Active surveillance program for prostate cancer: an update of the Johns Hopkins experience

Billis, Athanase

doi:10.1590/s1677-55382011000200021

Cited by 3 publications

(5 citation statements)

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“…Another important finding of our study is the low progression rate of 13.9% over a median follow-up of 52 months. This compares favourably with previous studies reporting higher progression rates between 20 and 36% over shorter follow-up periods (1.8-3.9 years) [25][26][27][28][29] and likely reflects the stringent enrolment criteria employed, incorporating MRI and early re-biopsy for discordant historadiological findings.…”

Section: Discussionsupporting

confidence: 74%

MRI-derived PRECISE scores for predicting pathologically-confirmed radiological progression in prostate cancer patients on active surveillance

et al. 2020

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Objectives To assess the predictive value and correlation to pathological progression of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) scoring system in the follow-up of prostate cancer (PCa) patients on active surveillance (AS). Methods A total of 295 men enrolled on an AS programme between 2011 and 2018 were included. Baseline multiparametric magnetic resonance imaging (mpMRI) was performed at AS entry to guide biopsy. The follow-up mpMRI studies were prospectively reported by two sub-specialist uroradiologists with 10 years and 13 years of experience. PRECISE scores were dichotomized at the cut-off value of 4, and the sensitivity, specificity, positive predictive value and negative predictive value were calculated. Diagnostic performance was further quantified by using area under the receiver operating curve (AUC) which was based on the results of targeted MRI-US fusion biopsy. Univariate analysis using Cox regression was performed to assess which baseline clinical and mpMRI parameters were related to disease progression on AS. Results Progression rate of the cohort was 13.9% (41/295) over a median follow-up of 52 months. With a cut-off value of category ≥ 4, the PRECISE scoring system showed sensitivity, specificity, PPV and NPV for predicting progression on AS of 0.76, 0.89, 0.52 and 0.96, respectively. The AUC was 0.82 (95% CI = 0.74–0.90). Prostate-specific antigen density (PSA-D), Likert lesion score and index lesion size were the only significant baseline predictors of progression (each p < 0.05). Conclusion The PRECISE scoring system showed good overall performance, and the high NPV may help limit the number of follow-up biopsies required in patients on AS. Key Points • PRECISE scores 1–3 have high NPV which could reduce the need for re-biopsy during active surveillance. • PRECISE scores 4–5 have moderate PPV and should trigger either close monitoring or re-biopsy. • Three baseline predictors (PSA density, lesion size and Likert score) have a significant impact on the progression-free survival (PFS) time.

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Section: Discussionsupporting

confidence: 74%

MRI-derived PRECISE scores for predicting pathologically-confirmed radiological progression in prostate cancer patients on active surveillance

et al. 2020

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“…ML methods can enable automated and early identification of diseases, with performance comparable to that of human experts 16 . Professionals could use a developed algorithm with good diagnostic capacity as a valuable and cost‐effective tool to assist in the diagnosis of challenging entities, allowing adequate and timely treatment 9,17 …”

Section: Discussionmentioning

confidence: 99%

“…16 Professionals could use a developed algorithm with good diagnostic capacity as a valuable and cost-effective tool to assist in the diagnosis of challenging entities, allowing adequate and timely treatment. 9,17 In the medical literature focusing on head and neck pathologies, there is some evidence of a great advance in the use of AI approaches for the diagnosis of OSCC. [18][19][20][21][22][23] 26 Additionally, only a few studies used radiomic data for intraosseous lesions detections, segmentation, or diagnosis 27 but none investigated approaches using histopathological slides for OT diagnosis.…”

Section: Cnn Trainingmentioning

confidence: 99%

“…8 In the mandible, resection may be complete (segmental osteotomy or mandibulectomy) or partial (maintaining the bone continuity). 9 In the maxilla, radical treatment comprises maxillectomy, which is classified into partial or total according to the extent of the resection. According to a meta-analysis study, 10 the combined recurrence rate for ameloblastomas was 8% after radical treatment and 41% after conservative treatment.…”

Section: Introductionmentioning

confidence: 99%

“…Ameloblastoma may recur after surgical resection 8 . In the mandible, resection may be complete (segmental osteotomy or mandibulectomy) or partial (maintaining the bone continuity) 9 . In the maxilla, radical treatment comprises maxillectomy, which is classified into partial or total according to the extent of the resection.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas

Giraldo‐Roldan,

Ribeiro,

Araújo

et al. 2023

J Oral Pathology Medicine

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BackgroundOdontogenic tumors (OT) are composed of heterogeneous lesions, which can be benign or malignant, with different behavior and histology. Within this classification, ameloblastoma and ameloblastic carcinoma (AC) represent a diagnostic challenge in daily histopathological practice due to their similar characteristics and the limitations that incisional biopsies represent. From these premises, we wanted to test the usefulness of models based on artificial intelligence (AI) in the field of oral and maxillofacial pathology for differential diagnosis. The main advantages of integrating Machine Learning (ML) with microscopic and radiographic imaging is the ability to significantly reduce intra‐and inter observer variability and improve diagnostic objectivity and reproducibility.MethodsThirty Digitized slides were collected from different diagnostic centers of oral pathology in Brazil. After performing manual annotation in the region of interest, the images were segmented and fragmented into small patches. In the supervised learning methodology for image classification, three models (ResNet50, DenseNet, and VGG16) were focus of investigation to provide the probability of an image being classified as class0 (i.e., ameloblastoma) or class1 (i.e., Ameloblastic carcinoma).ResultsThe training and validation metrics did not show convergence, characterizing overfitting. However, the test results were satisfactory, with an average for ResNet50 of 0.75, 0.71, 0.84, 0.65, and 0.77 for accuracy, precision, sensitivity, specificity, and F1‐score, respectively.ConclusionsThe models demonstrated a strong potential of learning, but lack of generalization ability. The models learn fast, reaching a training accuracy of 98%. The evaluation process showed instability in validation; however, acceptable performance in the testing process, which may be due to the small data set. This first investigation opens an opportunity for expanding collaboration to incorporate more complementary data; as well as, developing and evaluating new alternative models.

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Prostate cancer mortality and metastasis under different biopsy frequencies in North American active surveillance cohorts

Lange

Laviana

Penson

et al. 2019

Cancer

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BACKGROUND: Active surveillance (AS) is an accepted means of managing low-risk prostate cancer. Because of the rarity of downstream events, data from existing AS cohorts cannot yet address how differences in surveillance intensity affect metastasis and mortality. This study projected the comparative benefits of different AS schedules in men diagnosed with prostate cancer who had Gleason score (GS) ≤6 disease and risk profiles similar to those in North American AS cohorts. METHODS: Times of GS upgrading were simulated based on AS data from the University of Toronto, Johns Hopkins University, the University of California at San Francisco, and the Canary Pass Active Surveillance Cohort. Times to metastasis and prostate cancer death, informed by models from the Scandinavian Prostate Cancer Group 4 trial, were projected under biopsy surveillance schedules ranging from watchful waiting to annual biopsies. Outcomes included the risk of metastasis, the risk of death, remaining life-years (LYs), and quality-adjusted LYs. RESULTS: Compared with watchful waiting, AS biopsies reduced the risk of prostate cancer metastasis and prostate cancer death at 20 years by 1.4% to 3.3% and 1.0% to 2.4%, respectively; and 5-year biopsies reduced the risk of metastasis and prostate cancer death by 1.0% to 2.4% and 0.6% to 1.6%, respectively. There was little difference between annual and 5-year biopsy schedules in terms of LYs (range of differences, 0.04-0.16 LYs) and qualityadjusted LYs (range of differences, −0.02 to 0.09 quality-adjusted LYs). CONCLUSIONS: Among men diagnosed with GS ≤6 prostate cancer, obtaining a biopsy every 3 or 4 years appears to be an acceptable alternative to more frequent biopsies. Reducing surveillance intensity for those who have a low risk of progression reduces the number of biopsies while preserving the benefit of more frequent schedules. Cancer 2020;126:583-592.

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Active surveillance program for prostate cancer: an update of the Johns Hopkins experience

Cited by 3 publications

References 1 publication

MRI-derived PRECISE scores for predicting pathologically-confirmed radiological progression in prostate cancer patients on active surveillance

MRI-derived PRECISE scores for predicting pathologically-confirmed radiological progression in prostate cancer patients on active surveillance

Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas

Prostate cancer mortality and metastasis under different biopsy frequencies in North American active surveillance cohorts

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