Prospective comparison of restriction spectrum imaging and non-invasive biomarkers to predict upgrading on active surveillance prostate biopsy

Multiparametric 18F-FDG PET/MRI based on restrictive spectrum imaging and amide proton transfer-weighted imaging facilitates the assessment of lymph node metastases in non-small cell lung cancer

BACKGROUND

To investigate the value of multiparametric 18F-FDG PET/MRI based on tri-compartmental restrictive spectrum imaging (RSI), amide proton transfer-weighted imaging (APTWI), and diffusion-weighted imaging (DWI) in the assessment of lymph node metastasis (LNM) of non-small cell lung cancer (NSCLC).

METHODS

A total of 152 patients (LNM-positive, 86 cases; LNM-negative, 66 cases) with NSCLC underwent chest multiparametric 18F-FDG PET/MRI were enrolled. 18F-FDG PET- derived parameter (SUVmax), RSI-derived parameters (f1, f2, and f3), APTWI-derived parameter (MTRasym(3.5 ppm)), DWI-derived parameter (ADC), and were calculated and compared. Logistic regression analysis was used to identify independent predictors, and combined diagnostics. Area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA) were employed to assess the performance of the combined diagnostics.

RESULTS

MTRasym(3.5 ppm), SUVmax, f2, and f3 were higher and ADC and f1 were lower in LNM-positive group than in LNM-negative group (all P < 0.05). Maximum lesion diameter, f1, MTRasym(3.5 ppm), SUVmax, and ADC were independent predictors of LNM status in NSCLC patients, and the combination of them had an optimal diagnostic efficacy (AUC = 0.978; sensitivity = 95.35%; specificity = 90.91%), which was significantly higher than maximum lesion diameter, f1, MTRasym(3.5 ppm), SUVmax, and ADC (AUC = 0.774, 0.810, 0.832, 0.834, and 0.783, respectively, and all P < 0.01). The combined diagnosis showed a good performance (AUC = 0.968) in the bootstrap (1000 samples)-based internal validation. Calibration curves and DCA demonstrated that the combined diagnosis not only provided better stability, but also resulted in a higher net benefit for the patients involved.

CONCLUSION

Multiparametric 18F-FDG PET/MRI based on RSI, APTWI, and DWI is beneficial for the non-invasive assessment of LNM status in NSCLC.

Histogram analysis of continuous-time random walk and restrictive spectrum imaging for identifying hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Background

To compare the ability and potential additional value of various diffusion models, including continuous-time random walk (CTRW), restrictive spectrum imaging (RSI), and diffusion-weighted imaging (DWI), as well as their associated histograms, in distinguishing the pathological subtypes of liver cancer.

Methods

40 patients with liver cancer were included in this study. Histogram metrics were derived from CTRW (D, α, β), RSI (f1, f2, f3), and DWI (ADC) parameters across the entire tumor volume. Statistical analyses included the Chi-square test, independent samples t-test, Mann-Whitney U test, ROC, logistic regression, and Spearman correlation.

Results

Patients with hepatocellular carcinoma exhibited higher values in f1 median, f1 20th, f1 40th, and f1 60th compared to patients with intrahepatic cholangiocarcinoma, whereas Dmean, Dmedian, D40th, D60th, and D80th percentiles were lower (P<0.05). Among the individual histogram parameters, f1 40th percentile demonstrated the highest accuracy (AUC = 0.717). Regarding the combined and single models, the total combined model exhibited the best diagnostic performance (AUC = 0.792). Although RSI showed higher diagnostic efficacy than CTRW (AUC = 0.731, 0.717), the combination of CTRW and RSI further improved diagnostic performance (AUC = 0.787), achieving superior sensitivity and specificity (sensitivity = 0.72, specificity = 0.80).

Conclusion

CTRW, RSI, and their corresponding histogram parameters demonstrated the ability to distinguish between pathological subtypes of liver cancer. Moreover, whole-lesion histogram parameters provided more comprehensive statistical insights compared to mean values alone.

Evaluating 4Kscore's role in predicting progression on active surveillance for prostate cancer independently of clinical information and PIRADS score

BACKGROUND

4Kscore is used to aid the decision for prostate biopsy, however its role in active surveillance (AS) has not been investigated in a magnetic resonance imaging (MRI)-based protocol. Our objective was to assess the association between 4Kscore and progression in men undergoing AS on a prospective MRI-based protocol.

METHODS

This was a single-institution, single-arm, non-therapeutic, interventional trial of 166 men with biopsy-confirmed prostate cancer enrolled between 2014-2020. Patients were placed on a trial-mandated AS protocol including yearly multiparametric (mp)MRI, prostate biopsy, and 4Kscore followed for 48 months after diagnosis. We analyzed protocol-defined and grade progression at confirmatory and subsequent surveillance biopsies.

RESULTS

Out of 166 patients, 83 (50%) men progressed per protocol and of them 41 (24.7% of whole cohort) progressed by grade. At confirmatory biopsy, men with a baseline 4Kscore ≥ 20% had a higher risk of grade progression compared to those with 4Kscore < 20% (OR = 4.04, 95% CI: 1.05-15.59, p = 0.043) after adjusting for National Comprehensive Cancer Network (NCCN) risk and baseline PIRADS score. At surveillance biopsies, most recent 4Kscore ≥ 20% significantly predicted per protocol (OR = 2.61, 95% CI: 1.03-6.63, p = 0.044) and grade progression (OR = 5.13, 95% CI: 1.63-16.11, p = 0.005).

CONCLUSIONS

For patients on AS, baseline 4Kscore predicted grade progression at confirmatory biopsy, and most recent 4Kscore predicted per-protocol and grade progression at surveillance biopsy.

Restriction Spectrum Imaging and Diffusion Kurtosis Imaging for Assessing Proliferation Status in Rectal Carcinoma

OBJECTIVES

To investigate the application of the three-compartment restriction spectrum imaging (RSI) model, diffusion kurtosis imaging (DKI), and diffusion-weighted imaging (DWI) in predicting Ki-67 status in rectal carcinoma.

METHODS

A total of 80 rectal carcinoma patients, including 47 high-proliferation (Ki-67 > 50%) cases and 33 low-proliferation (Ki-67 ≤ 50%) cases, underwent pelvic MRI were enrolled. Parameters derived from RSI (f1, f2, and f3), DKI (MD and MK), and DWI (ADC) were calculated and compared between the two groups. Logistic regression (LR) analysis was conducted to identify independent predictors and assess combined diagnosis. Area under the receiver operating characteristic curve (AUC), DeLong analysis, and calibration curve analyses were performed to evaluate diagnostic performance.

RESULTS

The patients with high-proliferation rectal carcinoma exhibited significantly higher f1 and MK values and significantly lower ADC, MD, f2, and f3 values than those with low-proliferation rectal carcinoma (P < 0.05). LR analysis showed that MD, MK, and f2 were independent predictors for Ki-67 status in rectal carcinoma. Moreover, the combination of these three parameters achieved an optimal diagnostic efficacy (AUC = 0.877, sensitivity = 80.85%, specificity = 84.85%) that was significantly better than that obtained using ADC (AUC = 0.783, Z = 2.347, P = 0.019), f2 (AUC = 0.732, Z = 2.762, P = 0.006), and f3 (AUC = 0.700, Z = 3.071, P = 0.002). The combined diagnosis also showed good performance (AUC = 0.859) in the internal validation analysis based on 1000 bootstrap samples, while the calibration curve demonstrated that the combined diagnosis provided good stability.

CONCLUSION

RSI, DKI, and DWI can effectively differentiate between patients with high- and low-proliferation rectal carcinoma. Furthermore, the MD, MK, and f2 imaging parameters may be a novel and promising combination biomarker for examining Ki-67 status in rectal carcinoma.

The value of restriction spectrum imaging in predicting lymph node metastases in rectal cancer: a comparative study with diffusion-weighted imaging and diffusion kurtosis imaging

BACKGROUND

To investigate the efficacy of three-compartment restriction spectrum imaging (RSI), diffusion kurtosis imaging (DKI), and diffusion-weighted imaging (DWI) in the assessment of lymph node metastases (LNM) in rectal cancer.

METHODS

A total of 77 patients with rectal cancer who underwent pelvic MRI were enrolled. RSI-derived parameters (f1, f2, and f3), DKI-derived parameters (Dapp and Kapp), and the DWI-derived parameter (ADC) were calculated and compared using a Mann-Whitney U test or independent samples t-test. Logistic regression (LR) analysis was used to identify independent predictors of LNM status. Area under the receiver operating characteristic curve (AUC) and Delong analysis were performed to assess the diagnostic performance of each parameter.

RESULTS

The LNM-positive group exhibited significantly higher f1 and Kapp levels and significantly lower f3, Dapp, and ADC levels compared to the LNM-negative group (p < 0.05). There was no difference in f2 levels between the two groups (p = 0.783). LR analysis showed that Dapp and Kapp were independent predictors of a positive LNM status. AUC and Delong analysis showed that DKI (Dapp + Kapp) exhibited significantly higher diagnostic efficacy (AUC = 0.908; sensitivity = 87.10%; specificity = 86.96%) than RSI (f1 + f3) and DWI (ADC), with AUCs were 0.842 and 0.771 (Z = 2.113, 3.453; p = 0.035, < 0.001, respectively). The AUC performance between RSI and DWI was also statistically significant (Z = 1.972, p = 0.049).

CONCLUSION

The RSI model is superior to conventional DWI but inferior to DKI in differentiation between LNM-positive and LNM-negative rectal cancers. Further study is needed before it could serve as a promising biomarker for guiding effective treatment strategies.

CRITICAL RELEVANCE STATEMENT

The three-compartment restriction spectrum imaging was able to differentiate between LNM-positive and LNM-negative rectal cancers with high accuracy, which has the potential to serve as a promising biomarker that could guide treatment strategies.

KEY POINTS

Three-compartment restriction spectrum imaging could differentiate lymph node metastases in rectal cancer. Diffusion kurtosis imaging and diffusion-weighted were associated with lymph node metastases in rectal cancer. The combination of different parameters has the potential to serve as a promising biomarker.

Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions

PURPOSE OF REVIEW

In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC.

RECENT FINDINGS

In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy.

SUMMARY

The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.

A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model

Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer.

SIGNIFICANCE

Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.

Recent advances and future perspectives in the therapeutics of prostate cancer

Prostate cancer (PC) is one of the most common cancers in males and the fifth leading reason of death. Age, ethnicity, family history, and genetic defects are major factors that determine the aggressiveness and lethality of PC. The African population is at the highest risk of developing high-grade PC. It can be challenging to distinguish between low-risk and high-risk patients due to the slow progression of PC. Prostate-specific antigen (PSA) is a revolutionary discovery for the identification of PC. However, it has led to an increase in over diagnosis and over treatment of PC in the past few decades. Even if modifications are made to the standard PSA testing, the specificity has not been found to be significant. Our understanding of PC genetics and proteomics has improved due to advances in different fields. New serum, urine, and tissue biomarkers, such as PC antigen 3 (PCA3), have led to various new diagnostic tests, such as the prostate health index, 4K score, and PCA3. These tests significantly reduce the number of unnecessary and repeat biopsies performed. Chemotherapy, radiotherapy, and prostatectomy are standard treatment options. However, newer novel hormone therapy drugs with a better response have been identified. Androgen deprivation and hormonal therapy are evolving as new and better options for managing hormone-sensitive and castration-resistant PC. This review aimed to highlight and discuss epidemiology, various risk factors, and developments in PC diagnosis and treatment regimens.