Improving the diagnostic yield of prostate cancer

Khan, M.A., 2021. Improving the diagnostic yield of prostate cancer. PhD, Nottingham Trent University.

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Although the introduction of PSA as a biomarker for detecting prostate cancer has enabled the disease to be diagnosed at an earlier stage, the primary drawback to PSA as a biomarker and basis for prostate cancer screening and diagnosis is its lack of specificity. To address this issue, PSA density, velocity and percent free PSA have all been assessed as potential approaches for improving the diagnosis of prostate cancer, albeit with variable and limited success.

These challenges and the desire to improve the management and treatment of patients with prostate cancer prompted studies that investigated the capacity of measuring pro- PSA levels to improve diagnostic yield over that of PSA alone. Our study confirmed that combining pro-PSA, total and percent free PSA improved the specificity of prostate cancer detection from that of 23% for PSA alone to 44%. To further improve the utility of pro-PSA, we subsequently determined that the ratio of pro-PSA and benign-PSA can identify the presence of prostate cancer with greater accuracy when the percent free PSA is below 15%.

Traditionally, prostate cancer has been definitively diagnosed by performing transrectal ultrasound (TRUS) guided prostate biopsies. However, such a biopsy technique has a cancer detection rate of less than 30% in a benign feeling prostate. In addition, when TRUS biopsies are repeated due to rising PSA, the cancer detection rate significantly reduces to below 10% for men undergoing a third set of such biopsies. We therefore undertook a study to assess the diagnostic strength of the transperineal template prostate biopsies (TPTPB) in this group of men and demonstrated that the cancer detection rate of the TPTPB is significantly better than that of the TRUS biopsy (52%-68%). We subsequently directly compared TRUS against TPTPB in biopsy naïve men and revealed that TPTPB significantly outperforms TRUS in prostate cancer detection (60% versus 32%).

Based on the established concept that there is a reciprocal relationship between cancer and the immune system, and the proposition that the presence of cancer will influence the phenotype of immune cells in the blood which can be detected by profiling peripheral blood mononuclear cells, we subsequently investigated whether profiling the phenotype of immune cells in the blood could further improve our ability not only to detect prostate cancer, but clinically significant prostate cancer. For this, flow cytometric immune profiling of the peripheral blood from men with benign prostate disease and patients with confirmed prostate cancer identified phenotypic features 'fingerprints' within the lymphocyte populations which, when incorporated into machine learning based algorithms, can be used to distinguish between the presence of benign prostate disease and prostate cancer. Furthermore, we identified a panel of eight natural killer (NK) cell phenotypic features which can be used to very accurately differentiate between high risk and low/intermediate risk prostate cancer when incorporated into machine learning algorithms.

In summary, over the past two decades, my work has not only resulted in an improvement in the utility of PSA as a biomarker for the detection of prostate, but I also demonstrated that performing transperineal prostate biopsies significantly improves prostate cancer detection. Furthermore, we have recently revealed that our immune system can aid us to differentiate between indolent and clinically significant prostate cancer. Taken together, this programme of work has greatly impacted clinical practice.

Item Type: Thesis
Description: A thesis submitted in partial fulfilment of the requirements of Nottingham Trent University for the degree of 'Doctor of Philosophy by Published Work'.
Creators: Khan, M.A.
Date: October 2021
Rights: The copyright in this work is held by the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed to the author.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 16 Jun 2022 13:40
Last Modified: 16 Jun 2022 13:40

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