External Validation of a Five-Tiered CT Algorithm for the Diagnosis of Clear Cell Renal Cell Carcinoma: A Retrospective Five-Reader Study

World Health Organization 2022 Classification Update: Radiologic and Pathologic Features of Papillary Renal Cell Carcinomas

RATIONALE AND OBJECTIVES

To describe imaging and pathology features of newly defined papillary renal cell carcinoma (pRCC) based on the WHO 2022 update.

MATERIALS AND METHODS

This retrospective study included 87 patients with 93 pathologically proven papillary renal cell carcinomas who underwent pre-treatment renal mass protocol CT or MRI. Baseline and post-treatment follow-up imaging was evaluated by two radiologists systematically based on established lexicon.

RESULTS

At pathology, 63 (68%) were grade 1-2, 29 (31%) were grade 3-4, and 1 (%) was unreported. At surgical pathology, 84 (90%) were localized (≤pT2b), 5 (5%) were pT3a, and none were ≥pT3b; 4 (4%) had unknown pT stage (core biopsies). 33 (35%) had necrosis and 39 (41%) had hemorrhage. None had sarcomatoid or rhabdoid differentiation. At imaging, 73 (83%) were solid and 16 (17%) were cystic. Of 16 cystic masses, four were Bosniak class IIF (three were heterogeneously T1 hyperintense) and 12 were class IV. All were well-circumscribed. 92 (99%) were hypovascular. Median follow-up for 74 patients was 30 months (IQR 12-56). One untreated patient had non-regional nodal metastasis at presentation, and one patient had metastasis to lymph nodes and bones after surgery, but the patient had unresected renal masses elsewhere without pathology. Otherwise, no recurrence or metastases were detected.

CONCLUSION

Most pRCCs present as a hypovascular, circumscribed, solid renal mass. A few pRCCs present as the newly defined Bosniak class IIF subtype. Our results can form the basis of a non-invasive, likelihood score to identify this relatively indolent pathology in the era of virtual biopsy and active surveillance.

Evaluation of a multiparametric renal CT algorithm for diagnosis of clear-cell renal cell carcinoma among small (≤ 4 cm) solid renal masses

OBJECTIVE

To evaluate a recently proposed CT-based algorithm for diagnosis of clear-cell renal cell carcinoma (ccRCC) among small (≤ 4 cm) solid renal masses diagnosed by renal mass biopsy.

METHODS

This retrospective study included 51 small renal masses in 51 patients with renal-mass CT and biopsy between 2014 and 2021. Three radiologists independently evaluated corticomedullary phase CT for the following: heterogeneity and attenuation ratio (mass:renal cortex), which were used to inform the CT score (1-5). CT score ≥ 4 was considered positive for ccRCC. Diagnostic accuracy was calculated for each reader and overall using fixed effects logistic regression modelling.

RESULTS

There were 51% (26/51) ccRCC and 49% (25/51) other masses. For diagnosis of ccRCC, area under curve (AUC), sensitivity, specificity, and positive predictive value (PPV) were 0.69 (95% confidence interval 0.61-0.76), 78% (68-86%), 59% (46-71%), and 67% (54-79%), respectively. CT score ≤ 2 had a negative predictive value 97% (92-99%) to exclude diagnosis of ccRCC. For diagnosis of papillary renal cell carcinoma (pRCC), CT score ≤ 2, AUC, sensitivity, specificity, and PPV were 0.89 (0.81-0.98), 81% (58-94%), 98% (93-99%), and 85% (62-97%), respectively. Pooled inter-observer agreement for CT scoring was moderate (Fleiss weighted kappa = 0.52).

CONCLUSION

The CT scoring system for prediction of ccRCC was sensitive with a high negative predictive value and moderate agreement. The CT score is highly specific for diagnosis of pRCC.

CLINICAL RELEVANCE STATEMENT

The CT score algorithm may help guide renal mass biopsy decisions in clinical practice, with high sensitivity to identify clear-cell tumors for biopsy to establish diagnosis and grade and high specificity to avoid biopsy in papillary tumors.

KEY POINTS

• A CT score ≥ 4 had high sensitivity and negative predictive value for diagnosis of clear-cell renal cell carcinoma (RCC) among solid ≤ 4-cm renal masses. • A CT score ≤ 2 was highly specific for diagnosis of papillary RCC among solid ≤ 4-cm renal masses. • Inter-observer agreement for CT score was moderate.

Availability of ChatGPT to provide medical information for patients with kidney cancer

ChatGPT is an advanced natural language processing technology that closely resembles human language. We evaluated whether ChatGPT could help patients understand kidney cancer and replace consultations with urologists. Two urologists developed ten questions commonly asked by patients with kidney cancer. The answers to these questions were produced using ChatGPT. The five-dimension SERVQUAL model was used to assess the service quality of ChatGPT. The survey was distributed to 103 urologists via email, and twenty-four urological oncologists specializing in kidney cancer were included as experts with more than 20 kidney cancer cases in clinic per month. All respondents were physicians. We received 24 responses to the email survey (response rate: 23.3%). The appropriateness rate for all ten answers exceeded 60%. The answer to Q2 received the highest agreement (91.7%, etiology of kidney cancer), whereas the answer to Q8 had the lowest (62.5%, comparison with other cancers). The experts gave low assessment ratings (44.4% vs. 93.3%, p = 0.028) in the SERVQUAL assurance (certainty of total answers) dimension. Positive scores for the overall understandability of ChatGPT answers were assigned by 54.2% of responders, and 70.8% said that ChatGPT could not replace explanations provided by urologists. Our findings affirm that although ChatGPT answers to kidney cancer questions are generally accessible, they should not supplant the counseling of a urologist.