1 (PI-RADS 2.1) score, GS, lesion FF, whole gland FF, periprostatic fat thickness (PPFT), and subcutaneous fat thickness (SFT). Independent risk factors for stratifying PCa risk were identified through multivariate logistic regression analysis, and a predictive model was established. Receiver operating characteristic (ROC) curve analysis was conducted for visual analysis. Significant differences were found in BMI, PV, PSA, tumor ADC value, standard T2SI, PI-RADS score, lesion FF, and PPFT between low- and medium-risk and high-risk groups (P<0.05). No significant differences were observed in age, smoking history, drinking history, diabetes history, and SFT between the two groups (P>0.05). GS correlated significantly with FF (ρ=0.6, P<0.001), PSA (ρ=0.432, P<0.001), ADC value (ρ=-0.379, P<0.001), and PI-RADS (ρ=0.366, P<0.001). Multiple logistic regression analysis revealed that an increase in FF, a PI-RADS score increase of 5 points, and a decrease in ADC value and PV were independent predictors of high-risk PCa (P<0.05). The ROC curve showed that the best cut-off value for the model was 0.67, with an area under the curve (AUC) of 0.907, sensitivity of 78.1%, and specificity of 88.9%. The FF of PCa determined by proton density MRI is significantly associated with GS, serving as an independent predictor of high-risk PCa.

Intravoxel incoherent motion diffusion-weighted imaging in evaluating preoperative staging of esophageal squamous cell carcinoma

BackgroundThe aim of this research is to prospectively investigate the diagnostic performance of intravoxel incoherent motion (IVIM) using the integrated slice-specific dynamic shimming (iShim) technique in staging primary esophageal squamous cell carcinoma (ESCC) and predicting presence of lymph node metastases from ESCC.MethodsSixty-three patients with ESCC were prospectively enrolled from April 2016 to April 2019. MR and IVIM using iShim technique (b = 0, 25, 50, 75, 100, 200, 400, 600, 800 s/mm2) were performed on 3.0T MRI system before operation. Primary tumour apparent diffusion coefficient (ADC) and IVIM parameters, including true diffusion coefficient (D), pseudodiffusion coefficient (D*), pseudodiffusion fraction (f) were measured by two independent radiologists. The differences in D, D*, f and ADC values of different T and N stages were assessed. Intraclass correlation coefficients (ICCs) were calculated to evaluate the interobserver agreement between two readers. The diagnostic performances of D, D*, f and ADC values in primary tumour staging and prediction of lymph node metastasis of ESCC were determined using receiver operating characteristic (ROC) curve analysis.ResultsThe inter-observer consensus was excellent for IVIM parameters and ADC (D: ICC = 0.922; D*: ICC = 0.892; f: ICC = 0.948; ADC: ICC = 0.958). The ADC, D, D* and f values of group T1 + T2 were significantly higher than those of group T3 + T4a [ADC: (2.55 ± 0.43) ×10− 3 mm2/s vs. (2.27 ± 0.40) ×10− 3 mm2/s, t = 2.670, P = 0.010; D: (1.82 ± 0.39) ×10− 3 mm2/s vs. (1.53 ± 0.33) ×10− 3 mm2/s, t = 3.189, P = 0.002; D*: 46.45 (30.30,55.53) ×10− 3 mm2/s vs. 32.30 (18.60,40.95) ×10− 3 mm2/s, z=-2.408, P = 0.016; f: 0.45 ± 0.12 vs. 0.37 ± 0.12, t = 2.538, P = 0.014]. The ADC, D and f values of the lymph nodes-positive (N+) group were significantly lower than those of lymph nodes-negative (N0) group [ADC: (2.10 ± 0.33) ×10− 3 mm2/s vs. (2.55 ± 0.40) ×10− 3 mm2/s, t=-4.564, P < 0.001; D: (1.44 ± 0.30) ×10− 3 mm2/s vs. (1.78 ± 0.37) ×10− 3 mm2/s, t=-3.726, P < 0.001; f: 0.32 ± 0.10 vs. 0.45 ± 0.11, t=-4.524, P < 0.001]. The combination of D, D* and f yielded the highest area under the curve (AUC) (0.814) in distinguishing group T1 + T2 from group T3 + T4a. D combined with f provided the highest diagnostic performance (AUC = 0.849) in identifying group N + and group N0 of ESCC.ConclusionsIVIM may be used as an effective functional imaging technique to evaluate preoperative stage of primary tumour and predict presence of lymph node metastases from ESCC.

Association between mpMRI detected tumor apparent diffusion coefficient and 5-year biochemical recurrence risk after radical prostatectomy.

To assess the ability of tumor apparent diffusion coefficient (ADC) values obtained from multiparametric magnetic resonance imaging (mpMRI) to predict the risk of 5-year biochemical recurrence (BCR) after radical prostatectomy (RP). This retrospective analysis included 1207 peripheral and 232 non-peripheral zone prostate cancer (PCa) patients who underwent mpMRI before RP (2012-2015), with the outcome of interest being 5-year BCR. ADC was evaluated as a continuous variable and as categories: low (< 850µm2/s), intermediate (850-1100µm2/s), and high (> 1100µm2/s). Kaplan-Meier curves with log-rank testing of BCR-free survival, multivariable Cox proportional hazard regression models were formed to estimate the risk of BCR. Among the 1439 males with median age 63 (± 7) years, the median follow-up was 59months, and 306 (25%) patients experienced BCR. Peripheral zone PCa patients with BCR had lower tumor ADC values than those without BCR (874 versus 1025µm2/s, p < 0.001). Five-year BCR-free survival rates were 52.3%, 74.4%, and 87% for patients in the low, intermediate, and high ADC value categories, respectively (p < 0.0001). Lower ADC was associated with BCR, both as continuously coded variable (HR: 5.35; p < 0.001) and as ADC categories (intermediate versus high ADC-HR: 1.56, p = 0.017; low vs. high ADC-HR; 2.36, p < 0.001). In the non-peripheral zone PCa patients, no association between ADC and BCR was observed. Tumor ADC values and categories were found to be predictive of the 5-year BCR risk after RP in patients with peripheral zone PCa and may serve as a prognostic biomarker.

Multiparametric MR characterization for human epithelial growth factor receptor 2 expression in bladder cancer: an exploratory study.

To investigate the application value of multiparametric MRI in evaluating the expression status of human epithelial growth factor receptor 2 (HER2) in bladder cancer (BCa). From April 2021 toJuly 2023, preoperative imaging manifestations of 90 patients with pathologically confirmed BCa were retrospectively collected and analyzed. All patients underwent multiparametric MRI including synthetic MRI, DWI, from which the T1, T2, proton density (PD) and apparent diffusion coefficient (ADC) values were obtained. The clinical and imaging characteristics as well as quantitative parameters (T1, T2, PD and ADC values) between HER2-positive and -negative BCa were compared using student t test and chi-square test. The diagnostic efficacy of parameters in predicting HER2 expression status was evaluated by calculating the area under ROC curve (AUC). In total, 76 patients (mean age, 63.59years ± 12.84 [SD]; 55 men) were included: 51 with HER2-negative and 25 with HER2-positiveBCa. HER2-positive group demonstrated significantly higher ADC, T1, and T2 values than HER2-negative group (all P < 0.05). The combination of ADC values and tumor grade yielded the best diagnostic performance in evaluating HER2 expression level with an AUC of 0.864. The multiparametric MR characterization can accurately evaluate the HER2 expression status in BCa, which may further guide the determination of individualized anti-HER2 targeted therapy strategies.

The value of whole tumor apparent diffusion coefficient histogram parameters in predicting meningiomas progesterone receptor expression.

This letter provides a critical assessment of a previous study on the utility of whole tumor apparent diffusion coefficient (ADC) histogram characteristics in predicting meningioma progesterone receptor expression. While acknowledging the benefits of employing classical diffusion-weighted imaging (DWI) for non-invasive tumor evaluation, it also emphasizes significant drawbacks. Advanced imaging techniques such as diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) were not used in the study, which could have provided a more comprehensive understanding of tumor microstructure and heterogeneity. Furthermore, the inclusion of necrotic and cystic areas in ADC analysis may distort results due to their different diffusion properties. While focusing on first-order ADC histogram characteristics is useful, it ignores the potential insights gained from higher-order features and texture analysis. These limitations indicate that future research should combine improved imaging modalities with thorough analytical methodologies to increase the predictive value of imaging biomarkers for meningioma features and progesterone receptor expression.

Diffusion weighted MRI and apparent diffusion coefficient as a prognostic biomarker in evaluating chemotherapy-antiangiogenic treated stage IV non-small cell lung cancer: A prospective, single-arm, open-label, clinical trial (BevMar)

PurposeWhen treating Lung Cancer, it is necessary to identify early treatment failure to enable timely therapeutic adjustments. The Aim of this study was to investigate whether changes in tumor diffusion during treatment with chemotherapy and bevacizumab could serve as a predictor of treatment failure. Material and MethodsA prospective single-arm, open-label, clinical trial was conducted between September 2014 and December 2020, enrolling patients with stage IV non-small cell lung cancer (NSCLC). The patients were treated with chemotherapy-antiangiogenic combination. Diffusion weighted magnetic resonance imaging (DW-MRI) was performed at baseline, two, four, and sixteen weeks after initiating treatment. The differences in apparent diffusion coefficient (ADC) values between pre- and post-treatment MRIs were recorded as Delta values (ΔADC). We assessed whether ΔADC could serve as a prognostic biomarker for overall survival (OS), with a five year follow up. Results18 patients were included in the final analysis. Patients with a ΔADC value ≥ -3 demonstrated a significantly longer OS with an HR of 0.12 (95 % CI; 0.03– 0.61; p = 0.003) The median OS in patients with a ΔADC value ≥ -3 was 18 months, (95 % C.I; 7–46) compared to 7 months (95 % C.I; 5–9) in those with a ΔADC value < -3. ConclusionOur findings suggest that early changes in tumor ADC values, may be indicative of a longer OS. Therefore, DW-MRI could serve as an early biomarker for assessing treatment response in patients receiving chemotherapy combined with antiangiogenic therapy.

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology

BackgroundThe detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model.MethodsWe recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan–Meier curve and log-rank tests were used for survival analysis.ResultsOverall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model.ConclusionsThe proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Evaluating the glymphatic system via magnetic resonance diffusion tensor imaging along the perivascular spaces in brain tumor patients

PurposeTo investigate glymphatic system function in patients with brain tumors, including both primary and secondary tumors, using diffusion tensor imaging along perivascular spaces (DTI-ALPS).MethodsWe retrospectively analyzed the MR DTI of 24 patients with unilateral brain tumors and compared them with age and sex-matched controls. We compared the DTI-ALPS index of the ipsi- and contralateral brain hemispheres. The region of interest was placed in the periventricular vessels adjacent to the lateral ventricles. Differences between sex, age, and kind of tumor (primary or brain metastasis) were evaluated. Correlations between DTI-ALPS index and age and the tumor's apparent diffusion coefficient (ADC) were also investigated.ResultsThe DTI-ALPS index was significantly lower (p < 0.05) in the tumor-affected hemisphere (mean = 1.26 ± 0.24) than contralateral (mean = 1.43 ± 0.28). A comparison with healthy controls revealed no significant difference on the matched ipsilateral side. However, the DTI-ALPS index of the contralateral side of the patients was larger than the HC. Additionally, no statistically significant differences were found when analyzing the DTI-ALPS index vs. age, sex, and tumor entity. Additionally, we did not find a correlation between the DTI-ALPS index and patient age or tumor ADC.ConclusionThe decreased DTI-ALPS index in the tumor-affected hemisphere may be related to impaired glymphatic system function. However, cancer is often a systemic disease; thus, the DTI-ALPS index from the contralateral brain hemisphere may not generally be considered as a normal control. Nonetheless, the DTI-ALPS index does not only reflect diffusion in the perivascular spaces but it can also be influenced by factors such as axonal degeneration. Therefore, it does not directly reflect brain waste clearance and changes in the index should be interpreted carefully.

The value of whole tumor apparent diffusion coefficient histogram parameters in predicting meningiomas progesterone receptor expression.

This study investigated the value of whole tumor apparent diffusion coefficient (ADC) histogram parameters and magnetic resonance imaging (MRI) semantic features in predicting meningioma progesterone receptor (PR) expression. The imaging, pathological, and clinical data of 53 patients with PR-negative meningiomas and 52 patients with PR-positive meningiomas were retrospectively reviewed. The whole tumor was outlined using Firevoxel software, and the ADC histogram parameters were calculated. The differences in ADC histogram parameters and MRI semantic features were compared between the two groups. The predictive values of parameters for PR expression were assessed using receiver operating characteristic curves. The correlation between whole-tumor ADC histogram parameters and PR expression in meningiomas was also analyzed. Grading was able to predict the PR expression in meningiomas (p = 0.012), though the semantic features of MRI were not (all p > 0.05). The mean, Perc.01, Perc.05, Perc.10, Perc.25, and Perc.50 histogram parameters were able to predict meningioma PR expression (all p < 0.05). The predictive performance of the combined histogram parameters improved, and the combination of grade and histogram parameters provided the optimal predictive value, with an area under the curve of 0.849 (95%CI: 0.766-0.911) and sensitivity, specificity, ACC, PPV, and NPV of 73.08%, 81.13%, 77.14%, 79.20%, and 75.40%, respectively. The mean, Perc.01, Perc.05, Perc.10, Perc.25, and Perc.50 histogram parameters were positively correlated with PR expression (all p < 0.05). Whole tumor ADC histogram parameters have additional clinical value in predicting PR expression in meningiomas.

Apparent diffusion coefficient and tissue stiffness are associated with different tumor microenvironment features of hepatocellular carcinoma

ObjectivesTo investigate associations between tissue diffusion, stiffness, and different tumor microenvironment features in resected hepatocellular carcinoma (HCC).MethodsSeventy-two patients were prospectively included for preoperative magnetic resonance (MR) diffusion-weighted imaging and MR elastography examination. The mean apparent diffusion coefficient (ADC) and stiffness value were measured on the central three slices of the tumor and peri-tumor area. Cell density, tumor-stroma ratio (TSR), lymphocyte-rich HCC (LR-HCC), and CD8 + T cell infiltration were estimated in resected tumors. The interobserver agreement of MRI measurements and subjective pathological evaluation was assessed. Variables influencing ADC and stiffness were screened with univariate analyses, and then identified with multivariable linear regression. The potential relationship between explored imaging biomarkers and histopathological features was assessed with linear regression after adjustment for other influencing factors.ResultsSeventy-two patients (male/female: 59/13, mean age: 56 ± 10.2 years) were included for analysis. Inter-reader agreement was good or excellent regarding MRI measurements and histopathological evaluation. No correlation between tumor ADC and tumor stiffness was found. Multivariable linear regression confirmed that cell density was the only factor associated with tumor ADC (Estimate = −0.03, p = 0.006), and tumor-stroma ratio was the only factor associated with tumor stiffness (Estimate = −0.18, p = 0.03). After adjustment for fibrosis stage (Estimate = 0.43, p < 0.001) and age (Estimate = 0.04, p < 0.001) in the multivariate linear regression, intra-tumoral CD8 + T cell infiltration remained a significant factor associated with peri-tumor stiffness (Estimate = 0.63, p = 0.02).ConclusionsTumor ADC surpasses tumor stiffness as a biomarker of cellularity. Tumor stiffness is associated with tumor-stroma ratio and peri-tumor stiffness might be an imaging biomarker of intra-tumoral immune microenvironment.Clinical relevance statementTissue stiffness could potentially serve as an imaging biomarker of the intra-tumoral immune microenvironment of hepatocellular carcinoma and aid in patient selection for immunotherapy.Key PointsApparent diffusion coefficient reflects cellularity of hepatocellular carcinoma.Tumor stiffness reflects tumor-stroma ratio of hepatocellular carcinoma and is associated with tumor-infiltrating lymphocytes.Tumor and peri-tumor stiffness might serve as imaging biomarkers of intra-tumoral immune microenvironment.

The Relationship between Breast Cancer Subtypes, Prognostic Factors, and Apparent Diffusion Coefficient Histogram Analysis.

Diffusion Magnetic Resonance Imaging (MRI) is a useful method to evaluate tumor biology and tumor microstructure. The apparent diffusion coefficient (ADC) value correlates negatively with the cellular density of the tumor. This study aimed to investigate the effectiveness of the ADC histogram analysis in showing the relationship between breast cancer prognostic factors and ADC parameters. This study is a retrospective observational descriptive study. ADC histogram parameters were evaluated in all tumor volumes of 67 breast cancer patients. Minimum, 5, 10, 25, 50, 75, 90, 95 percentiles, maximum, mean, median ADC values, kurtosis, and skewness were calculated. Breast MRI examinations were performed on a 3T MR scanner. We evaluated the fibroglandular tissue density of bilateral breasts, background enhancement, localization of masses, multifocality-multicentricity, shape, rim, internal contrast enhancement, and kinetic curve on breast MRI. BIRADS scoring was performed according to breast MRI. Pathologically, histologic type, histologic grade, HER 2, Ki 67, ER-, and PR status were evaluated. A significant correlation was found between tumor volume and ADC scores. There is a significant correlation between min ADC values (p< 0.031), max ADC (p< 0.001), and skewness (p< 0.019). A significant correlation was found between tumor kurtosis and lymph nodes (p< 0.029). There was a significant difference in ADC <sub<mean</sub<, ADC<sub<10%</sub<, ADC<sub<25%</sub<, ADC<sub<50%</sub<, ADC<sub<75%</sub<, ADC<sub<90%</sub<, ADC <sub<95%</sub< and ADC<sub<max</sub> values depending on ER-and PRstatus. (for ER p = 0.004, p = 0.018, p = 0.010, p = 0.008, p = 0.004, p = 0.004, p = 0.02, p = 0.02 and p = 0.038, for PR p &#60; 0.001, p = 0.028, p = 0.011, p = 0.001, p < 0.001, p =<0.001, p < 0.001, and p < 0.001, respectively; p < 0.05). These values were lower in ER-and PR-positive status than in ER-and PR-negative receptor status. According to HER2 status, there was a statistically significant difference in ADC<sub<5%</sub< and measurements of the lesions (p = 0.041; p < 0.05). Our study found no significant correlation between other prognostic factors, such as histological grade, Ki-67 indices, and ADC values. Our study found a significant difference between tumor volume, ER- and, PR status, HER2, and lymph node involvement, and some ADC values among prognostic factors for breast cancer. Furthermore, ADC histogram analysis can provide additional value in predicting some prognostic factors.