Development of multiparametric MRI models for prostate cancer detection based on improved correlative pathology

University of Minnesota Ph.D. dissertation. June 2014. Major: Biophysical Sciences and Medical Physics. Advisor: Gregory J. Metzger. 1 computer file (PDF); xix, 116 pages.Prostate cancer (PCa) is a prevalent disease which affects 1 in 6 men in the United States and has overtaken lung cancer as the leading cause of cancer related deaths in American men and number two worldwide. Among several diagnostic imaging tests that are available for detection of PCa in the market today, Magnetic Resonance Imaging (MRI) occupies a unique position in the detection of PCa due to its excellent soft tissue contrast and its ability to generate tissue property dependent multi-parametric data. While MRI has become an increasingly valuable tool in the management of men with PCa, its use to identify aggressive disease and characterize extent have yet to be developed. Multi-parametric MRI (MP-MRI) studies have been shown to increase sensitivity and specificity towards PCa detection compared to any single MRI dataset. The ability to develop and evaluate MP-MRI to prospectively detect disease, assess aggressiveness and delineate extent, first requires the retrospective validation against post-surgical pathology sections. Despite the large effort made by many groups in this area of research, the correlation of in vivo MP-MRI with pathology is still a challenge and to date is insufficient to develop highly accurate models of disease. To address this problem this thesis showcases (1) a novel registration approach called LATIS (Local Affine Transformation assisted by Internal Structures) for co-registering post prostatectomy pseudo-whole mount (PWM) pathological sections with in vivo MRI images and (2) MP-MRI based predictive model for disease detection using a composite biomarker score based on a unique database of pathology co-registered MR data sets. Also showcased in this thesis is a study where r1 and r2* relaxivities of a common paramagnetic contrast agent were measured in blood and saline at both 3T and 7T. This is important information to have when attempting to perform DCE-MRI studies as part of a MP-MRI protocol at ultra-high magnetic field strengths

3D fusion of histology to multi-parametric MRI for prostate cancer imaging evaluation and lesion-targeted treatment planning

Multi-parametric magnetic resonance imaging (mpMRI) of localized prostate cancer has the potential to support detection, staging and localization of tumors, as well as selection, delivery and monitoring of treatments. Delineating prostate cancer tumors on imaging could potentially further support the clinical workflow by enabling precise monitoring of tumor burden in active-surveillance patients, optimized targeting of image-guided biopsies, and targeted delivery of treatments to decrease morbidity and improve outcomes. Evaluating the performance of mpMRI for prostate cancer imaging and delineation ideally includes comparison to an accurately registered reference standard, such as prostatectomy histology, for the locations of tumor boundaries on mpMRI. There are key gaps in knowledge regarding how to accurately register histological reference standards to imaging, and consequently further gaps in knowledge regarding the suitability of mpMRI for tasks, such as tumor delineation, that require such reference standards for evaluation. To obtain an understanding of the magnitude of the mpMRI-histology registration problem, we quantified the position, orientation and deformation of whole-mount histology sections relative to the formalin-fixed tissue slices from which they were cut. We found that (1) modeling isotropic scaling accounted for the majority of the deformation with a further small but statistically significant improvement from modeling affine transformation, and (2) due to the depth (mean±standard deviation (SD) 1.1±0.4 mm) and orientation (mean±SD 1.5±0.9°) of the sectioning, the assumption that histology sections are cut from the front faces of tissue slices, common in previous approaches, introduced a mean error of 0.7 mm. To determine the potential consequences of seemingly small registration errors such as described above, we investigated the impact of registration accuracy on the statistical power of imaging validation studies using a co-registered spatial reference standard (e.g. histology images) by deriving novel statistical power formulae that incorporate registration error. We illustrated, through a case study modeled on a prostate cancer imaging trial at our centre, that submillimeter differences in registration error can have a substantial impact on the required sample sizes (and therefore also the study cost) for studies aiming to detect mpMRI signal differences due to 0.5 – 2.0 cm3 prostate tumors. With the aim of achieving highly accurate mpMRI-histology registrations without disrupting the clinical pathology workflow, we developed a three-stage method for accurately registering 2D whole-mount histology images to pre-prostatectomy mpMRI that allowed flexible placement of cuts during slicing for pathology and avoided the assumption that histology sections are cut from the front faces of tissue slices. The method comprised a 3D reconstruction of histology images, followed by 3D–3D ex vivo–in vivo and in vivo–in vivo image transformations. The 3D reconstruction method minimized fiducial registration error between cross-sections of non-disruptive histology- and ex-vivo-MRI-visible strand-shaped fiducials to reconstruct histology images into the coordinate system of an ex vivo MR image. We quantified the mean±standard deviation target registration error of the reconstruction to be 0.7±0.4 mm, based on the post-reconstruction misalignment of intrinsic landmark pairs. We also compared our fiducial-based reconstruction to an alternative reconstruction based on mutual-information-based registration, an established method for multi-modality registration. We found that the mean target registration error for the fiducial-based method (0.7 mm) was lower than that for the mutual-information-based method (1.2 mm), and that the mutual-information-based method was less robust to initialization error due to multiple sources of error, including the optimizer and the mutual information similarity metric. The second stage of the histology–mpMRI registration used interactively defined 3D–3D deformable thin-plate-spline transformations to align ex vivo to in vivo MR images to compensate for deformation due to endorectal MR coil positioning, surgical resection and formalin fixation. The third stage used interactively defined 3D–3D rigid or thin-plate-spline transformations to co-register in vivo mpMRI images to compensate for patient motion and image distortion. The combined mean registration error of the histology–mpMRI registration was quantified to be 2 mm using manually identified intrinsic landmark pairs. Our data set, comprising mpMRI, target volumes contoured by four observers and co-registered contoured and graded histology images, was used to quantify the positive predictive values and variability of observer scoring of lesions following the Prostate Imaging Reporting and Data System (PI-RADS) guidelines, the variability of target volume contouring, and appropriate expansion margins from target volumes to achieve coverage of histologically defined cancer. The analysis of lesion scoring showed that a PI-RADS overall cancer likelihood of 5, denoting “highly likely cancer”, had a positive predictive value of 85% for Gleason 7 cancer (and 93% for lesions with volumes \u3e0.5 cm3 measured on mpMRI) and that PI-RADS scores were positively correlated with histological grade (ρ=0.6). However, the analysis also showed interobserver differences in PI-RADS score of 0.6 to 1.2 (on a 5-point scale) and an agreement kappa value of only 0.30. The analysis of target volume contouring showed that target volume contours with suitable margins can achieve near-complete histological coverage for detected lesions, despite the presence of high interobserver spatial variability in target volumes. Prostate cancer imaging and delineation have the potential to support multiple stages in the management of localized prostate cancer. Targeted biopsy procedures with optimized targeting based on tumor delineation may help distinguish patients who need treatment from those who need active surveillance. Ongoing monitoring of tumor burden based on delineation in patients undergoing active surveillance may help identify those who need to progress to therapy early while the cancer is still curable. Preferentially targeting therapies at delineated target volumes may lower the morbidity associated with aggressive cancer treatment and improve outcomes in low-intermediate-risk patients. Measurements of the accuracy and variability of lesion scoring and target volume contouring on mpMRI will clarify its value in supporting these roles

Méthode de mise en correspondance tridimensionnelle entre des coupes IRM de la prostate et les coupes histologiques des pièces de prostatectomie

Prostate cancer is the most frequently diagnosed cancer of men in Europe, yet no current imaging technique is capable of detecting with precision tumours in the prostate. The histology slices are the gold standard for the diagnosis. Therefore, in order to evaluate each imaging technique, the histology slices must be precisely registered to the imaged data. As it cannot be assumed that the histology slices are cut along the same plane as the imaged data is acquired, the registration must be considered as a 3D problem. An apparatus has been developed that enables internal fiducial markers to be created in the histology slices in a rapid and standardised manner. An algorithm has been developed that automatically detects and identifies these markers, enabling the alignment of the histology slices. The method has been tested on 10 prostate specimens, with 19.2 slices on average per specimen. The accuracy of the alignment at the fiducial markers was on average 0.18±0.13 mm. A second algorithm was developed to 3D register the aligned histology slices with the MR images. The registration is designed to be guided by the ejaculatory ducts, an anatomical landmark present in every prostate and visible in both histology and MR images acquired at standard clinical resolution. The algorithm was first tested by using the fiducial needles to guide the registration. The average registration accuracy was 0.45 ± 0.25 mm at the fiducial needles and 1.04±0.21 mm at the ejaculatory ducts. The algorithm was then tested by using the ejaculatory ducts to guide the registration. The average registration accuracy was 0.16±0.05 mm at the ejaculatory ducts and 2.82 ± 0.41 mm at the fiducial needles. The results suggest that the histology shrinkage factor is of the order 1.07±0.03 and the tilt of the histology slicing plane is 13.6◦ ±9.61◦, with both parameters showing significant varianceLe cancer de la prostate est le cancer le plus fréquent chez l'homme en Europe, néanmoins il n'existe actuellement pas de technique d'imagerie permettant de détecter avec précision les tumeurs dans la glande. Sachant que les coupes histologiques contiennent la réalité de terrain concernant le diagnostic, il est nécessaire de recaler les images de chaque technique d'imagerie aux coupes histologiques afin de pouvoir les évaluer. De plus, comme il n'existe pas de méthode permettant de contrôler précisément le plan de coupe histologique, le recalage doit être considéré comme un problème 3D. Un dispositif permettant de réaliser, de manière rapide et standardisée, des marqueurs internes dans les coupes histologiques a été développé, de même qu'un algorithme permettant de détecter automatiquement ces marqueurs, de les identifier et d'aligner les coupes histologiques. La méthode a été testée sur 10 prostates, avec en moyenne 19.2 coupes par prostate, et a permis d'obtenir une précision de recalage moyenne de 0.18 ± 0.13 mm au niveau des marqueurs. Un deuxième algorithme a été développé pour recaler les coupes histologiques, une fois alignées, avec les images IRM. Ce recalage a été conçu pour être guidé par les canaux éjaculateurs, un repère anatomique présent dans chaque prostate et visible à la fois en histologie et dans les images IRM cliniques, acquises avec une résolution standard. L'algorithme a d'abord été testé en s'appuyant sur les marqueurs artificiels. La précision obtenue pour le recalage était en moyenne de 0.45±0.25 mm au niveau des marqueurs et de 1.04 ± 0.21 mm au niveau des canaux éjaculateurs. L'algorithme a enfin été testé en guidant le recalage à l'aide de la position des canaux éjaculateurs. La précision moyenne obtenue était alors de 0.16±0.05 mm au niveau des canaux éjaculateurs et de 2.82±0.41 mm au niveau des marqueurs. Ces résultats suggèrent une valeur du facteur de rétrécissement de l'ordre de 1.07±0.03 et une inclinaison vis à vis du plan de coupe histologique de l'ordre de 13.6◦ ± 9.61◦, avec une variance importante pour ces deux paramètre

Diagnostic du cancer de la prostate par imagerie moderne : place de l’IRM dans la sélection des candidats à une surveillance active et dans la caractérisation des zones tumorales intra-prostatiques

MRI is an increasingly important imaging modality for prostate cancer diagnosis. Further indications are being validated in prostate cancer to establish the prognostic, to guide treatment and to follow up patients especially after partial treatment. The first part of this work has focused on clinical studies of MRI in patient selection for active surveillance. The performance of MRI particularly in the detection of anterior cancers would reduce the risk of initial underestimation of tumor burden and therefore reduce the risk of reclassification during active surveillance protocols. The second part of this work has focused on the correlation between the signal abnormalities on MRI and intra-prostatic areas. We used a simple and reproducible technique for MRI and histopathology registration and we correlated signal abnormalities recorded on MRI with quantitative histopathological parameters at prostatectomy surgical specimens.L’IRM représente une modalité d’imagerie du cancer de la prostate qui occupe une place de plus en plus importante pour le diagnostic positif. D’autres indications sont en cours de validation pour établir le pronostique, pour guider le traitement et pour assurer le suivi après traitement notamment partiel. La première partie de ce travail a porté sur les études cliniques de l’IRM dans la sélection des candidats à une surveillance active. Les performances de l’IRM particulièrement dans la détection des cancers antérieurs permettront de réduire le risque de sous-estimation initiale des tumeurs et par conséquent le risque de la reclassification au cours des protocoles de surveillance active. La seconde partie de ce travail a porté sur la corrélation entre les anomalies de signal à l’IRM et les zones tumorales et non tumorales intra-prostatiques. La validation d’une technique simple et reproductible de recalage a permis ensuite une corrélation des anomalies de signal enregistrées sur l’IRM et des paramètres histo-pathologiques quantitatifs des pièces opératoires de prostatectomie

Validación clínica de algoritmos de procesado en imagen médica. Aplicación en resonancia magnética multiparamétrica de próstata

[ES] Hoy en día, el cáncer de próstata es considerado uno de los carcinomas malignos más prevalentes entre la población masculina, siendo en España, el segundo más frecuente y el tercero en mortalidad. El tratamiento consta principalmente de procedimientos quirúrgicos, lo que puede producir en el paciente, efectos secundarios y secuelas. Por ello, surge la necesidad del diagnóstico precoz, de establecer el riesgo de progresión de los tumores y valorar a cada paciente antes de tomar cualquier decisión terapéutica. Con el propósito de obtener una mayor cantidad de información sobre la patología, una de las técnicas más utilizadas, es la Imagen por Resonancia Magnética (RM), con la cual, de manera no invasiva, se puede clasificar de forma local y regional los estados en pacientes con cáncer. Los avances en la tecnología han derivado en el desarrollo de RM multiparamétrica, en el que se combina secuencias T2W anatómicas con evaluaciones funcionales y fisiológicas, incluyendo imágenes de difusión e imágenes de perfusión. Al existir una excesiva variación en la interpretación de las imágenes de cáncer de próstata, para estandarizar el proceso de evaluación RM multiparamétrica y su aplicación, se creó el Sistema de Información y Datos en Imagen de Próstata (PI-RADS), formado por directrices básicas para la adquisición de las imágenes y la evaluación de cada lesión analizando la información proveniente de las distintas secuencias. El presente trabajo consiste en la validación de los algoritmos de preprocesado involucrados en el análisis de las distintas secuencias, siendo estos: selección de la función de entrada arterial en imágenes de perfusión DCE y registros intersecuencia. Se lleva a cabo un proceso de validación completo e independiente para cada algoritmo de preprocesado, obteniéndose métricas y medidas estadísticas para evaluar el buen funcionamiento de los distintos algoritmos en una base de datos de pacientes representativos de la práctica clínica.[EN] One of the most prevalent malignant carcinomas among the male population in these days is prostate cancer, also it is the second most frequent in Spain and the third in mortality. The treatment consists mainly of surgical procedures, which can produce side effects and sequels. Therefore, the need for early diagnosis arises, to establish the risk of tumor progression and to evaluate each patient before taking any therapeutic decision. In order to obtain a greater amount of information about this pathology, one of the most used techniques is Magnetic Resonance Imaging (MRI), which with the use of contrast agents, states in cancer patients can be locally and regionally classified. Advances in technology have led to the development of Multiparametric MRI (mpMRI), which combines anatomical T2W sequences with functional and physiological assessments, including diffusion and perfusion imaging. As there is excessive variation in the interpretation of prostate cancer images, in order to standardize the mpMRI evaluation process and its application, the Prostate Image-Reporting and Data System (PI-RADS) was created, consisting of basic guidelines for the evaluation of each lesion with this type of analysis. The present work is about the validation of a series of pre-processing algorithms for the evaluation of prostate cancer using mpMRI and the recommendations of PI-RADS. These algorithms are the selection of the arterial input function in DCE perfusion images and the registration of images. A complete and independent validation process is carried out for each type of analysis, obtaining metrics and statistical measures that allow differentiating and demonstrating the validity of the application under study.[CA] Hui en dia, el càncer de pròstata és considerat un dels carcinomes malignes més prevalents entre la població masculina, sent a Espanya, el segon més freqüent i el tercer en mortalitat. El tractament consta principalment de procediments quirúrgics, la qual cosa pot produir en el pacient, efectes secundaris i seqüeles. Per això, sorgix la necessitat del diagnòstic precoç, d'establir el risc de progressió dels tumors i valorar cada pacient abans de prendre qualsevol decisió terapèutica. Amb el propòsit d'obtindre una major quantitat d'informació sobre la patologia, una de les tècniques més utilitzades, és la Imatge per Ressonància Magnètica (RM) , amb la qual, de manera no invasiva, es pot classificar de forma local i regional els estats en pacients amb càncer. Els avanços en la tecnologia han derivat en el desenrotllament de RM multiparamétrica, en el que es combina seqüències T2W anatòmiques amb avaluacions funcionals i fisiològiques, incloent imatges de difusió i imatges de perfusió. A l'existir una excessiva variació en la interpretació de les imatges de càncer de pròstata, per a estandarditzar el procés d'avaluació RM multiparamétrica i la seua aplicació, es va crear el Sistema d'Informació i Dades en Imatge de Pròstata (PI- RADS) , format per directrius bàsiques per a l'adquisició de les imatges i l'avaluació de cada lesió analitzant la informació provinent de les distintes seqüències. El present treball consistix en la validació dels algoritmes de preprocessat involucrats en l'anàlisi de les distintes seqüències, sent estos: selecció de la funció d'entrada arterial en imatges de perfusió DCE i registres interseqüència. Es du a terme un procés de validació complet i independent per a cada algoritme de preprocessat, obtenint-se mètriques i mesures estadístiques per a avaluar el bon funcionament dels distints algoritmes en una base de dades de pacients representatius de la pràctica clínica.Mancebo González, M. (2020). Validación clínica de algoritmos de procesado en imagen médica. Aplicación en resonancia magnética multiparamétrica de próstata. Universitat Politècnica de València. http://hdl.handle.net/10251/162077TFG