Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology.Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials

RegiSTORM: channel registration for multi-color stochastic optical reconstruction microscopy

Background: Stochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions. Results: We developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error (TRE) with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software. Conclusions: The developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at https://github.com/oystein676/RegiSTORM.git and DOI:10.5281/zenodo.5509861 (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux)

eLearning and Embryology: Designing an Application to Improve 3D Comprehension of Embryological Structures

Embryology and histology are subjects that are viewed as particularly challenging by students in higher education. This negative perception is the result of many factors such as restricted access to lab facilities, lack of allocated time to these labs, and the complexity of the subject itself. One main factor that influences this viewpoint is the difficulty of grasping 3D orientation of sectioned tissues, especially regarding embryology. Attempts have been made previously to create alternative teaching methods to help alleviate these issues, but few have explored 3D visualisation. We aimed to address these issues by creating 3D embryological reconstructions from serial histology sections of a sheep embryo. These were deployed in a mobile application that allowed the user to explore the original sections in sequence, alongside the counterpart 3D model. The application was tested against a currently available eHistology programme on a cohort of life sciences graduates (n = 14) through qualitative surveys and quantitative testing through labelling and orientation-based tests. The results suggest that using a 3D modality such as the one described here significantly improves student comprehension of orientation of slides compared to current methods (p = 0.042). Furthermore, the developed application was deemed more interesting, useful, and usable than current eHistology tools (p < 0.05). Modalities such as that developed here could therefore provide a more effective approach to learning these challenging subjects potentially increasing student engagement with embryology and histology

Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading

The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68–0.84) and 0.50 (range 0.40–0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems

eLearning and embryology: Designing an application to improve 3D comprehension of embryological structures

Embryology and histology are subjects that are viewed as particularly challenging by students in higher education. This negative perception is the result of many factors such as restricted access to lab facilities, lack of allocated time to these labs, and the complexity of the subject itself. One main factor that influences this viewpoint is the difficulty of grasping 3D orientation of sectioned tissues, especially regarding embryology. Attempts have been made previously to create alternative teaching methods to help alleviate these issues, but few have explored 3D visualisation. We aimed to address these issues by creating 3D embryological reconstructions from serial histology sections of a sheep embryo. These were deployed in a mobile application that allowed the user to explore the original sections in sequence, alongside the counterpart 3D model. The application was tested against a currently available eHistology programme on a cohort of life sciences graduates (n = 14) through qualitative surveys and quantitative testing through labelling and orientation-based tests. The results suggest that using a 3D modality such as the one described here significantly improves student comprehension of orientation of slides compared to current methods (p = 0.042). Furthermore, the developed application was deemed more interesting, useful, and usable than current eHistology tools (p &lt; 0.05). Modalities such as that developed here could therefore provide a more effective approach to learning these challenging subjects potentially increasing student engagement with embryology and histology

Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study

Background: An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading. Methods: We digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50–69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa. Findings: The AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994–0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972–0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95–0·97) for the independent test dataset and 0·87 (0·84–0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60–0·73). Interpretation: An AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist. Funding: Swedish Research Council, Swedish Cancer Society, Swedish eScience Research Center, EIT Health