Biobanks – A Source of Large Biological Data Sets: Open Problems and Future Challenges

Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading

Background

Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether and to what extent A.I. grading translates to better prognostication.

Methods

In this study, we developed a system to predict prostate cancer-specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2807 prostatectomy cases from a single European center with 5–25 years of follow-up (median: 13, interquartile range 9–17).

Results

Here, we show that the A.I.’s risk scores produced a C-index of 0.84 (95% CI 0.80–0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. has a C-index of 0.82 (95% CI 0.78–0.85). On the subset of cases with a GG provided in the original pathology report (n = 1517), the A.I.’s C-indices are 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95% CI 0.71–0.86) for GG obtained from the reports. These represent improvements of 0.08 (95% CI 0.01–0.15) and 0.07 (95% CI 0.00–0.14), respectively.

Conclusions

Our results suggest that A.I.-based Gleason grading can lead to effective risk stratification, and warrants further evaluation for improving disease management.

Gleason grading is the process by which pathologists assess the morphology of prostate tumors. The assigned Grade Group tells us about the likely clinical course of people with prostate cancer and helps doctors to make decisions on treatment. The process is complex and subjective, with frequent disagreement amongst pathologists. In this study, we develop and evaluate an approach to Gleason grading based on artificial intelligence, rather than pathologists’ assessment, to predict risk of dying of prostate cancer. Looking back at tumors and data from 2,807 people diagnosed with prostate cancer, we find that our approach is better at predicting outcomes compared to grading by pathologists alone. These findings suggest that artificial intelligence might help doctors to accurately determine the probable clinical course of people with prostate cancer, which, in turn, will guide treatment.

Wulczyn et al. utilise a deep learning-based Gleason grading model to predict prostate cancer-specific mortality in a retrospective cohort of radical prostatectomy patients. Their model enables improved risk stratification compared to pathologists’ grading and demonstrates the potential for computational pathology in the management of prostate cancer.

A qualitative study of participants' views on re-consent in a longitudinal biobank

BACKGROUND

Biomedical research increasingly relies on long-term studies involving use and re-use of biological samples and data stored in large repositories or "biobanks" over lengthy periods, often raising questions about whether and when a re-consenting process should be activated. We sought to investigate the views on re-consent of participants in a longitudinal biobank.

METHODS

We conducted a qualitative study involving interviews with 24 people who were participating in a longitudinal biobank. Their views were elicited using a semi-structured interview schedule and scenarios based on a hypothetical biobank. Data analysis was based on the constant comparative method.

RESULTS

What participants identified as requiring new consent was not a straightforward matter predictable by algorithms about the scope of the consent, but instead was contingent. They assessed whether proposed new research implied a fundamental alteration in the underlying character of the biobank and whether specific projects were within the scope of the original consent. What mattered most to them was that the cooperative bargain into which they had entered was maintained in good faith. They saw re-consent as one important safeguard in this bargain. In determining what required re-consent, they deployed two logics. First, they used a logic of boundaries, where they sought to detect any possible rupture with their existing framework of cooperation. Second, they used a logic of risk, where they assessed proposed research for any potential threats for them personally or the research endeavour. When they judged that a need for re-consent had been activated, participants saw the process as way of re-actualising and renewing the cooperative bargain.

CONCLUSIONS

Participants' perceptions of research as a process of mutual co-operation between volunteer and researcher were fundamental to their views on consent. Consenting arrangements for biobanks should respect the cooperative values that are important to participants, recognise the two logics used by research volunteers, and avoid rigidity. Agility may be favoured by tiered consent combined with strong oversight mechanisms; this approach requires evaluation.

Studies of the role of a smart web for precision medicine supported by biobanking

Both the extraction of medical knowledge from data mining many patient records and from authoritative natural language text on the Internet are important for clinical decision support and biomedical research. The samples in biobanks represent a further kind of information repository of recognized increasing importance, so mechanisms being developed for a smart web for medicine should take them into account. While this paper is primarily a review of Quantum Universal Exchange Language as an XML extension to enable a future smart web for healthcare and biomedicine, it is the first time that we have discussed the connection with biobanks and the design of Quantum Universal Exchange Language's XML-like tags to support their use.