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Quantitative computer-aided diagnostic algorithm for automated detection of peak lesion attenuation in differentiating clear cell from papillary and chromophobe renal cell carcinoma, oncocytoma, and fat-poor angiomyolipoma on multiphasic multidetector computed tomography

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Abstract

Objective

To evaluate the performance of a novel, quantitative computer-aided diagnostic (CAD) algorithm on four-phase multidetector computed tomography (MDCT) to detect peak lesion attenuation to enable differentiation of clear cell renal cell carcinoma (ccRCC) from chromophobe RCC (chRCC), papillary RCC (pRCC), oncocytoma, and fat-poor angiomyolipoma (fp-AML).

Materials and methods

We queried our clinical databases to obtain a cohort of histologically proven renal masses with preoperative MDCT with four phases [unenhanced (U), corticomedullary (CM), nephrographic (NP), and excretory (E)]. A whole lesion 3D contour was obtained in all four phases. The CAD algorithm determined a region of interest (ROI) of peak lesion attenuation within the 3D lesion contour. For comparison, a manual ROI was separately placed in the most enhancing portion of the lesion by visual inspection for a reference standard, and in uninvolved renal cortex. Relative lesion attenuation for both CAD and manual methods was obtained by normalizing the CAD peak lesion attenuation ROI (and the reference standard manually placed ROI) to uninvolved renal cortex with the formula [(peak lesion attenuation ROI − cortex ROI)/cortex ROI] × 100%. ROC analysis and area under the curve (AUC) were used to assess diagnostic performance. Bland–Altman analysis was used to compare peak ROI between CAD and manual method.

Results

The study cohort comprised 200 patients with 200 unique renal masses: 106 (53%) ccRCC, 32 (16%) oncocytomas, 18 (9%) chRCCs, 34 (17%) pRCCs, and 10 (5%) fp-AMLs. In the CM phase, CAD-derived ROI enabled characterization of ccRCC from chRCC, pRCC, oncocytoma, and fp-AML with AUCs of 0.850 (95% CI 0.732–0.968), 0.959 (95% CI 0.930–0.989), 0.792 (95% CI 0.716–0.869), and 0.825 (95% CI 0.703–0.948), respectively. On Bland–Altman analysis, there was excellent agreement of CAD and manual methods with mean differences between 14 and 26 HU in each phase.

Conclusion

A novel, quantitative CAD algorithm enabled robust peak HU lesion detection and discrimination of ccRCC from other renal lesions with similar performance compared to the manual method.

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Authors and Affiliations

  1. Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan-UCLA Medical Center, 924 Westwood Boulevard, Suite 650, Los Angeles, CA, 90024, USA

    Heidi Coy, Jonathan R. Young, Michael L. Douek & Matthew S. Brown

  2. Department of Biostatistics, UCLA School of Public Heath, Room 51-253A, Los Angeles, CA, 90095, USA

    James Sayre

  3. UCLA Department of Radiological Sciences, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, RRUMC 1621H, Box 957437, Los Angeles, CA, 90095, USA

    Steven S. Raman

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  1. Heidi Coy
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Correspondence to Heidi Coy.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.

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Institutional review board (IRB) approval for a waiver of informed consent was received for this retrospective study.

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Coy, H., Young, J.R., Douek, M.L. et al. Quantitative computer-aided diagnostic algorithm for automated detection of peak lesion attenuation in differentiating clear cell from papillary and chromophobe renal cell carcinoma, oncocytoma, and fat-poor angiomyolipoma on multiphasic multidetector computed tomography. Abdom Radiol 42, 1919–1928 (2017). https://doi.org/10.1007/s00261-017-1095-6

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