Montironi Rodolfo, Scarpelli Marina, Lopez-Beltran Antonio, Mazzucchelli Roberta, Alberts David, Ranger-Moore James, Bartels Hubert G, Hamilton Peter W, Einspahr Janine, Bartels Peter H
Section of Pathological Anatomy and Histopathology, Polytechnic University of the Marche Region, Ancona, Italy.
Cell Oncol. 2007;29(1):47-58. doi: 10.1155/2007/356464.
A preceding exploratory study (J. Clin. Pathol. 57(2004), 1201-1207) had shown that a karyometric assessment of nuclei from papillary urothelial neoplasms of low malignant potential (PUNLMP) revealed subtle differences in phenotype which correlated with recurrence of disease.
To validate the results from the exploratory study on a larger sample size.
93 karyometric features were analyzed on haematoxylin and eosin-stained sections from 85 cases of PUNLMP. 45 cases were from patients who had a solitary PUNLMP lesion and were disease-free during a follow-up period of at least 8 years. The other 40 were from patients with a unifocal PUNLMP, with one or more recurrences in the follow-up. A combination of the previously defined classification functions together with a new P-index derived classification method was used in an attempt to classify cases and identify a biomarker of recurrence in PUNLMP lesions.
Validation was pursued by a number of separate approaches. First, the exact procedure from the exploratory study was applied to the large validation set. Second, since the discriminant function 2 of the exploratory study had been based on a small sample size, a new discriminant function was derived. The case classification showed a correct classification of 61% for non-recurrent and 74% for recurrent cases, respectively. Greater success was obtained by applying unsupervised learning technologies to take advantage of phenotypical composition (correct classification of 92%). This approach was validated by dividing the data into training and test sets with 2/3 of the cases assigned to the training sets, and 1/3 to the test sets, on a rotating basis, and validation of the classification rate was thus tested on three separate data sets by a leave-k-out process. The average correct classification was 92.8% (training set) and 84.6% (test set).
Our validation study detected subvisual differences in chromatin organization state between non-recurrent and recurrent PUNLMP, thus allowing a very stable method of predicting recurrence of papillary urothelial neoplasms of low malignant potential by karyometry.
之前的一项探索性研究（《临床病理学杂志》57(2004)，1201 - 1207）表明，对低恶性潜能乳头状尿路上皮肿瘤（PUNLMP）细胞核进行核测量评估揭示了与疾病复发相关的细微表型差异。
在更大样本量上验证探索性研究的结果。
对85例PUNLMP的苏木精和伊红染色切片分析了93个核测量特征。45例来自仅有一个PUNLMP病变且在至少8年随访期内无疾病复发的患者。另外40例来自单灶性PUNLMP患者，在随访中有一次或多次复发。使用先前定义的分类函数与一种新的基于P指数的分类方法相结合，试图对病例进行分类并识别PUNLMP病变复发的生物标志物。
通过多种独立方法进行验证。首先，将探索性研究的精确程序应用于大型验证集。其次，由于探索性研究的判别函数2基于小样本量，推导了一个新的判别函数。病例分类显示非复发病例的正确分类率为61%，复发病例为74%。通过应用无监督学习技术利用表型组成获得了更高的成功率（正确分类率为92%）。通过将数据以轮流方式分为训练集和测试集，其中2/3的病例分配到训练集，1/3分配到测试集，并通过留一法在三个独立数据集上测试分类率，从而验证了该方法。平均正确分类率在训练集为92.8%，测试集为84.6%。
我们的验证研究检测到非复发性和复发性PUNLMP之间染色质组织状态的亚视觉差异，从而允许通过核测量采用一种非常稳定的方法预测低恶性潜能乳头状尿路上皮肿瘤的复发。
