Technical Note: Method to correlate whole‐specimen histopathology of radical prostatectomy with diagnostic MR imaging

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134778/1/mp1016.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134778/2/mp1016_am.pd

Prostate Tumor Volume Measurement on Digital Histopathology and Magnetic Resonance Imaging

An accurate assessment of prostate tumour burden supports appropriate treatment selection, ranging from active surveillance through focal therapy, to radical whole-prostate therapies. For selected patients, knowledge of the three-dimensional locations and sizes of prostate tumours on pre-procedural imaging supports planning of effective focal therapies that preferentially target tumours, while sparing surrounding healthy tissue. In the post-prostatectomy context, pathologic measurement of tumour burden in the surgical specimen may be an independent prognostic factor determining the need for potentially life-saving adjuvant therapy. An accurate and repeatable method for tumour volume assessment based on histology sections taken from the surgical specimen would be supportive both to the clinical workflow in the post-prostatectomy setting and to imaging validation studies correlating tumour burden measurements on pre-prostatectomy imaging with reference standard histologic tumour volume measurements. Digital histopathology imaging is enabling a transition to a more objective quantification of some surgical pathology assessments, such as tumour volume, that are currently visually estimated by pathologists and subject to inter-observer variability. Histologic tumour volume measurement is challenged by the traditional 3–5 mm sparse spacing of images acquired from sections of radical prostatectomy specimens. Tumour volume estimates may benefit from a well-motivated approach to inter-slide tumour boundary interpolation that crosses these large gaps in a smooth fashion. This thesis describes a new level set-based shape interpolation method that reconstructs smooth 3D shapes based on arbitrary 2D tumour contours on digital histology slides. We measured the accuracy of this approach and used it as a reference standard against which to compare previous approaches in the literature that are simpler to implement in a clinical workflow, with the aim of determining a method for histologic tumour volume estimation that is both accurate and amenable to widespread implementation. We also measured the effect of decreasing inter-slide spacing on the repeatability of histologic tumour volume estimation. Furthermore, we used this histologic reference standard for tumour volume to measure the accuracy, inter-observer variability, and inter-sequence variability of prostate tumour volume estimation based on radiologists’ contouring of multi-parametric magnetic resonance imaging (MPMRI). Our key findings were that (1) simple approaches to histologic tumour volume estimation that are based on 2- or 3-dimensional linear tumour measurements are more accurate than those based on 1-dimensional measurements; (2) although tumour shapes produced by smooth through-slide interpolation are qualitatively substantially different from those obtained from a planimetric approach normally used as a reference standard for histologic tumour volume, the volumes obtained were similar; (3) decreasing inter-slide spacing increases repeatability of histologic tumour volume estimates, and this repeatability decreases rapidly for inter-slide spacing values greater than 5 mm; (4) on MPMRI, observers consistently overestimated tumour volume as compared to the histologic reference standard; and (5) inter-sequence variability in MPMRI-based tumour volume estimation exceeded inter-observer variability

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

The role of magnetic resonance imaging (MRI) in focal therapy for prostate cancer: recommendations from a consensus panel

OBJECTIVE: To establish a consensus on the utility of multiparametric magnetic resonance imaging (mpMRI) to identify patients for focal therapy. METHODS: Urological surgeons, radiologists, and basic researchers, from Europe and North America participated in a consensus meeting about the use of mpMRI in focal therapy of prostate cancer. The consensus process was face-to-face and specific clinical issues were raised and discussed with agreement sought when possible. All participants are listed among the authors. Topics specifically did not include staging of prostate cancer, but rather identifying the optimal requirements for performing MRI, and the current status of optimally performed mpMRI to (i) determine focality of prostate cancer (e.g. localising small target lesions of \u3e/=0.5 mL), (ii) to monitor and assess the outcome of focal ablation therapies, and (iii) to identify the diagnostic advantages of new MRI methods. In addition, the need for transperineal template saturation biopsies in selecting patients for focal therapy was discussed, if a high quality mpMRI is available. In other words, can mpMRI replace the role of transperineal saturation biopsies in patient selection for focal therapy? RESULTS: Consensus was reached on most key aspects of the meeting; however, on definition of the optimal requirements for mpMRI, there was one dissenting voice. mpMRI is the optimum approach to achieve the objectives needed for focal therapy, if made on a high quality machine (3T with/without endorectal coil or 1.5T with endorectal coil) and judged by an experienced radiologist. Structured and standardised reporting of prostate MRI is paramount. State of the art mpMRI is capable of localising small tumours for focal therapy. State of the art mpMRI is the technique of choice for follow-up of focal ablation. CONCLUSIONS: The present evidence for MRI in focal therapy is limited. mpMRI is not accurate enough to consistently grade tumour aggressiveness. Template-guided saturation biopsies are no longer necessary when a high quality state of the art mpMRI is available; however, suspicious lesions should always be confirmed by (targeted) biopsy

Non-Cancerous Abnormalities That Could Mimic Prostate Cancer Like Signal in Multi-Parametric MRI Images

Prostate Cancer (PCa) is the most common non-cutaneous cancer in North American men. Multi-parametric magnatic resonance imaging (mpMRI) has the potential to be used as a non-invasive procedure to predict locations and prognosis of PCa. This study aims to examine non-cancerous pathology lesions and normal histology that could mimic cancer in mpMRI signals. This study includes 19 radical prostatectomy specimens from the London Health Science Centre (LHSC) that were marked with 10 strand-shaped fiducials per specimen which were used as landmarks in histology processing and ex vivo MRI. Initial registration between fiducials on histology and MR images was performed followed by the development of an interactive digital technique for deformable registration of in vivo to ex vivo MRI with digital histopathology images. The relationship between MRI signals and non-cancerous abnormalities that could mimic PCa has not been tested previously in correlation with digital histopathology imaging. The unregistered mp-MRI images are contoured by 4 individual radiology observers according to the Prostate Imaging Reporting and Data System (PI-RADS). Analysis of the radiology data showed prostatic intraepithelial neoplasia (PIN), atrophy and benign prostatic hyperplasia (BPH) as main non-cancerous abnormalities responsible for cancer like signals on mpMRI. This study will help increase the accuracy of detecting PCa and play a role in the diagnosis and classification of confounders that mimic cancer in MR images

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

Toward optimization of target planning for magnetic resonance image-targeted, 3D transrectal ultrasound-guided fusion prostate biopsy

The current clinical standard for diagnosis of prostate cancer (PCa) is 2D transrectal ultrasound (TRUS)-guided biopsy. However, this procedure has a false negative rate of 21-47% and therefore many patients return for repeat biopsies. A potential solution for improving upon this problem is “fusion” biopsy, where magnetic resonance imaging (MRI) is used for PCa detection and localization prior to biopsy. In this procedure, tumours are delineated on pre-procedural MRI and registered to the 3D TRUS needle guidance modality. However, fusion biopsy continues to yield false negative results and there remains a gap in knowledge regarding biopsy needle target selection. Within-tumour needle targets are currently chosen ad hoc by the operating clinician without accounting for guidance system and registration errors. The objective of this thesis was to investigate how the choice of target selection strategy and number of biopsy attempts made per lesion may affect PCa diagnosis in the presence of needle delivery error. A fusion prostate biopsy simulation software platform was developed, which allowed for the investigation of how needle delivery error affects PCa diagnosis and cancer burden estimation. Initial work was conducted using 3D lesions contoured on MRI by collaborating radiologists. The results indicated that more than one core must be taken from the majority of lesions to achieve a sampling probability 95% for a biopsy system with needle delivery error ≥ 3.5 mm. Furthermore, it was observed that the optimal targeting scheme depends on the relative levels of systematic and random needle delivery errors inherent to the specific fusion biopsy system. Lastly, PCa tumours contoured on digital histology images by genitourinary pathologists were used to conduct biopsy simulations. The results demonstrated that needle delivery error has a substantial impact on the biopsy core involvement observed, and that targeting of high-grade lesions may result in higher core involvement variability compared with lesions of all grades. This work represents a first step toward improving the manner in which lesions are targeted using fusion biopsy. Successful integration of these findings into current fusion biopsy system operation could lead to earlier PCa diagnosis with the need for fewer repeat biopsy procedures

Registration of pre-operative lung cancer PET/CT scans with post-operative histopathology images

Non-invasive imaging modalities used in the diagnosis of lung cancer, such as Positron Emission Tomography (PET) or Computed Tomography (CT), currently provide insuffcient information about the cellular make-up of the lesion microenvironment, unless they are compared against the gold standard of histopathology.The aim of this retrospective study was to build a robust imaging framework for registering in vivo and post-operative scans from lung cancer patients, in order to have a global, pathology-validated multimodality map of the tumour and its surroundings.;Initial experiments were performed on tissue-mimicking phantoms, to test different shape reconstruction methods. The choice of interpolator and slice thickness were found to affect the algorithm's output, in terms of overall volume and local feature recovery. In the second phase of the study, nine lung cancer patients referred for radical lobectomy were recruited. Resected specimens were inflated with agar, sliced at 5 mm intervals, and each cross-section was photographed. The tumour area was delineated on the block-face pathology images and on the preoperative PET/CT scans.;Airway segments were also added to the reconstructed models, to act as anatomical fiducials. Binary shapes were pre-registered by aligning their minimal bounding box axes, and subsequently transformed using rigid registration. In addition, histopathology slides were matched to the block-face photographs using moving least squares algorithm.;A two-step validation process was used to evaluate the performance of the proposed method against manual registration carried out by experienced consultants. In two out of three cases, experts rated the results generated by the algorithm as the best output, suggesting that the developed framework outperforms the current standard practice.Non-invasive imaging modalities used in the diagnosis of lung cancer, such as Positron Emission Tomography (PET) or Computed Tomography (CT), currently provide insuffcient information about the cellular make-up of the lesion microenvironment, unless they are compared against the gold standard of histopathology.The aim of this retrospective study was to build a robust imaging framework for registering in vivo and post-operative scans from lung cancer patients, in order to have a global, pathology-validated multimodality map of the tumour and its surroundings.;Initial experiments were performed on tissue-mimicking phantoms, to test different shape reconstruction methods. The choice of interpolator and slice thickness were found to affect the algorithm's output, in terms of overall volume and local feature recovery. In the second phase of the study, nine lung cancer patients referred for radical lobectomy were recruited. Resected specimens were inflated with agar, sliced at 5 mm intervals, and each cross-section was photographed. The tumour area was delineated on the block-face pathology images and on the preoperative PET/CT scans.;Airway segments were also added to the reconstructed models, to act as anatomical fiducials. Binary shapes were pre-registered by aligning their minimal bounding box axes, and subsequently transformed using rigid registration. In addition, histopathology slides were matched to the block-face photographs using moving least squares algorithm.;A two-step validation process was used to evaluate the performance of the proposed method against manual registration carried out by experienced consultants. In two out of three cases, experts rated the results generated by the algorithm as the best output, suggesting that the developed framework outperforms the current standard practice