Abstract
Recognition of noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) that distinguishes them from invasive malignant encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC) can prevent overtreatment of NIFTP patients. We and others have previously reported that programmed death-ligand 1 (PD-L1) is a useful biomarker in thyroid tumors; however, all reports to date have relied on manual scoring that is time consuming as well as subject to individual bias. Consequently, we developed a digital image analysis (DIA) protocol for cytoplasmic and membranous stain quantitation (ThyApp) and evaluated three tumor sampling methods [Systemic Uniform Random Sampling, hotspot nucleus, and hotspot nucleus/3,3′-Diaminobenzidine (DAB)]. A patient cohort of 153 cases consisting of 48 NIFTP, 44 EFVPTC, 26 benign nodules and 35 encapsulated follicular lesions/neoplasms with lymphocytic thyroiditis (LT) was studied. ThyApp quantitation of PD-L1 expression revealed a significant difference between invasive EFVPTC and NIFTP; but none between NIFTP and benign nodules. ThyApp integrated with hotspot nucleus tumor sampling method demonstrated to be most clinically relevant, consumed least processing time, and eliminated interobserver variance. In conclusion, the fully automatic DIA algorithm developed using a histomorphological approach objectively quantitated PD-L1 expression in encapsulated thyroid neoplasms and outperformed manual scoring in reproducibility and higher efficiency.
Highlights
The diagnosis of the borderline lesions are challenging in clinical practice [1,2,3]
Among the 153 cases meeting the inclusion criteria, 26 cases were classified as benign nodules, 48 as NIFTP, 44 as encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC) and 35 cases with predominant coexisting lymphocytic and Hashimoto’s thyroiditis (LT)
digital image analysis (DIA) of programmed death-ligand 1 (PD-L1) expression distinguished between NIFTP and invasive EFVPTC
Summary
The diagnosis of the borderline lesions are challenging in clinical practice [1,2,3]. The identification of highly indolent noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) from the invasive malignant encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC) can prevent overdiagnosis and overtreatment of NIFTP patients [4]. There is increasing recognition to utilize biomarker analysis to complement histopathological diagnosis and prognosis management decisions. Our laboratory has been investigating protein biomarkers for thyroid carcinomas [5,6,7,8,9,10] and showed that programmed death-ligand 1 www.oncotarget.com (PD-L1) overexpression, predominantly localized in cytoplasm and occasionally in plasma membrane of tumor cells, is a useful prognostic marker for aggressive papillary thyroid cancer and its variants [11]. As pathology evolves into a more digital discipline, the need for the development of a digital image analysis (DIA) tool that could provide an efficient, reproducible, and accurate quantitation of protein biomarker expression came into focus
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