Prevention of infectious complications after prostate biopsy procedure

Togo, Yoshikazu; Yamamoto, Shingo

doi:10.1111/iju.13369

Cited by 16 publications

(7 citation statements)

References 76 publications

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“…Traditionally, the PSA cut-off value for PCa detection was set to 4 ng/mL, but in this case, the specificity has been shown to be 21%, and sensitivity is 91% [15]. This can lead to high false positive rates and unnecessary prostate biopsies, and finally, to an increase in the frequency of complications due to unnecessary prostate biopsies [16]. Because of these limitations of PSA, many other additional tests have been suggested, e.g., free PSA, prostate health index, 4Kscore, PCA3, Select MDx, and ExoDx Prostate [17][18][19][20][21][22].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Agreement between Two Difference Prostate-Specific Antigen Assay Modalities

Chung

Park

Cho

et al. 2021

Biology

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There is controversy over the usefulness of prostate-specific antigen (PSA) as a prostate cancer (PCa) biomarker. This controversy arises when there are differences in the results of PSA assay modalities. In this study, which aimed to evaluate a proper validation between the two PSA assay modalities, the agreement between the results of the two modalities was analyzed. PSA examinations were conducted using two PSA assay modalities in 4810 patients. The intra-class correlation coefficient (ICC) and weighted kappa analysis were used to evaluate the agreement between the two assay modalities. A linear regression was performed to evaluate the association between the two assay modalities. According to ICC values (ICC: 0.999, p < 0.001) and weighted kappa analysis values (kappa: 0.951, alpha’s standard error (ASE): 0.001, p < 0.0001), the agreement between the assay modalities was rated as excellent. However, the strength of agreement was poor in the following PSA sub-groups: 0.05–0.1 ng/mL (ICC: 0.281, p = 0.0860); 0.15–0.2 ng/mL (ICC: 0.288, p = 0.0036); 1.5–2.0 ng/mL (ICC: 0.360, p = 0.0860); and 2.0–2.5 ng/mL (ICC: 0.303, p = 0.0868). In linear regression analysis, when modality B PSA yielded a value of 0.2 ng/mL, the expected value for modality A was 0.258 ng/mL (95% CI: 0.255–0.260), and when modality B PSA yielded a value of 4 ng/mL, the expected value for modality A was 3.192 ng/mL (95% CI: 3.150–3.235). The difference in the PSA values between the two PSA assay modalities is confirmed, and this difference may be clinically meaningful.

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Section: Discussionmentioning

confidence: 99%

Assessment of Agreement between Two Difference Prostate-Specific Antigen Assay Modalities

Chung

Park

Cho

et al. 2021

Biology

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“…In addition, this may increase the complications of prostate biopsy, such as acute prostatitis. 13 Moreover, patients suffer mental and physical stress. 14 Lowering the PSA cut-off value is still controversial as adverse effects will increase.…”

Section: Discussionmentioning

confidence: 99%

Strategy for Prostate Cancer Patients with Low Prostate Specific Antigen Level (2.5 to 4.0 ng/mL)

Chung

Song

et al. 2020

J Korean Med Sci

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Background To evaluate the strategy for detection of prostate cancer (PCa) with low prostate specific antigen (PSA) level (2.5–4.0 ng/mL), prostate biopsy patients with low PSA were assessed. We evaluated the risk of low PSA PCa and the strategy for screening low-PSA patients. Methods We retrospectively analyzed the patients who underwent prostate biopsy with low PSA level. Baseline characteristics, PSA level before prostate biopsy, prostate volume, prostate specific antigen density (PSAD), and pathological data were assessed. Results Among the 1986 patients, 24.97% were diagnosed with PCa. The PSAD was 0.12 ± 0.04 ng/mL 2 in the PCa-diagnosed group and 0.10 ± 0.04 ng/mL 2 in non-cancer-diagnosed group ( P < 0.001). Of the 496 patients diagnosed with PCa, 302 (60.89%) were in the intermediate- or high-risk group. PSAD was 0.13 ± 0.04 ng/mL 2 in the intermediate- or high-risk group and 0.11 ± 0.03 ng/mL 2 in the very low- and low-risk group ( P < 0.001). Of 330 patients who underwent radical prostatectomy, 85.15% were diagnosed as having significant cancer. There was significant correlation between PSAD and PCa ( r = 0.294, P < 0.001). PSAD with a specificity of 80.00% of a clinically significant cancer diagnosis was assessed at 0.1226 ng/mL 2 . Conclusion The PCa detection rate in the low-PSA group was not lower than that of previous studies of patients with PSA from 4.0 to 10.0 ng/mL. Further, it may be helpful to define a strategy for PCa detection using PSAD in the low-PSA group.

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“…It is typically performed by using an ultrasound probe that is placed in the rectum which than produces a real-time image of the prostate. The samples produced from this procedure are then taken to a pathologist for evaluation and grading [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods

et al. 2022

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One of the most precise methods to detect prostate cancer is by evaluation of a stained biopsy by a pathologist under a microscope. Regions of the tissue are assessed and graded according to the observed histological pattern. However, this is not only laborious, but also relies on the experience of the pathologist and tends to suffer from the lack of reproducibility of biopsy outcomes across pathologists. As a result, computational approaches are being sought and machine learning has been gaining momentum in the prediction of the Gleason grade group. To date, machine learning literature has addressed this problem by using features from magnetic resonance imaging images, whole slide images, tissue microarrays, gene expression data, and clinical features. However, there is a gap with regards to predicting the Gleason grade group using DNA sequences as the only input source to the machine learning models. In this work, using whole genome sequence data from South African prostate cancer patients, an application of machine learning and biological experiments were combined to understand the challenges that are associated with the prediction of the Gleason grade group. A series of machine learning binary classifiers (XGBoost, LSTM, GRU, LR, RF) were created only relying on DNA sequences input features. All the models were not able to adequately discriminate between the DNA sequences of the studied Gleason grade groups (Gleason grade group 1 and 5). However, the models were further evaluated in the prediction of tumor DNA sequences from matched-normal DNA sequences, given DNA sequences as the only input source. In this new problem, the models performed acceptably better than before with the XGBoost model achieving the highest accuracy of 74 ± 01, F1 score of 79 ± 01, recall of 99 ± 0.0, and precision of 66 ± 0.1.

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Prevention of infectious complications after prostate biopsy procedure

Cited by 16 publications

References 76 publications

Assessment of Agreement between Two Difference Prostate-Specific Antigen Assay Modalities

Assessment of Agreement between Two Difference Prostate-Specific Antigen Assay Modalities

Strategy for Prostate Cancer Patients with Low Prostate Specific Antigen Level (2.5 to 4.0 ng/mL)

Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods

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