Deep learning in prostate cancer diagnosis and Gleason grading in histopathology images: An extensive study

Linkon, Ali Hasan Md.; Labib, Md. Mahir; Hasan, Tarik; Hossain, M. Tahmid; Marium-E-Jannat,

doi:10.1016/j.imu.2021.100582

Cited by 40 publications

(21 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Such fast scanning rates suggest that our proposed deep UV microscopy method is quite feasible for routine clinical practice and potentially surgical pathology. This approach could also be combined with other state-of-the-art structure-based neural networks recently introduced to help automate diagnosis 67 , 81 .…”

Section: Discussionmentioning

confidence: 99%

Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains

Soltani

Ojaghi

Qiao

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. Here we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease, thus providing a new tool to help address this important challenge. We find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that provides unique structural insight (i.e., molecular maps or “optical stains") of thin tissue sections with subcellular (nanoscale) resolution. We show that this phenotypical continuum can also be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. In addition to providing several novel “optical stains” with contrast for disease, we also adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images, thus providing multiple stains (including the gold-standard H&E) from the same unlabeled specimen. Agreement between the virtual H&E images and the H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis.

show abstract

Section: Discussionmentioning

confidence: 99%

Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains

Soltani

Ojaghi

Qiao

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Automated deep learning systems have delivered promising results from histopathological images to accurate grading of PCa. Many studies have shown that deep learning strategies can achieve better outcomes than simpler systems that make use of pathology samples [ 29 ]. There are other examples of algorithms based on artificial intelligence and machine learning in PCa that could be an excellent addition to our work [ 30 , 31 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

Usefulness of Collaborative Work in the Evaluation of Prostate Cancer from MRI

Mata

Walker

Oliver

et al. 2022

Clinics and Practice

View full text Add to dashboard Cite

The aim of this study is to show the usefulness of collaborative work in the evaluation of prostate cancer from T2-weighted MRI using a dedicated software tool. The variability of annotations on images of the prostate gland (central and peripheral zones as well as tumour) by two independent experts was firstly evaluated, and secondly compared with a consensus between these two experts. Using a prostate MRI database, experts drew regions of interest (ROIs) corresponding to healthy prostate (peripheral and central zones) and cancer. One of the experts then drew the ROI with knowledge of the other expert’s ROI. The surface area of each ROI was used to measure the Hausdorff distance and the Dice coefficient was measured from the respective contours. They were evaluated between the different experiments, taking the annotations of the second expert as the reference. The results showed that the significant differences between the two experts disappeared with collaborative work. To conclude, this study shows that collaborative work with a dedicated tool allows consensus between expertise in the evaluation of prostate cancer from T2-weighted MRI.

show abstract

“…While not all digital pathology applications fit neatly into the two categories above, we may still distinguish between outputs that are amenable to visual verification and those that are not. For example, numerous AI-based methods have been developed for Gleason grading [24,25]. Some include elements of object detection and quantification before the application of AI, whereas others operate more directly on the pixels without explicit detection; either way, a pathologist can judge the algorithm's performance by comparing the final generated gradings with their own assessments.…”

Section: Hybrid Approachesmentioning

confidence: 99%

Developing image analysis methods for digital pathology

Bankhead

2022

The Journal of Pathology

View full text Add to dashboard Cite

The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology being described across many publications, few become widely adopted and many are not applied in more than a single study. The explanation is often straightforward: software implementing the method is simply not available, or is too complex, incomplete, or dataset‐dependent for others to use. The result is a disconnect between what seems already possible in digital pathology based upon the literature, and what actually is possible for anyone wishing to apply it using currently available software. This review begins by introducing the main approaches and techniques involved in analysing pathology images. I then examine the practical challenges inherent in taking algorithms beyond proof‐of‐concept, from both a user and developer perspective. I describe the need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms, and argue that openness, implementation, and usability deserve more attention among digital pathology researchers. The review ends with a discussion about how digital pathology could benefit from interacting with and learning from the wider bioimage analysis community, particularly with regard to sharing data, software, and ideas. © 2022 The Author. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

show abstract

Deep learning in prostate cancer diagnosis and Gleason grading in histopathology images: An extensive study

Cited by 40 publications

References 67 publications

Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains

Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains

Usefulness of Collaborative Work in the Evaluation of Prostate Cancer from MRI

Developing image analysis methods for digital pathology

Contact Info

Product

Resources

About