Digital versus light microscopy assessment of surgical margin status after radical prostatectomy.
Metka VolavšekAna BlancaRodolfo MontironiLiang ChengMaria R RaspolliniNuno VauJorge FonsecaFrancesco PiercontiAntonio López-BeltranPublished in: Virchows Archiv : an international journal of pathology (2018)
Positive surgical margin (PSM) extension reported as focal or non-focal/extensive is an important pathologic prognostic parameter after radical prostatectomy. Likewise, there is limited or no agreement on how to measure and what the best cut-off points to be used in practice are. We hypothesized that digital microscopy (DM) would potentially provide a more objective way to measure PSM and better define its clinical significance. To further our knowledge, we have evaluated PSM status in 107 laparoscopic radical prostatectomies using digital and conventional light microscopy (LM). DM evaluation detected three additional PSM cases, but no differences were seen (LM vs DM; p = 0.220). Mean linear measurement correlated to biochemical recurrence (BR) (LM, p = 0.002; DM, p = 0.001). ROC analysis identified a cut-off point to assess linear measurement by LM (3.5 mm) or DM (3.2 mm), but only digital measurement was significant for BR-free survival. Our study also evaluated a cut-off ≤ 3 mm that was associated to BR using LM (p = 0.023) or DM (p = 0.001). Finally, the number of paraffin blocks bearing PSM correlated with BR (p < 0.001) status with either LM or DM. In conclusion, DM produces similar data than LM but shows more accurate measurements. Reporting of PSM with score of ≤ 3 vs. > 3 mm linear extent using LM (3.2 mm if digital microscopy is applied) might represent an important prognostic feature after radical prostatectomy. Alternatively, reporting the number of blocks with PSM 1 vs. 2 or more might also provide important prognostic data in practice.
Keyphrases
- radical prostatectomy
- prostate cancer
- high resolution
- single molecule
- free survival
- glycemic control
- healthcare
- high speed
- high throughput
- primary care
- electronic health record
- type diabetes
- squamous cell carcinoma
- machine learning
- big data
- emergency department
- adipose tissue
- radiation therapy
- weight loss
- locally advanced
- robot assisted