Decision support systems for morphology-based diagnosis and prognosis of prostate neoplasms: a methodological approach

Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies

Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate biopsies, computer-aided grading becomes feasible. Computer-aided grading has the potential to improve histopathological grading and treatment selection for prostate cancer. Automated detection of GPs and determination of the grade groups (GG) using a convolutional neural network. In total, 96 prostate biopsies from 38 patients are annotated on pixel-level. Automated detection of GP 3 and GP ≥ 4 in digitized prostate biopsies is performed by re-training the Inception-v3 convolutional neural network (CNN). The outcome of the CNN is subsequently converted into probability maps of GP ≥ 3 and GP ≥ 4, and the GG of the whole biopsy is obtained according to these probability maps. Differentiation between non-atypical and malignant (GP ≥ 3) areas resulted in an accuracy of 92% with a sensitivity and specificity of 90 and 93%, respectively. The differentiation between GP ≥ 4 and GP ≤ 3 was accurate for 90%, with a sensitivity and specificity of 77 and 94%, respectively. Concordance of our automated GG determination method with a genitourinary pathologist was obtained in 65% (κ = 0.70), indicating substantial agreement. A CNN allows for accurate differentiation between non-atypical and malignant areas as defined by GPs, leading to a substantial agreement with the pathologist in defining the GG.

Macroscopic morphology for estimation of malignant potential in pancreatic neuroendocrine neoplasm

PURPOSE

Pancreatic neuroendocrine neoplasm (Pan-NEN) representing approximately 1.3 % of pancreatic malignancy cases in incidence has been a so rare disease that it remains major problem to analyze the malignant potential. The aim of this study was to verify whether the macroscopic morphology of Pan-NEN, a novel pathological classification, contributes to malignant potential.

METHODS

From a total of 86 patients with Pan-NEN, 41 surgical sections obtained from the primary site were classified by their morphology into a simple nodular (SN) group and a non-SN group. The non-SN group was further divided into three subtypes: simple nodular with extranodular growth (SNEG), confluent multinodular (CM), and infiltrative (IF). The clinicopathological features of the SN and the non-SN groups were retrospectively compared.

RESULTS

Overall 5-year survival rates with and without surgical resection were 94 and 48 %, respectively. SN and non-SN types were identified in 21 and 20 patients, respectively. The non-SN group comprised 14 SNEG type, 2 CM type, and 4 IF type. Synchronous lymph node metastases (p = 0.009), synchronous liver metastases (p = 0.048), microinvasion to an adjacent organ (p < 0.001), vascular invasion (p = 0.023), and neural invasion (p = 0.019) were more significant in the non-SN group than in the SN group. As judged by WHO 2004 classification and TNM stages (AJCC and ENETS), non-SN type showed malignant trend (p < 0.05). Moreover, overall 5-year survival rates of SN and non-SN groups were 100 and 84.4 %, respectively (p = 0.048).

CONCLUSIONS

Non-SN tumors may have higher malignant potential than SN tumors.

Recurrent papillary urothelial neoplasm of low malignant potential. Subtle architectural disorder detected by quantitative analysis in DAXX-immunostained tissue sections

The aim of the study was to identify subtle changes in the so-called architectural predominant order in nonrecurrent and recurrent papillary urothelial neoplasm of low malignant potential (PUNLMP). Quantitative analysis was performed with a software package written in LabVIEW (National Instruments, Austin, TX) in DAXX-immunostained tissue sections. Twelve cases of PUNLMP with papillary fronds sectioned lengthwise through the core were investigated and subdivided as follows: 7 nonrecurrent and 5 recurrent PUNLMP cases. Six cases of normal urothelium (NU) were included. When an epithelial thickness threshold is set at 108 μm (ie, 400 pixels), there is a complete separation between NU and PUNLMP; however, nonrecurrent and recurrent cases fall in the same range of thickness. In setting a nuclear elongation factor threshold at 2.1, there are differences between the 2 PUNLMP groups, recurrent PUNLMP and NU cases, showing a somewhat similar proportion of elongated nuclei. The nuclear orientation separates nonrecurrent from recurrent PUNLMP groups; however, NU cases do not appear as a separate group from the 2 PUNLMP groups. In combining epithelial thickness, nuclear elongation, and orientation in a multivariate analysis, the 2 PUNLMP groups appear separate between them and from NU. NU is less thickened than the 2 PUNLMP groups and shows a combination of elongated and less elongated nuclei. Elongated nuclei are more numerous in nonrecurrent PUNLMP, whereas the nuclei in recurrent PUNLMP are less elongated and less polarized than in the other group. Such finding can be used routinely to identify those PUNLMP patients who will have recurrence.

Nuclear morphometry, nucleomics and prostate cancer progression

Prostate cancer (PCa) results from a multistep process. This process includes initiation, which occurs through various aging events and multiple insults (such as chronic infection, inflammation and genetic instability through reactive oxygen species causing DNA double-strand breaks), followed by a multistep process of progression. These steps include several genetic and epigenetic alterations, as well as alterations to the chromatin structure, which occur in response to the carcinogenic stress-related events that sustain proliferative signaling. Events such as evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis are readily observed. In addition, in conjunction with these critical drivers of carcinogenesis, other factors related to the etiopathogenesis of PCa, involving energy metabolism and evasion of the immune surveillance system, appear to be involved. In addition, when cancer spread and metastasis occur, the 'tumor microenvironment' in the bone of PCa patients may provide a way to sustain dormancy or senescence and eventually establish a 'seed and soil' site where PCa proliferation and growth may occur over time. When PCa is initiated and progression ensues, significant alterations in nuclear size, shape and heterochromatin (DNA transcription) organization are found, and key nuclear transcriptional and structural proteins, as well as multiple nuclear bodies can lead to precancerous and malignant changes. These series of cellular and tissue-related malignancy-associated events can be quantified to assess disease progression and management.

SurfaceSlide: a multitouch digital pathology platform

Background

Digital pathology provides a digital environment for the management and interpretation of pathological images and associated data. It is becoming increasing popular to use modern computer based tools and applications in pathological education, tissue based research and clinical diagnosis. Uptake of this new technology is stymied by its single user orientation and its prerequisite and cumbersome combination of mouse and keyboard for navigation and annotation.

Methodology

In this study we developed SurfaceSlide, a dedicated viewing platform which enables the navigation and annotation of gigapixel digitised pathological images using fingertip touch. SurfaceSlide was developed using the Microsoft Surface, a 30 inch multitouch tabletop computing platform. SurfaceSlide users can perform direct panning and zooming operations on digitised slide images. These images are downloaded onto the Microsoft Surface platform from a remote server on-demand. Users can also draw annotations and key in texts using an on-screen virtual keyboard. We also developed a smart caching protocol which caches the surrounding regions of a field of view in multi-resolutions thus providing a smooth and vivid user experience and reducing the delay for image downloading from the internet. We compared the usability of SurfaceSlide against Aperio ImageScope and PathXL online viewer.

Conclusion

SurfaceSlide is intuitive, fast and easy to use. SurfaceSlide represents the most direct, effective and intimate human–digital slide interaction experience. It is expected that SurfaceSlide will significantly enhance digital pathology tools and applications in education and clinical practice.