Simone Mazzetti

Detection of prostate cancer index lesions with multiparametric magnetic resonance imaging (mp-MRI) using whole-mount histological sections as the reference standard

To evaluate the sensitivity of multiparametric magnetic resonance imaging (mp‐MRI) for detecting prostate cancer foci, including the largest (index) lesions.

A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging

Multiparametric (mp)-Magnetic Resonance Imaging (MRI) is emerging as a powerful test to diagnose and stage prostate cancer (PCa). However, its interpretation is a time consuming and complex feat requiring dedicated radiologists. Computer-aided diagnosis (CAD) tools could allow better integration of data deriving from the different MRI sequences in order to obtain accurate, reproducible, non-operator dependent information useful to identify and stage PCa. In this paper, we present a fully automatic CAD system conceived as a 2-stage process. First, a malignancy probability map for all voxels within the prostate is created. Then, a candidate segmentation step is performed to highlight suspected areas, thus evaluating both the sensitivity and the number of false positive (FP) regions detected by the system. Training and testing of the CAD scheme is performed using whole-mount histological sections as the reference standard. On a cohort of 56 patients (i.e. 65 lesions) the area under the ROC curve obtained during the voxel-wise step was 0.91, while, in the second step, a per-patient sensitivity of 97% was reached, with a median number of FP equal to 3 in the whole prostate. The system here proposed could be potentially used as first or second reader to manage patients suspected to have PCa, thus reducing both the radiologists reporting time and the inter-reader variability. As an innovative setup, it could also be used to help the radiologist in setting the MRI-guided biopsy target.

MR-T2-weighted signal intensity: a new imaging biomarker of prostate cancer aggressiveness

Prostate cancer (PCa) is the most common solid neoplasm in males and a major cause of cancer-related death. Behaviour of PCa is dichotomous, as patients may either have an indolent clinical course or rapidly progress towards metastatic disease. Unfortunately, biopsy Gleason score (GS) may fail to predict cancer aggressiveness; tumour heterogeneity and inaccurate sampling during biopsy are major causes of underestimation. As a consequence, this frequently results in over-treatment, i.e. low-risk patients overcautiously undergo radical prostatectomy or radiotherapy, frequently with devastating side-effects. Some patients with PCa could be offered a more conservative approach if it were possible to predict patient risk confidently, especially in subjects lying in the grey zone of intermediate risk (i.e. GS = 7), which are in the majority. Recent studies have demonstrated that magnetic resonance (MR) imaging may help improve risk stratification in patients with PCa, providing imaging biomarkers of cancer aggressiveness. The aim of this study is to implement an automatic algorithm pipeline to discriminate different risks of progression from T2-weighted (T2-w) MR imaging. The obtained results confirm that T2-w signal intensity, together with other imaging biomarkers, may represent a new non-invasive approach to assess cancer aggressiveness, potentially helping to plan personalised treatments, and thus dramatically limiting over-diagnosis and over-treatment risks, and reducing the costs for the national healthcare system.

A fully automatic method to register the prostate gland on T2-weighted and EPI-DWI images

Prostate adenocarcinoma (PCa) is the most frequent noncutaneous cancer among men in developed countries. Magnetic Resonance (MR) has been used to detect PCa and several clinical trials report on the accuracy of the test. Multiparametric MR imaging (mpMRI) is defined as the integration of information from different morphological and functional datasets. mpMRI could be used to increase the performances of prostate MR, therefore allowing a more accurate assessment of the tumor gland extent, while reducing reporting time and interobserver variability. The first step to perform such a multiparametric analysis is to correct for voluntary and involuntary movements during the acquisitions, as well as for image distortion in the Diffusion Weighted (DWI) images. The aim of this work is to present a fully automatic registration algorithm between T2w and DWI images, able to realign the images and to correct the distortions in the DWI. Results showed a good overlap after registration and a strong decrease of mean surface distance in both the central gland and peripheral zone. These promising results suggest that the algorithm could be integrated in a CAD system which will combine the pharmacokinetic parameters derived from DCE-MRI, T2w MRI and DWI MR to generate one comprehensive value assessing the risk of malignancy. However to perform such a multiparametric analysis, it is necessary to correct for voluntary and involuntary (breathing, heart beating) movements during the DCE-MRI acquisition, and to realign also the DCE-MRI sequence to the T2w sequence.

A prostate CAD system based on multiparametric analysis of DCE T1-w, and DW automatically registered images

Prostate specific antigen (PSA)-based screening reduces the rate of death from prostate cancer (PCa) by 31%, but this benefit is associated with a high risk of overdiagnosis and overtreatment. As prostate transrectal ultrasound-guided biopsy, the standard procedure for prostate histological sampling, has a sensitivity of 77% with a considerable false-negative rate, more accurate methods need to be found to detect or rule out significant disease. Prostate magnetic resonance imaging has the potential to improve the specificity of PSA-based screening scenarios as a non-invasive detection tool, in particular exploiting the combination of anatomical and functional information in a multiparametric framework. The purpose of this study was to describe a computer aided diagnosis (CAD) method that automatically produces a malignancy likelihood map by combining information from dynamic contrast enhanced MR images and diffusion weighted images. The CAD system consists of multiple sequential stages, from a preliminary registration of images of different sequences, in order to correct for susceptibility deformation and/or movement artifacts, to a Bayesian classifier, which fused all the extracted features into a probability map. The promising results (AUROC=0.87) should be validated on a larger dataset, but they suggest that the discrimination on a voxel basis between benign and malignant tissues is feasible with good performances. This method can be of benefit to improve the diagnostic accuracy of the radiologist, reduce reader variability and speed up the reading time, automatically highlighting probably cancer suspicious regions.

A CAD system based on multi-parametric analysis for cancer prostate detection on DCE-MRI

Computer-aided diagnosis (CAD) systems using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data may be developed to help localize prostate cancer and guide biopsy, avoiding random sampling of the whole gland. The purpose of this study is to present a DCE-MRI CAD system, which calculates the likelihood of malignancy in a given area of the prostate by combining model-based and model-free parameters. The dataset includes 10 patients with prostate cancer, with a total of 13 foci of adenocarcinoma. The post-processing is based on the following steps: testing of registration quality, noise filtering, and extracting the proposed features needed to the CAD. Parameters with the best performance in discriminating between normal and cancer regions are selected by computing the area under the ROC curve, and by evaluating the correlation between pairs of features. A 6-dimensional parameters vector is generated for each pixel and fed into a Bayesian classifier, in which the output is the probability of malignancy. The classification performance is estimated using the leave-one-out method. The resulting area under the ROC curve is 0.899 (95%CI:0.893-0.905); sensitivity and specificity are 82.4% and 82.1% respectively at the best cut-off point (0.352). Preliminary results show that the system is accurate in detecting areas of the gland that are involved by tumor. Further studies will be necessary to confirm these promising preliminary results.

Big data in oncologic imaging

Cancer is a complex disease and unfortunately understanding how the components of the cancer system work does not help understand the behavior of the system as a whole. In the words of the Greek philosopher Aristotle “the whole is greater than the sum of parts.” To date, thanks to improved information technology infrastructures, it is possible to store data from each single cancer patient, including clinical data, medical images, laboratory tests, and pathological and genomic information. Indeed, medical archive storage constitutes approximately one-third of total global storage demand and a large part of the data are in the form of medical images. The opportunity is now to draw insight on the whole to the benefit of each individual patient. In the oncologic patient, big data analysis is at the beginning but several useful applications can be envisaged including development of imaging biomarkers to predict disease outcome, assessing the risk of X-ray dose exposure or of renal damage following the administration of contrast agents, and tracking and optimizing patient workflow. The aim of this review is to present current evidence of how big data derived from medical images may impact on the diagnostic pathway of the oncologic patient.

Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study

ObjectivesTo compare the performance of experienced readers in detecting prostate cancer (PCa) using likelihood maps generated by a CAD system with that of unassisted interpretation of multiparametric magnetic resonance imaging (mp-MRI).MethodsThree experienced radiologists reviewed mp-MRI prostate cases twice. First, readers observed CAD marks on a likelihood map and classified as positive those suspicious for cancer. After 6 weeks, radiologists interpreted mp-MRI examinations unassisted, using their favourite protocol. Sensitivity, specificity, reading time and interobserver variability were compared for the two reading paradigms.ResultsThe dataset comprised 89 subjects of whom 35 with at least one significant PCa. Sensitivity was 80.9% (95% CI 72.1–88.0%) and 87.6% (95% CI 79.8–93.2; p = 0.105) for unassisted and CAD paradigm respectively. Sensitivity was higher with CAD for lesions with GS > 6 (91.3% vs 81.2%; p = 0.046) or diameter ≥10 mm (95.0% vs 80.0%; p = 0.006). Specificity was not affected by CAD. The average reading time with CAD was significantly lower (220 s vs 60 s; p < 0.001).ConclusionsExperienced readers using likelihood maps generated by a CAD scheme can detect more patients with ≥10 mm PCa lesions than unassisted MRI interpretation; overall reporting time is shorter. To gain more insight into CAD–human interaction, different reading paradigms should be investigated.Key points• With CAD, sensitivity increases in patients with prostate tumours ≥10 mm and/or GS > 6.• CAD significantly reduces reporting time of multiparametric MRI.• When using CAD, a marginal increase of inter-reader agreement was observed.

A 3D voxel neighborhood classification approach within a multiparametric MRI classifier for prostate cancer detection

Prostate Magnetic Resonance Imaging (MRI) is one of the most promising approaches to facilitate prostate cancer diagnosis. The effort of research community is focused on classification techniques of MR images in order to predict the cancer position and its aggressiveness. The reduction of False Negatives (FNs) is a key aspect to reduce mispredictions and to increase sensitivity. In order to deal with this issue, the most common approaches add extra filtering algorithms after the classification step; unfortunately, this solution increases the prediction time and it may introduce errors. The aim of this study is to present a methodology implementing a 3D voxel-wise neighborhood features evaluation within a Support Vector Machine (SVM) classification model. When compared with a common single-voxel-wise classification, the presented technique increases both specificity and sensitivity of the classifier, without impacting on its performances. Different neighborhood sizes have been tested to prove the overall good performance of the classification.

A Prostate Cancer Computer Aided Diagnosis Software including Malignancy Tumor Probabilistic Classification

Prostate Cancer (PCa) is the most common solid neoplasm in males and a major cause of cancer-related death. n nScreening based on Prostate Specific Antigen (PSA) reduces the rate of death by 31%, but it is associated n nwith a high risk of over-diagnosis and over-treatment. Prostate Magnetic Resonance Imaging (MRI) has the n npotential to improve the specificity of PSA-based screening scenarios as a non-invasive detection tool. Research n ncommunity effort focused on classification techniques based on MRI in order to produce a malignancy n nlikelihood map. The paper describes the prototyping design, the implemented work-flow and the software n narchitecture of a Computer Aided Diagnosis (CAD) software which aims at providing a comprehensive diagnostic n ntool, including an integrated classification stack, from a preliminary registration of images to the n nclassification process. This software can improve the diagnostic accuracy of the radiologist, reduce reader n nvariability and speed up the whole diagnostic work-up.