Combined application of pharamcokinetic DCE-MRI and IVIM-DWI could improve detection efficiency in early diagnosis of ductal carcinoma in situ

Looking through the imaging perspective: the importance of imaging necrosis in glioma diagnosis and prognostic prediction - single centre experience

BACKGROUND

The aim of the study was to investigate the diagnostic value of imaging necrosis (Imnecrosis) in grading, predict the genotype and prognosis of gliomas, and further assess tumor necrosis by dynamic contrast-enhanced MR perfusion imaging (DCE-MRI).

PATIENTS AND METHODS

We retrospectively included 150 patients (104 males, mean age: 46 years old) pathologically proved as adult diffuse gliomas and all diagnosis was based on the 2021 WHO central nervous system (CNS) classification. The pathological necrosis (Panecrosis) and gene mutation information were collected. All patients underwent conventional and DCE-MRI examinations and had been followed until May 31, 2021. The Imnecrosis was determined by two experienced neuroradiologists. DCE-MRI derived metric maps have been post-processed, and the mean value of each metric in the tumor parenchyma, peritumoral and contralateral area were recorded.

RESULTS

There was a strong degree of inter-observer agreement in defining Imnecrosis (Kappa = 0.668, p < 0.001) and a strong degree of agreement between Imnecrosis and Panecrosis (Kappa = 0.767, p < 0.001). Compared to low-grade gliomas, high-grade gliomas had more Imnecrosis (85.37%, p < 0.001), and Imnecrosis significantly increased with the grade of gliomas increasing. And Imnecrosis was significantly more identified in IDH-wildtype, 1p19q-non-codeletion, and CDKN2A/B-homozygous-deletion gliomas. Using multivariate Cox regression analysis, Imnecrosis was an independent and unfavorable prognosis factor (Hazard Ratio = 2.113, p = 0.046) in gliomas. Additionally, extravascular extracellular volume fraction (ve) in tumor parenchyma derived from DCE-MRI demonstrated the highest diagnostic efficiency in identifying Panecrosis and Imnecrosis with high specificity (83.3% and 91.9%, respectively).

CONCLUSIONS

Imnecrosis can provide supplementary evidence beyond Panecrosis in grading, predicting the genotype and prognosis of gliomas, and ve in tumor parenchyma can help to predict tumor necrosis with high specificity.

Diagnostic accuracy of intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) MRI to differentiate benign from malignant breast lesions: A systematic review and meta-analysis

PURPOSE

Magnetic resonance imaging (MRI) can reduce the need for unnecessary invasive diagnostic tests by nearly half. In this meta-analysis, we investigated the diagnostic accuracy of intravoxel incoherent motion modeling (IVIM) and dynamic contrast-enhanced (DCE) MRI in differentiating benign from malignant breast lesions.

METHOD

We systematically searched PubMed, EMBASE, and Scopus. We included English articles reporting diagnostic accuracy for both sequences in differentiating benign from malignant breast lesions. Articles were assessed by quality assessment of diagnostic accuracy studies-2 (QUADAS-2) questionnaire. We used a bivariate effects model for standardized mean difference (SMD) analysis and diagnostic test accuracy analysis.

RESULTS

Ten studies with 537 patients and 707 (435 malignant and 272 benign) lesions were included. The D, f, Ktrans, and Kep mean values significantly differ between benign and malignant lesions. The pooled sensitivity (95 % confidence interval) and specificity were 86.2 % (77.9 %-91.7 %) and 70.3 % (56.5 %-81.1 %) for IVIM, and 93.8 % (85.3 %-97.5 %) and 68.1 % (52.7 %-80.4 %) for DCE, respectively. Combined IVIM and DCE depicted the highest area under the curve of 0.94, with a sensitivity and specificity of 91.8 % (82.8 %-96.3 %) and 87.6 % (73.8 %-94.7 %), respectively.

CONCLUSIONS

Combined IVIM and DCE had the highest diagnostic accuracy, and multiparametric MRI may help reduce unnecessary benign breast biopsy.

Predictive value of DCE-MRI and IVIM-DWI in osteosarcoma patients with neoadjuvant chemotherapy

Objective

To investigate the predictive value of dynamic contrast enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for clinical outcomes of osteosarcoma patients with neoadjuvant chemotherapy.

Methods

The present prospective single-arm cohort study enrolled 163 patients of osteosarcoma during July 2017 to July 2022. All patients received the same treatment strategy of neoadjuvant chemotherapy. Both DCE-MRI and IVIM-DWI were conducted for the patients before the chemotherapy, as well as after one or two chemotherapy treatment cycles. The imaging parameters of contrast agent transfer rate between blood and tissue (K^trans), contrast agent back-flux rate constant (K_ep), extravascular extracellular fractional volume (V_e), as well as pure diffusion coefficient (D value), pseudo-diffusion coefficient (D* value), apparent diffusion coefficient (ADC) and the perfusion fraction (f value) were recorded. RECIST standard [complete response (CR), partial response (PR), stable disease (SD), progressive disease (PD)] was used as the main clinical outcome.

Results

After two treatment cycles, 112 (68.71%) cases were with CR and PR, 31 (19.02%) cases were with SD and 20 cases (12.27%) were with PD. After 1~2 treatment cycles, patients with CR/PR showed significantly markedly lower K^trans, K_ep, V_e values, while higher D, ADC and f values compared with SD or PD patients. Alkaline phosphatase (ALP) and lactate dehydrogenase (LDH) were positively correlated with values of K^trans, K_ep, and V_e, while negative correlation was observed between ALP and values of D, ADC and f, as well as between LDH and D and ADC after the whole treatment. D and K_ep values after two treatment cycles showed the best predictive value for diagnosis of PD. The values of K^tran, K_ep, ADC as well as ALP and LDH were all risk factors for PD after neoadjuvant chemotherapy.

Conclusion

DCE-MRI and IVIM-DWI have the potential to predict clinical outcomes of osteosarcoma patients with neoadjuvant chemotherapy.

Intravoxel incoherent motion to differentiate spinal metastasis: A pilot study

Background

To investigate the value of intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) to discriminate spinal metastasis from tuberculous spondylitis.

Methods

This study included 50 patients with spinal metastasis (32 lung cancer, 7 breast cancer, 11 renal cancer), and 20 with tuberculous spondylitis. The IVIM parameters, including the single-index model (apparent diffusion coefficient (ADC)-stand), double exponential model (ADCslow, ADCfast, and f), and the stretched-exponential model parameters (distributed diffusion coefficient (DDC) and α), were acquired. Receiver operating characteristic (ROC) and the area under the ROC curve (AUC) analysis was used to evaluate the diagnostic performance. Each parameter was substituted into a logistic regression model to determine the meaningful parameters, and the combined diagnostic performance was evaluated.

Results

The ADCfast and f showed significant differences between spinal metastasis and tuberculous spondylitis (all p &lt; 0.05). The logistic regression model results showed that ADCfast and f were independent factors affecting the outcome (P &lt; 0.05). The AUC values of ADCfast and f were 0.823 (95% confidence interval (CI): 0.719 to 0.927) and 0.876 (95%CI: 0.782 to 0.969), respectively. ADCfast combined with f showed the highest AUC value of 0.925 (95% CI: 0.858 to 0.992).

Conclusions

IVIM MR imaging might be helpful to differentiate spinal metastasis from tuberculous spondylitis, and provide guidance for clinical treatment.

Standard diffusion-weighted, intravoxel incoherent motion, and dynamic contrast-enhanced MRI of musculoskeletal tumours: correlations with Ki67 proliferation status

AIM

To determine whether quantitative parameters derived from conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) correlate with the Ki67 proliferation status in musculoskeletal tumours.

MATERIALS AND METHODS

Twenty-eight patients with musculoskeletal tumours diagnosed via surgical specimen histological analysis who underwent standard DWI, IVIM, and DCE were reviewed retrospectively. The mean standard DWI (apparent diffusion coefficient [ADC]), IVIM (pure diffusion coefficient [D], pseudo-diffusion coefficient [D∗] and perfusion fraction [ƒ]), and DCE (volume transfer constant [K^trans], rate constant [K_ep], and extravascular extracellular volume fraction [V_e]) parameters were measured and correlated with the Ki67 index. The Ki67 value was categorised as high (>20%) or low (≤20%).

RESULTS

The ADC and D values correlated negatively with the Ki67 index (r=-0.711∼-0.699, p<0.001), whereas the K^trans and K_ep values correlated positively with the Ki67 index (r=0.389-0.434, p=0.021, 0.041). The ADC and D values were lower (p<0.001), whereas the K^trans and K_ep values were higher (p=0.011, 0.005) in musculoskeletal tumours with a high Ki67 status than in those in a low status. The ADC and D demonstrated the largest area under the receiver-operating characteristic curve (AUC = 0.953), which is statistically bigger than the AUC of K^trans and K_ep (0.784 and 0.802, respectively).

CONCLUSION

ADC, D, K^trans, and K_ep correlate with the Ki67 index. ADC and D are the strongest quantitative parameters for predicting Ki67 status.

Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: A systematic review and meta-analysis

PURPOSE

We sought to evaluate the diagnostic performance of diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant breast tumors by performing a meta-analysis.

METHODS

We comprehensively searched the electronic databases PubMed and Embase from January 2000 to April 2020 for studies in English. Studies were included if they reported the sensitivity and specificity for identifying benign and malignant breast lesions using DWI or IVIM. Studies were reviewed according to QUADAS-2. The data inhomogeneity and publication bias were also assessed. In order to explore the influence of different field strengths and different b values on diagnostic efficiency, we conducted subgroup analysis.

RESULTS

We analyzed 79 studies, which included a total of 6294 patients with 4091 malignant lesions and 2793 benign lesions. Overall, the pooled sensitivity and specificity of ADC for detecting malignant breast tumors were 0.87 (0.86-0.88) and 0.80 (0.78-0.81), respectively. The PLR was 5.09 (4.16-6.24); the NLR was 0.15 (0.13-0.18); and the DOR was 38.95 (28.87-52.54). The AUC value was 0.9297. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity and specificity was 0.85 (0.82-0.88) and 0.87(0.83-0.90), respectively; the PLR was 5.65 (3.91-8.18); the NLR was 0.17 (0.12-0.26); and the DOR was 38.44 (23.57-62.69). The AUC value was 0.9265. Most of parameters demonstrated considerable statistically significant heterogeneity (P < 0.05, I²>50 %) except the pooled DOR, PLR of D and the pooled DOR and NLR of D*.

CONCLUSIONS

Our meta-analysis indicated that DWI and IVIM had high sensitivity and specificity in the differential diagnosis of breast lesions; and compared with DWI, IVIM could not further increase the diagnostic performance. There was no significant difference in diagnostic accuracy.

Diagnosis of Cervical Cancer With Parametrial Invasion on Whole-Tumor Dynamic Contrast-Enhanced Magnetic Resonance Imaging Combined With Whole-Lesion Texture Analysis Based on T2- Weighted Images

Purpose: To evaluate the diagnostic value of the combination of whole-tumor dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and whole-lesion texture features based on T2-weighted images for cervical cancer with parametrial invasion. Materials and Methods: Sixty-two patients with cervical cancer (27 with parametrial invasion and 35 without invasion) preoperatively underwent routine MRI and DCE-MRI examinations. DCE-MRI parameters (K^trans, K_ep, and V_e) and texture features (mean, skewness, kurtosis, uniformity, energy, and entropy) based on T2-weighted images were acquired by two observers. All parameters of parametrial invasion and non-invasion were analyzed by one-way analysis of variance. The diagnostic efficiency of significant variables was assessed using receiver operating characteristic analysis. Results: The invasion group of cervical cancer demonstrated significantly higher K^trans (0.335 ± 0.050 vs. 0.269 ± 0.079; p < 0.001), lower energy values (0.503 ± 0.093 vs. 0.602 ± 0.087; p < 0.001), and higher entropy values (1.391 ± 0.193 vs. 1.24 ± 0.129; p < 0.001) than those in the non-invasion group. Optimal diagnostic performance [area under curve [AUC], 0.925; sensitivity, 0.935; specificity, 0.829] could be obtained by the combination of K^trans, energy, and entropy values. The AUC values of K^trans (0.788), energy (0.761), entropy (0.749), the combination of K^trans and energy (0.814), the combination of K^trans and entropy (0.727), and the combination of energy and entropy (0.619) were lower than those of the combination of K^trans, energy, and entropy values. Conclusion: The combination of DCE-MRI and texture analysis is a promising method for diagnosis cervical cancer with parametrial infiltration. Moreover, the combination of K^trans, energy, and entropy is more valuable than any one alone, especially in improving diagnostic sensitivity.

Double reading of diffusion-weighted magnetic resonance imaging for breast cancer detection

PURPOSE

To estimate the performance of diffusion-weighted imaging (DWI) for breast cancer detection.

METHODS

Consecutive breast magnetic resonance imaging examinations performed from January to September 2016 were retrospectively evaluated. Examinations performed before/after neoadjuvant therapy, lacking DWI sequences or reference standard were excluded; breasts after mastectomy were also excluded. Two experienced breast radiologists (R1, R2) independently evaluated only DWI. Final pathology or > 1-year follow-up served as reference standard. Mc Nemar, χ², and κ statistics were applied.

RESULTS

Of 1,131 examinations, 672 (59.4%) lacked DWI sequence, 41 (3.6%) had no reference standard, 30 (2.7%) were performed before/after neoadjuvant therapy, and 10 (0.9%) had undergone bilateral mastectomy. Thus, 378 women aged 49 ± 11 years (mean ± standard deviation) were included, 51 (13%) with unilateral mastectomy, totaling 705 breasts. Per-breast cancer prevalence was 96/705 (13.6%). Per-breast sensitivity was 83/96 (87%, 95% confidence interval 78-93%) for both R1 and R2, 89/96 (93%, 86-97%) for double reading (DR) (p = 0.031); per-lesion DR sensitivity for cancers ≤ 10 mm was 22/31 (71%, 52-86%). Per-breast specificity was 562/609 (93%, 90-94%) for R1, 538/609 (88%, 86-91%) for R2, and 526/609 (86%¸ 83-89%) for DR (p < 0.001). Inter-observer agreement was substantial (κ = 0.736). Acquisition time varied from 3:00 to 6:22 min:s. Per-patient median interpretation time was 46 s (R1) and 51 s (R2).

CONCLUSIONS

DR DWI showed a 93% sensitivity and 88% specificity, with 71% sensitivity for cancers ≤ 10 mm, pointing out a potential for DWI as stand-alone screening method.