Tumor complexity scoring helps quantify the degree of anatomic difficulty of surgical intervention. However, it is important to note that while not every tumor is the same, not every patient is the same either. Tumor management should always take into account patient factors as well.
By quantifying degree of complexity, it can:
- Help inform case selection
- Predict ischemia
- Predict complications
- Predict AS
- May predict pathology
As such, it has now been incorporated into AUA guidelines as part of Evaluation & Management, Patient Counseling and Treatment decision making.
Next he began to focus specifically on the Nephrometry score, a geometry based scoring system. It’s a points based system – based on size, endophytic/exophytic nature, nearness to collecting system or sinus, anterior/posterior, and location relative to polar lines. AKA R.E.N.A.L. system – acronym for the key components. The summed score from 4 to 12 can help group renal masses in low (4-6), medium (7-9) or high-risk (10-12).
Beyond parenchymal complexity though, additional features need to be considered: contact surface area (CSA), pelvic and vascular anatomy. CSA determines how much normal parenchyma is affected. It has been correlated with GFR change and blood loss.
Independently, there are additional methods to grade complexity. There are measures of hilar complexity. There are also other well established complexity measuring tools – including PADUA (Eur Urol 2009), C-index (J Urol 2010), ABC score (Eur Urol 2014). However, all of them ultimately have been correlated:
- Complications post NSS – specifically correlated with increased Clavien-dindo classifications
- Complications post ablation
- Warm ischemia time (WIT)
- Operative approach – more likely to pursue open for high complexity lesions
- Choice of treatment – including decision to biopsy, decision to purse active surveillance
- Renal failure
- Operative time
He also focused some time of the impact of fat type of surgical outcomes. Anecdotally, every surgeon knows that there is “good fat” and “bad fat.” Older patients and men have more perinephric fat, often “bad fat”. Fat thickness at renal vein, fat stranding and other measures have been introduced (Mayo clinic Adhesive score) to help predict this prior to surgery.
Lastly, the urinary collecting system is important to evaluate as well. Collecting system anatomy can help predict urine leak. Small intrarenal renal pelvis may predict higher urine leaks due to higher intrarenal pelvis pressures, compared to extrarenal dilated renal pelvis’.
There is much work that can be done in this field to help better counsel patients, prepare for surgery and predict outcomes.
Presented by: Robert Uzzo
Written by: Thenappan Chandrasekar, MD, Clinical Fellow, University of Toronto, Twitter: @tchandra_uromd at the 37th Congress of Société Internationale d’Urologie - October 19-22, 2017- Lisbon, Portugal