Impact of the Kaiser score on clinical decision-making in BI-RADS 4 mammographic calcifications examined with breast MRI.

OBJECTIVES: To investigate whether the application of the Kaiser score for breast magnetic resonance imaging (MRI) might downgrade breast lesions that present as mammographic calcifications and avoid unnecessary breast biopsies METHODS: This IRB-approved, retrospective, cross-sectional, single-center study included 167 consecutive patients with suspicious mammographic calcifications and histopathologically verified results. These patients underwent a pre-interventional breast MRI exam for further diagnostic assessment before vacuum-assisted stereotactic-guided biopsy (95 malignant and 72 benign lesions). Two breast radiologists with different levels of experience independently read all examinations using the Kaiser score, a machine learning-derived clinical decision-making tool that provides probabilities of malignancy by a formalized combination of diagnostic criteria. Diagnostic performance was assessed by receiver operating characteristics (ROC) analysis and inter-reader agreement by the calculation of Cohen's kappa coefficients. RESULTS: Application of the Kaiser score revealed a large area under the ROC curve (0.859-0.889). Rule-out criteria, with high sensitivity, were applied to mass and non-mass lesions alike. The rate of potentially avoidable breast biopsies ranged between 58.3 and 65.3%, with the lowest rate observed with the least experienced reader. CONCLUSIONS: Applying the Kaiser score to breast MRI allows stratifying the risk of breast cancer in lesions that present as suspicious calcifications on mammography and may thus avoid unnecessary breast biopsies. KEY POINTS: • The Kaiser score is a helpful clinical decision tool for distinguishing malignant from benign breast lesions that present as calcifications on mammography. • Application of the Kaiser score may obviate 58.3-65.3% of unnecessary stereotactic biopsies of suspicious calcifications. • High Kaiser scores predict breast cancer with high specificity, aiding clinical decision-making with regard to re-biopsy in case of negative results.

Can the addition of clinical information improve the accuracy of PI-RADS version 2 for the diagnosis of clinically significant prostate cancer in positive MRI?

AIM: To report prostate cancer (PCa) prevalence in Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) categories and investigate the potential to avoid unnecessary, magnetic resonance imaging (MRI)-guided in-bore biopsies by adding clinical and biochemical patient characteristics. MATERIALS AND METHODS: The present institutional review board-approved, prospective study on 137 consecutive men with 178 suspicious lesions on 3 T MRI was performed. Routine data collected for each patient included patient characteristics (age, prostate volume), clinical background information (prostate-specific antigen [PSA] levels, PSA density), and PI-RADS v2 scores assigned in a double-reading approach. RESULTS: Histopathological evaluation revealed a total of 93/178 PCa (52.2%). The mean age was 66.3 years and PSA density was 0.24 ng/ml2 (range, 0.04-0.89 ng/ml). Clinically significant PCa (csPCa, Gleason score >6) was confirmed in 50/93 (53.8%) lesions and was significantly associated with higher PI-RADS v2 scores (p=0.0044). On logistic regression analyses, age, PSA density, and PI-RADS v2 scores contributed independently to the diagnosis of csPCa (p=7.9×10-7, p=0.097, and p=0.024, respectively). The resulting area under the receiver operating characteristic curve (AUC) to predict csPCa was 0.76 for PI-RADS v2, 0.59 for age, and 0.67 for PSA density. The combined regression model yielded an AUC of 0.84 for the diagnosis of csPCa and was significantly superior to each single parameter (p≤0.0009, respectively). Unnecessary biopsies could have been avoided in 50% (64/128) while only 4% (2/50) of csPCa lesions would have been missed. CONCLUSIONS: Adding age and PSA density to PI-RADS v2 scores improves the diagnostic accuracy for csPCa. A combination of these variables with PI-RADS v2 can help to avoid unnecessary in-bore biopsies while still detecting the majority of csPCa.

[Contrast-agent free imaging of the prostate : Ready for clinical practice?] / Kontrastmittelfreie Bildgebung der Prostata : Reif für die Praxis?

BACKGROUND: Multiparametric MRI (mpMRI) is currently the most accurate imaging modality for detection and local staging of prostate cancer (PCa). Disadvantages of this modality are high costs, time consumption and the need for a contrast medium. AIMS: The aim of the work was to provide an overview of the current state of fast and contrast-free MRI imaging of the prostate. RESULTS: Biparametric examination protocols and the use of three-dimensional T2-weighted sequences are readily available methods that can be used to shorten the examination time without sacrificing diagnostic accuracy.

Impact of androgen deprivation therapy on apparent diffusion coefficient and T2w MRI for histogram and texture analysis with respect to focal radiotherapy of prostate cancer.

PURPOSE: Accurate prostate cancer (PCa) detection is essential for planning focal external beam radiotherapy (EBRT). While biparametric MRI (bpMRI) including T2-weighted (T2w) and diffusion-weighted images (DWI) is an accurate tool to localize PCa, its value is less clear in the case of additional androgen deprivation therapy (ADT). The aim of this study was to investigate the value of a textural feature (TF) approach on bpMRI analysis in prostate cancer patients with and without neoadjuvant ADT with respect to future dose-painting applications. METHODS: 28 PCa patients (54-80 years) with (n = 14) and without (n = 14) ADT who underwent bpMRI with T2w and DWI were analyzed retrospectively. Lesions, central gland (CG), and peripheral zone (PZ) were delineated by an experienced urogenital radiologist based on localized pre-therapeutic histopathology. Histogram parameters and 20 Haralick TF were calculated. Regional differences (i. e., tumor vs. PZ, tumor vs. CG) were analyzed for all imaging parameters. Receiver-operating characteristic (ROC) analysis was performed to measure diagnostic performance to distinguish PCa from benign prostate tissue and to identify the features with best discriminative power in both patient groups. RESULTS: The obtained sensitivities were equivalent or superior when utilizing the TF in the no-ADT group, while specificity was higher for the histogram parameters. However, in the ADT group, TF outperformed the conventional histogram parameters in both specificity and sensitivity. Rule-in and rule-out criteria for ADT patients could exclusively be defined with the aid of TF. CONCLUSIONS: The TF approach has the potential for quantitative image-assisted boost volume delineation in PCa patients even if they are undergoing neoadjuvant ADT.

Apparent diffusion coefficient values obtained by unenhanced MRI predicts disease-specific survival in bladder cancer.

AIM: To assess the ability of apparent diffusion coefficient (ADC) measurements obtained by MRI to predict disease-specific survival (DSS) in patients with bladder cancer and compare it with established clinico-pathological prognostic factors. MATERIAL AND METHODS: The ethical review board approved this cross-sectional study. Patients with suspected bladder cancer receiving diagnostic 3 T diffusion-weighted imaging (DWI) of the bladder before transurethral resection of the bladder (TUR-B) or radical cystectomy were evaluated prospectively. Two independent radiologists measured ADC values in bladder cancer lesions in regions of interest. Associations between ADC values and pathological features with DSS were tested statistically. A combined model was established using artificial neuronal network (ANN) methodology. RESULTS: A total of 51 patients (median age 69 years, range 41-89 years) were included. Three patients were lost to follow-up, leaving 48 patients for survival analysis. Seven patients died during the 795 months studied. ADC showed significant potential to predict DSS (p<0.05). Except for grading, all pathological features as assessed by TUR-B could predict DSS (p<0.05, respectively). The combined ANN classifier showed the highest accuracy to predict DSS (0.889, 95% confidence interval: 0.732-1, p=0.001) compared to all single parameters. ADC was the second important predictor of the ANN. CONCLUSIONS: ADC measurements obtained by unenhanced MRI predicts DSS in bladder cancer patients. A combined classifier including ADC and clinico-pathological information showed high accuracy to identify patients at high risk for disease-related death.

Automatic quantification of multi-modal rigid registration accuracy using feature detectors.

In radiotherapy, the use of multi-modal images can improve tumor and target volume delineation. Images acquired at different times by different modalities need to be aligned into a single coordinate system by 3D/3D registration. State of the art methods for validation of registration are visual inspection by experts and fiducial-based evaluation. Visual inspection is a qualitative, subjective measure, while fiducial markers sometimes suffer from limited clinical acceptance. In this paper we present an automatic, non-invasive method for assessing the quality of intensity-based multi-modal rigid registration using feature detectors. After registration, interest points are identified on both image data sets using either speeded-up robust features or Harris feature detectors. The quality of the registration is defined by the mean Euclidean distance between matching interest point pairs. The method was evaluated on three multi-modal datasets: an ex vivo porcine skull (CT, CBCT, MR), seven in vivo brain cases (CT, MR) and 25 in vivo lung cases (CT, CBCT). Both a qualitative (visual inspection by radiation oncologist) and a quantitative (mean target registration error-mTRE-based on selected markers) method were employed. In the porcine skull dataset, the manual and Harris detectors give comparable results but both overestimated the gold standard mTRE based on fiducial markers. For instance, for CT-MR-T1 registration, the mTREman (based on manually annotated landmarks) was 2.2 mm whereas mTREHarris (based on landmarks found by the Harris detector) was 4.1 mm, and mTRESURF (based on landmarks found by the SURF detector) was 8 mm. In lung cases, the difference between mTREman and mTREHarris was less than 1 mm, while the difference between mTREman and mTRESURF was up to 3 mm. The Harris detector performed better than the SURF detector with a resulting estimated registration error close to the gold standard. Therefore the Harris detector was shown to be the more suitable method to automatically quantify the geometric accuracy of multimodal rigid registration.

Magnetic resonance imaging-guided prostate biopsy: institutional analysis and systematic review.

OBJECTIVES: To evaluate the detection rate of prostate cancer (PCa) after magnetic resonance-guided biopsy (MRGB); to monitor the patient cohort with negative MRGB results and to compare our own results with other reports in the current literature. MATERIALS AND METHODS: A group of 41 patients was included in this IRB-approved study and subjected to combined MRI and MRGB. MRGB was performed in a closed 1.5 T MR unit and the needle was inserted rectally. The follow-up period ranged between 12 and 62 months (mean 3.1 years). To compare the results with the literature, a systematic literature search was performed. Eighteen publications were evaluated. RESULTS: The cancer-suspicious regions were punctured successfully in all cases. PCa was detected in eleven patients (26.9 %) who were all clinically significant. MRGB showed a benign histology in the remaining 30 patients. In the follow-up (mean 3.1 years) of patients with benign histology, no new PCa was diagnosed. The missed cancer rate during follow-up was 0.0 % in our study. CONCLUSION: MRGB is effective for the detection of clinically significant cancer, and this is in accordance with the recent literature. In the follow-up of patients with benign histology, no new PCa was discovered. Although the probability of developing PCa after negative MRGB is very low, active surveillance is reasonable.

[Imaging in oncology: terms and definitions]. / Onkologische Bildgebung: Begriffe und Definitionen.

Oncologic imaging includes the morphological description of the primary tumor region for an accurate classification of the tumor and lymph node stage and whether distant metastases have occurred according to the TNM staging system. Knowing the stage of the disease helps to plan the treatment and to estimate the prognosis. In clinical routine this is accomplished by conventional imaging techniques, such as ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI). Additionally, oncologic imaging is essential in treatment monitoring to visualize and quantify the effect of cancer therapy according to response evaluation criteria in solid tumors (RECIST) and World Health Organization (WHO) criteria. The tremendous development in oncology and technical innovations in imaging represent a particular challenge for radiology.