MRI-based prostate cancer detection with high-level representation and hierarchical classification

Extracting the high-level feature representation by using deep neural networks for detection of prostate cancer, and then based on high-level feature representation constructing hierarchical classification to refine the detection results

In vivo MRI based prostate cancer localization with random forests and auto-context model

Prostate cancer is one of the major causes of cancer death for men. Magnetic resonance (MR) imaging is being increasingly used as an important modality to localize prostate cancer. Therefore, localizing prostate cancer in MRI with automated detection methods has become an active area of research. Many methods have been proposed for this task. However, most of previous methods focused on identifying cancer only in the peripheral zone (PZ), or classifying suspicious cancer ROIs into benign tissue and cancer tissue. Few works have been done on developing a fully automatic method for cancer localization in the entire prostate region, including central gland (CG) and transition zone (TZ). In this paper, we propose a novel learning-based multi-source integration framework to directly localize prostate cancer regions from in vivo MRI. We employ random forests to effectively integrate features from multi-source images together for cancer localization. Here, multi-source images include initially the multi-parametric MRIs (i.e., T2, DWI, and dADC) and later also the iteratively-estimated and refined tissue probability map of prostate cancer. Experimental results on 26 real patient data show that our method can accurately localize cancerous sections. The higher section-based evaluation (SBE), combined with the ROC analysis result of individual patients, shows that the proposed method is promising for in vivo MRI based prostate cancer localization, which can be used for guiding prostate biopsy, targeting the tumor in focal therapy planning, triage and follow-up of patients with active surveillance, as well as the decision making in treatment selection. The common ROC analysis with the AUC value of 0.832 and also the ROI-based ROC analysis with the AUC value of 0.883 both illustrate the effectiveness of our proposed method

Magnetic resonance imaging of benign prostatic hyperplasia

Benign prostatic hyperplasia (BPH) is a common condition in middle-aged and older men and negatively affects the quality of life. An ultrasound classification for BPH based on a previous pathologic classification was reported, and the types of BPH were classified according to different enlargement locations in the prostate. Afterwards, this classification was demonstrated using magnetic resonance imaging (MRI). The classification of BPH is important, as patients with different types of BPH can have different symptoms and treatment options. BPH types on MRI are as follows: type 0, an equal to or less than 25 cm3 prostate showing little or no zonal enlargements; type 1, bilateral transition zone (TZ) enlargement; type 2, retrourethral enlargement; type 3, bilateral TZ and retrourethral enlargement; type 4, pedunculated enlargement; type 5, pedunculated with bilateral TZ and/or retrourethral enlargement; type 6, subtrigonal or ectopic enlargement; type 7, other combinations of enlargements. We retrospectively evaluated MRI images of BPH patients who were histologically diagnosed and presented the different types of BPH on MRI. MRI, with its advantage of multiplanar imaging and superior soft tissue contrast resolution, can be used in BPH patients for differentiation of BPH from prostate cancer, estimation of zonal and entire prostatic volumes, determination of the stromal/glandular ratio, detection of the enlargement locations, and classification of BPH types which may be potentially helpful in choosing the optimal treatment

Prostate Volumes Derived From MRI and Volume-Adjusted Serum Prostate-Specific Antigen: Correlation With Gleason Score of Prostate Cancer

The purpose of this article is to study relationships between MRI-based prostate volume and volume-adjusted serum prostate-specific antigen (PSA) concentration estimates and prostate cancer Gleason score

Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI

Accurately measuring arterial input function (AIF) is essential for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). We used the indicator dilution principle to evaluate the accuracy of AIF measured directly from an artery following a low-dose contrast media ultrafast DCE-MRI. In total, 15 patients with biopsy-confirmed localized prostate cancers were recruited. Cardiac MRI (CMRI) and ultrafast DCE-MRI were acquired on a Philips 3 T Ingenia scanner. The AIF was measured at iliac arties following injection of a low-dose (0.015 mmol/kg) gadolinium (Gd) contrast media. The cardiac output (CO) from CMRI (COCMRI) was calculated from the difference in ventricular volume at diastole and systole measured on the short axis of heart. The CO from DCE-MRI (CODCE) was also calculated from the AIF and dose of the contrast media used. A correlation test and Bland–Altman plot were used to compare COCMRI and CODCE. The average (±standard deviation [SD]) area under the curve measured directly from local AIF was 0.219 ± 0.07 mM·min. The average (±SD) COCMRI and CODCE were 6.52 ± 1.47 L/min and 6.88 ± 1.64 L/min, respectively. There was a strong positive correlation (r = 0.82, P < .01) and good agreement between COCMRI and CODCE. The CODCE is consistent with the reference standard COCMRI. This indicates that the AIF can be measured accurately from an artery with ultrafast DCE-MRI following injection of a low-dose contrast media

Prostate Volumes Derived From MRI and Volume-Adjusted Serum Prostate-Specific Antigen: Correlation With Gleason Score of Prostate Cancer

OBJECTIVE: The purpose of this article is to study relationships between MRI-based prostate volume and volume-adjusted serum prostate-specific antigen (PSA) concentration estimates and prostate cancer Gleason score. MATERIALS AND METHODS: The study included 61 patients with prostate cancer (average age, 63.3 years; range 52–75 years) who underwent MRI before prostatectomy. A semiautomated and MRI-based technique was used to estimate total and central gland prostate volumes, central gland volume fraction (central gland volume divided by total prostate volume), PSA density (PSAD; PSA divided by total prostate volume), and PSAD for the central gland (PSA divided by central gland volume). These MRI-based volume and volume-adjusted PSA estimates were compared with prostatectomy specimen weight and Gleason score by using Pearson (r) or Spearman (ρ) correlation coefficients. RESULTS: The estimated total prostate volume showed a high correlation with reference standard volume (r = 0.94). Of the 61 patients, eight (13.1%) had a Gleason score of 6, 40 (65.6%) had a Gleason score of 7, seven (11.5%) had a Gleason score of 8, and six (9.8%) had a Gleason score of 9 for prostate cancer. The Gleason score was significantly correlated with central gland volume fraction (ρ = −0.42; p = 0.0007), PSAD (ρ = 0.46; p = 0.0002), and PSAD for the central gland (ρ = 0.55; p = 0.00001). CONCLUSION: Central gland volume fraction, PSAD, and PSAD for the central gland estimated from MRI examinations show a modest but significant correlation with Gleason score and have the potential to contribute to personalized risk assessment for significant prostate cancer