Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD 20892, USA.
Med Phys. 2011 Oct;38(10):5738-46. doi: 10.1118/1.3633898.
To explore the added value of the shape of renal lesions for classifying renal neoplasms. To investigate the potential of computer-aided analysis of contrast-enhanced computed-tomography (CT) to quantify and classify renal lesions.
A computer-aided clinical tool based on adaptive level sets was employed to analyze 125 renal lesions from contrast-enhanced abdominal CT studies of 43 patients. There were 47 cysts and 78 neoplasms: 22 Von Hippel-Lindau (VHL), 16 Birt-Hogg-Dube (BHD), 19 hereditary papillary renal carcinomas (HPRC), and 21 hereditary leiomyomatosis and renal cell cancers (HLRCC). The technique quantified the three-dimensional size and enhancement of lesions. Intrapatient and interphase registration facilitated the study of lesion serial enhancement. The histograms of curvature-related features were used to classify the lesion types. The areas under the curve (AUC) were calculated for receiver operating characteristic curves.
Tumors were robustly segmented with 0.80 overlap (0.98 correlation) between manual and semi-automated quantifications. The method further identified morphological discrepancies between the types of lesions. The classification based on lesion appearance, enhancement and morphology between cysts and cancers showed AUC = 0.98; for BHD + VHL (solid cancers) vs. HPRC + HLRCC AUC = 0.99; for VHL vs. BHD AUC = 0.82; and for HPRC vs. HLRCC AUC = 0.84. All semi-automated classifications were statistically significant (p < 0.05) and superior to the analyses based solely on serial enhancement.
The computer-aided clinical tool allowed the accurate quantification of cystic, solid, and mixed renal tumors. Cancer types were classified into four categories using their shape and enhancement. Comprehensive imaging biomarkers of renal neoplasms on abdominal CT may facilitate their noninvasive classification, guide clinical management, and monitor responses to drugs or interventions.
探讨肾脏病变形态在肾脏肿瘤分类中的附加价值。研究基于自适应水平集的计算机辅助分析增强 CT 定量和分类肾脏病变的潜力。
采用基于自适应水平集的计算机辅助临床工具分析 43 例患者增强腹部 CT 研究的 125 个肾脏病变。其中 47 个囊肿和 78 个肿瘤：22 个 Von Hippel-Lindau(VHL)，16 个 Birt-Hogg-Dube(BHD)，19 个遗传性乳头状肾细胞癌(HPRC)和 21 个遗传性平滑肌瘤病和肾细胞癌(HLRCC)。该技术定量了病变的三维大小和增强程度。同患者和不同相位的配准促进了病变的连续增强研究。曲率相关特征的直方图用于分类病变类型。计算受试者工作特征曲线下的面积(AUC)。
肿瘤的分割具有 0.80 的重叠（0.98 的相关性），手动和半自动定量之间的差异很小。该方法进一步确定了不同病变类型之间的形态差异。基于病变外观、增强和形态的囊肿和癌症之间的分类显示 AUC=0.98；BHD+VHL（实体瘤）与 HPRC+HLRCC 的 AUC=0.99；VHL 与 BHD 的 AUC=0.82；HPRC 与 HLRCC 的 AUC=0.84。所有半自动分类均具有统计学意义（p<0.05），且优于仅基于连续增强的分析。
计算机辅助临床工具允许对囊性、实体性和混合性肾肿瘤进行准确的定量。使用病变的形状和增强程度将癌症类型分为四类。腹部 CT 上的肾脏肿瘤综合成像生物标志物可能有助于其非侵入性分类，指导临床管理，并监测药物或干预措施的反应。
