Detecting prostate cancer and prostatic calcifications using advanced magnetic resonance imaging

A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients

OBJECTIVE

Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of US calcifications in predicting the risk of lymph node metastasis (LNM) in papillary thyroid cancer (PTC).

METHODS

Based on the DeepLabv3+ networks, 2992 thyroid nodules in US images were used to train a model to detect thyroid nodules, of which 998 were used to train a model to detect and quantify calcifications. A total of 225 and 146 thyroid nodules obtained from two centers, respectively, were used to test the performance of these models. A logistic regression method was used to construct the predictive models for LNM in PTCs.

RESULTS

Calcifications detected by the network model and experienced radiologists had an agreement degree of above 90%. The novel quantitative parameters of US calcification defined in this study showed a significant difference between PTC patients with and without cervical LNM (p < 0.05). The calcification parameters were beneficial to predicting the LNM risk in PTC patients. The LNM prediction model using these calcification parameters combined with patient age and other US nodular features showed a higher specificity and accuracy than the calcification parameters alone.

CONCLUSIONS

Our models not only detect the calcifications automatically, but also have value in predicting cervical LNM risk of PTC patients, thereby making it possible to investigate the relationship between calcifications and highly invasive PTC in detail.

CLINICAL RELEVANCE STATEMENT

Due to the high association of US microcalcifications with thyroid cancers, our model will contribute to the differential diagnosis of thyroid nodules in daily practice.

KEY POINTS

• We developed an ML-based network model for automatically detecting and quantifying calcifications within thyroid nodules in US images. • Three novel parameters for quantifying US calcifications were defined and verified. • These US calcification parameters showed value in predicting the risk of cervical LNM in PTC patients.

Analysis of Apparent Diffusion Coefficient Value and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameters of Prostate Cancer Patients after Diagnosis and Treatment with Magnetic Resonance Imaging

This research was aimed at exploring the changes in the apparent diffusion coefficient (ADC) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters of prostate cancer (PCa) patients. Sixty PCa patients from the hospital were recruited as the research object, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans were performed to determine the shape, scope, and enhancement characteristics of prostate lesions and their relationship with surrounding tissues. The quantitative parameters of ADC and DCE-MRI were measured. There were 4 patients (6.67%) with a Gleason score of 6 and 15 patients (25%) with a 4 + 3 score. The ADC with Gleason = 6 is 0.81 ± 0.08 × 10⁻³ s/mm², the ADC with Gleason = 3 + 4 is 0.74 ± 0.07 × 10⁻³ s/mm², the ADC with Gleason = 4 + 3 is 0.73 ± 0.05 × 10⁻³ s/mm², the ADC with Gleason = 9 is 0.65 ± 0.06 × 10⁻³ s/mm², and the ADC with Gleason = 10 is 0.59 ± 0.07 × 10⁻³ s/mm². As the Gleason score increased, the ADC decreased and the permeation parameter transfer constant increased. When the ADC was combined with the permeability parameter transfer constant, the AUC of Gleason = 6 points and Gleason = 7 points was greatly different (P < 0.05). The 95% CI of the ADC combined permeability parameter transport constant when Gleason = 6 points and Gleason = 7 points was 0.898-0.934, the sensitivity was 75.4%, and the specificity was 86.2%. The ADC value was negatively correlated with Gleason score. The ADC value combined with VTC value has good diagnostic performance in evaluating the invasion of PCa, which is very important for making treatment plan and evaluating prognosis.

Based on biomedical index data: Risk prediction model for prostate cancer

ABSTRACT

To explore the influencing factors of prostate cancer occurrence, set up risk prediction model, require reference for the preliminary diagnosis of clinical doctors, this model searched database through the data of prostate cancer patients and prostate hyperplasia patients National Clinical Medical Science Data Center.With the help of Stata SE 12.0 and SPSS 25.0 software, the biases between groups were balanced by propensity score matching. Based on the matched data, the relevant factors were further screened by stepwise logistic regression analysis, the key variable and artificial neural network model are established. The prediction accuracy of the model is evaluated by combining the probability of test set with the area under receiver operating characteristic curve (ROC).After 1:2 PSM, 339 pairs were matched successfully. There are 159 cases in testing groups and 407 cases in training groups. And the regression model was P = 1 / (1 + e (0.122 ∗ age + 0.083 ∗ Apo lipoprotein C3 + 0.371 ∗ total prostate specific antigen (tPSA) -0.227 ∗ Apo lipoprotein C2-6.093 ∗ free calcium (iCa) + 0.428 ∗ Apo lipoprotein E-1.246 ∗ triglyceride-1.919 ∗ HDL cholesterol + 0.083 ∗ creatine kinase isoenzyme [CKMB])). The logistic regression model performed very well (ROC, 0.963; 95% confidence interval, 0.951 to 0.978) and artificial neural network model (ROC, 0.983; 95% confidence interval, 0.964 to 0.997). High degree of Apo lipoprotein E (Apo E) (Odds Ratio, [OR], 1.535) in blood test is a risk factor and high triglyceride (TG) (OR, 0.288) is a protective factor.It takes the biochemical examination of the case as variables to establish a risk prediction model, which can initially reflect the risk of prostate cancer and bring some references for diagnosis and treatment.

Value of High-resolution MRI in Detecting Lymph Node Calcifications in Patients with Rectal Cancer

RATIONALE AND OBJECTIVES

To analyze CT and high-resolution MRI findings of nodal metastasis calcifications and determine the value of high-resolution MRI in detecting nodal calcifications in rectal cancer patients.

MATERIALS AND METHODS

In total, 229 rectal cancer patients were included. The CT was reviewed for the presence of nodal calcifications by two radiologists. High-resolution two-dimensional turbo spin-echo T2-weighted imaging (2D-TSE-T2WI) and fat-suppressed gadolinium-enhanced isotropic high-resolution three-dimensional gradient-echo T1-weighted imaging (3D-GRE-T1WI) were independently reviewed for nodal calcifications by the two radiologists at one-month and two-month intervals, respectively. The sensitivities, specificities and accuracies of the two high-resolution MRI in detecting nodal calcifications were calculated using CT results as a reference.

RESULTS

Regional calcified metastatic lymph nodes were found in 28 patients. The node-to-node evaluation revealed that 55 (98.2%) of the 56 calcified lymph nodes were metastatic. Fifty-one (92.7%) calcified metastatic lymph nodes displayed scattered fine punctate calcifications to different degrees on CT. In both types of high-resolution MRI, the calcifications demonstrated a patchy area of markedly reduced signal intensity in corresponding areas that were larger than those on CT. The sensitivity and accuracy of fat-suppressed gadolinium-enhanced isotropic high-resolution 3D-GRE-T1WI were significantly higher than those of high-resolution 2D-TSE-T2WI (76.8% vs 58.9%, P = 0.013; 98.3% vs 97.9%, P = 0.007; respectively).

CONCLUSION

Metastatic nodal calcifications are characteristic imaging findings in rectal cancer. Calcifications are indicated by markedly reduced signal on high-resolution MRI, which will alert radiologists to scrutinize CT for nodal calcifications and aid in the accurate diagnosis of metastatic lymph nodes.

PEG-coated and Gd-loaded fluorescent silica nanoparticles for targeted prostate cancer magnetic resonance imaging and fluorescence imaging

Background: Multimodal imaging probes have become a powerful tool for improving detection sensitivity and accuracy, which are important in disease diagnosis and treatment.

Methods: In this study, novel bifunctional magnetic resonance imaging (MRI)/fluorescence probes were prepared by loading gadodiamide into fluorescent silica nanoparticles (NPs) (Gd@Cy5.5@SiO₂-PEG-Ab NPs) for targeting of prostate cancer (PCa). The physicochemical characteristics, biosafety and PCa cell targeting ability of the Gd@Cy5.5@SiO₂-PEG-Ab NPs were studied in vitro and in vivo.

Results: The Gd@Cy5.5@SiO₂-PEG-Ab NPs had a spherical morphology with a relatively uniform size distribution and demonstrated high efficiency for Gd loading. In vitro and in vivo cell-targeting experiments demonstrated a high potential for the synthesized NPs to target prostate-specific membrane antigen (PSMA) receptor-positive PCa cells, enabling MRI and fluorescence imaging. In vitro cytotoxicity assays and in vivo hematological and pathological assays showed that the prepared NPs exhibited good biological safety.

Conclusion: Our study demonstrates that the synthesized Gd@Cy5.5@SiO₂-PEG-Ab NPs have great potential as MRI/fluorescence contrast agents for specific identification of PSMA receptor-positive PCa cells.

Combined analysis of CRMP4 methylation levels and CAPRA-S score predicts metastasis and outcomes in prostate cancer patients

The present study analyzed the predictive value of combined analysis of collapsin response mediator protein 4 (CRMP4) methylation levels and the Cancer of the Prostate Risk Assessment (CAPRA-S) Postsurgical score of patients who required adjuvant hormone therapy (AHT) after radical prostatectomy (RP). We retrospectively analyzed 305 patients with prostate cancer (PCa) who received RP and subsequent androgen deprivation therapy (ADT). Two hundred and thirty patients with clinically high-risk PCa underwent immediate ADT, and 75 patients with intermediate risk PCa underwent deferred ADT. CRMP4 methylation levels in biopsies were determined, and CAPRA-S scores were calculated. In the deferred ADT group, the values of the hazard ratios for tumor progression and cancer-specific mortality (CSM) in patients with ≥15% CRMP4 methylation were 6.81 (95% CI: 2.34–19.80) and 12.83 (95% CI: 2.16–26.10), respectively. Receiver-operating characteristic curve analysis indicated that CRMP4 methylation levels ≥15% served as a significant prognostic marker of tumor progression and CSM. In the immediate ADT group, CAPRA-S scores ≥6 and CRMP4 methylation levels ≥15% were independent predictors of these outcomes (uni- and multi-variable Cox regression analyses). The differences in the 5-year progression-free survival between each combination were statistically significant. Combining CAPRA-S score and CRMP4 methylation levels improved the area under the curve compared with the CRMP4 or CAPRA-S model. Therefore, CRMP4 methylation levels ≥15% were significantly associated with a poor prognosis and their combination with CAPRA-S score accurately predicted tumor progression and metastasis for patients requiring AHT after RP.

Diagnostic performance of susceptibility-weighted magnetic resonance imaging for the detection of calcifications: A systematic review and meta-analysis

Since its introduction, susceptibility-weighted-magnetic resonance imaging (SW-MRI) has shown the potential to overcome the insensitivity of MRI to calcification. Previous studies reporting the diagnostic performance of SW-MRI and magnetic resonance imaging (MRI) for the detection of calcifications are inconsistent and based on single-institution designs. To our knowledge, this is the first meta-analysis on SW-MRI, determining the potential of SW-MRI to detect calcifications. Two independent investigators searched MEDLINE, EMBASE and Web of Science for eligible diagnostic accuracy studies, which were published until March 24, 2017 and investigated the accuracy of SW-MRI to detect calcifications, using computed tomography (CT) as a reference. The QUADAS-2 tool was used to assess study quality and methods for analysis were based on PRISMA. A bivariate diagnostic random-effects model was applied to obtain pooled sensitivities and specificities. Out of the 4629 studies retrieved by systematic literature search, 12 clinical studies with 962 patients and a total of 1,032 calcifications were included. Pooled sensitivity was 86.5% (95%-confidence interval (CI): 73.6–93.7%) for SW-MRI and 36.7% (95%–CI:29.2–44.8%) for standard MRI. Pooled specificities of SW-MRI (90.8%; 95%–CI:81.0–95.8%) and standard MRI (94.2; 95%–CI:88.9–96.7%) were comparable. Results of the present meta-analysis suggest, that SW-MRI is a reliable method for detecting calcifications in soft tissues.