Active Surveillance for Small Renal Masses in Young Patients

A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma

BACKGROUND

A substantial proportion of patients undergo treatment for renal masses where active surveillance or observation may be more appropriate.

OBJECTIVE

To determine whether radiomic-based machine learning platforms can distinguish benign from malignant renal masses.

DESIGN, SETTING, AND PARTICIPANTS

A prospectively maintained single-institutional renal mass registry was queried to identify patients with a computed tomography-proven clinically localized renal mass who underwent partial or radical nephrectomy.

INTERVENTION

Radiomic analysis of preoperative scans was performed. Clinical and radiomic variables of importance were identified through decision tree analysis, which were incorporated into Random Forest and REAL Adaboost predictive models.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary outcome was the degree of congruity between the virtual diagnosis and final pathology. Subanalyses were performed for small renal masses and patients who had percutaneous renal mass biopsies as part of their workup. Receiver operating characteristic curves were used to evaluate each model's discriminatory function.

RESULTS AND LIMITATIONS

A total of 684 patients met the selection criteria. Of them, 76% had renal cell carcinoma; 57% had small renal masses, of which 73% were malignant. Predictive modeling differentiated benign pathology from malignant with an area under the curve (AUC) of 0.84 (95% confidence interval [CI] 0.79-0.9). In small renal masses, radiomic analysis yielded a discriminatory AUC of 0.77 (95% CI 0.69-0.85). When negative and nondiagnostic biopsies were supplemented with radiomic analysis, accuracy increased from 83.3% to 93.4%.

CONCLUSIONS

Radiomic-based predictive modeling may distinguish benign from malignant renal masses. Clinical factors did not substantially improve the diagnostic accuracy of predictive models. Enhanced diagnostic predictability may improve patient selection before surgery and increase the utilization of active surveillance protocols.

PATIENT SUMMARY

Not all kidney tumors are cancerous, and some can be watched. We evaluated a new method that uses radiographic features invisible to the naked eye to distinguish benign masses from true cancers and found that it can do so with acceptable accuracy.

CT-guided cryoablation of renal cancer: radiation burden and the associated risk of secondary cancer from procedural- and follow-up imaging

OBJECTIVES

To estimate radiation dose and the associated risk of secondary cancer risk related to percutaneous cryoablation (PCA) and follow-up imaging in a cohort of patients treated for small renal masses (SRMs).

METHODS

A total of 149 patients underwent PCA for a SRM at our institution. Based on CT dose reports, we calculated the mean effective dose for a CT-guided PCA procedure and post-ablative follow-up CT. Applying follow-up recommendations by a multidisciplinary expert panel, we calculated the total radiation dose for the PCA procedure and the CT surveillance program corresponding to a minimal and preferable follow-up regime (5-year vs 10-year). Estimates of the lifetime attributable cancer risk for different age groups were calculated based on the cumulative effective dose based on the latest BEIR VII report.

RESULTS

Total dose for the PCA treatment and follow-up CTs amounted to 174 and 294 mSv for a minimal and preferable protocol, respectively. Follow-up CTs accounted for the majority of the total effective dose for the minimal and preferable protocol (89% vs 94%). CT fluoroscopy contributed only to a limited amount of the total radiation dose for the minimal and preferable protocol (1.8% vs 1.1%). A 70-year-old male undergoing PCA treatment has a lifetime attributable cancer risk of 0.8% (1 in 131) when completing the preferable follow-up protocol. The same regimen in a 30-year-old female results in a lifetime attributable risk of cancer of 3.4% (1 in 29).

CONCLUSION

Radiation dose and the associated risk of secondary cancer are high for patients with SRMs undergoing PCA and post-ablative follow-up imaging in particular in younger patients. Radiation exposure in the PCA procedure itself accounts for only a limited amount of the total radiation. Radiologists and clinicians must strive to implement radiation dose saving measures especially with respect to the follow-up regime.