Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings.
Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings.
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DOI：
10.1016/j.euf.2016.05.009
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发表时间：
2017-10
影响因子：
5.4
通讯作者：
Madabhushi A
中科院分区：
文献类型：
--
作者：
Lee G;Veltri RW;Zhu G;Ali S;Epstein JI;Madabhushi A
格里森评分代表了前列腺癌 (PCa) 诊断和根治性前列腺切除术 (RP) 后预后评估的标准，但它没有考虑到邻近正常外观良性区域的模式，而这些模式可能预测疾病复发。旨在研究（1）数字病理图像上肿瘤邻近良性区域内计算机提取的图像特征是否可以预测前列腺癌患者手术后的复发，以及（2）与单独使用格里森评分或良性或癌性区域的特征相比，肿瘤加上邻近良性特征（TABS）是否可以更好地预测复发。我们研究了 2000 年至 2004 年间手术后 70 名 PCa 患者的 140 个组织微阵列核心（每个 0.6 mm），并进行了长达 14 年的随访。总体而言，22 名患者经历了复发（生化[前列腺特异性抗原]、局部或远处复发以及癌症死亡），48 名患者没有复发。干预：对所有患者进行 RP。被确定为最能预测良性和癌性区域内复发的前 10 个特征被组合成 10 个特征签名 (TABS)。通过随机森林分类准确性和 Kaplan-Meier 生存分析评估计算机从癌变区域、邻近良性区域和 TABS 中提取的核形状和结构特征。肿瘤邻近良性视野特征可预测复发（受试者工作特征曲线下面积 [AUC]：0.72）。肿瘤野核形状描述符和良性野局部核排列是 TABS 的主要特征（AUC：0.77）。 TABS 与 Gleason sum 相结合进一步提高了复发的识别率（AUC：0.81）。所有实验均使用三重交叉验证进行，没有独立的测试集验证。计算机提取的癌性和良性区域内的核特征可预测 RP 后的复发。此外，TABS 被证明可以为常见预测变量（包括格里森总和以及 Kattan 和 Stephenson 列线图）提供附加值。未来的研究可能受益于对手术切除的前列腺癌组织上靠近肿瘤的良性区域的评估，以评估疾病复发的风险。
Gleason scoring represents the standard for diagnosis of prostate cancer (PCa) and assessment of prognosis following radical prostatectomy (RP), but it does not account for patterns in neighboring normal-appearing benign fields that may be predictive of disease recurrence. To investigate (1) whether computer-extracted image features within tumor-adjacent benign regions on digital pathology images could predict recurrence in PCa patients after surgery and (2) whether a tumor plus adjacent benign signature (TABS) could better predict recurrence compared with Gleason score or features from benign or cancerous regions alone. We studied 140 tissue microarray cores (0.6 mm each) from 70 PCa patients following surgery between 2000 and 2004 with up to 14 yr of follow-up. Overall, 22 patients experienced recurrence (biochemical [prostate-specific antigen], local, or distant recurrence and cancer death) and 48 did not. Intervention: RP was performed in all patients. The top 10 features identified as most predictive of recurrence within both the benign and cancerous regions were combined into a 10-feature signature (TABS). Computer-extracted nuclear shape and architectural features from cancerous regions, adjacent benign fields, and TABS were evaluated via random forest classification accuracy and Kaplan-Meier survival analysis. Tumor-adjacent benign field features were predictive of recurrence (area under the receiver operating characteristic curve [AUC]: 0.72). Tumor-field nuclear shape descriptors and benign-field local nuclear arrangement were the predominant features found for TABS (AUC: 0.77). Combining TABS with Gleason sum further improved identification of recurrence (AUC: 0.81). All experiments were performed using threefold cross-validation without independent test set validation. Computer-extracted nuclear features within cancerous and benign regions predict recurrence following RP. Furthermore, TABS was shown to provide added value to common predictors including Gleason sum and Kattan and Stephenson nomograms. Future studies may benefit from evaluation of benign regions proximal to the tumor on surgically excised prostate cancer tissue for assessing risk of disease recurrence.
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DOI：
10.1073/pnas.1217982109
发表时间：
2012-12-11
影响因子：
11.1
作者：
Zong, Yang;Huang, Jiaoti;Witte, Owen N.
通讯作者：
Witte, Owen N.
影响因子：
6.6
作者：
Cohen, Ronald J.;Shannon, Beverley A.;Garrett, Kerryn L.
通讯作者：
Garrett, Kerryn L.
影响因子：
8.9
作者：
Tyson, Mark D., II;Andrews, Paul E.;Castle, Erik P.
通讯作者：
Castle, Erik P.
影响因子：
11.5
作者：
Veltri, RW;Khan, MA;Partin, AW
通讯作者：
Partin, AW
影响因子：
3.7
作者：
Gann PH;Deaton R;Amatya A;Mohnani M;Rueter EE;Yang Y;Ananthanarayanan V
通讯作者：
Ananthanarayanan V