Anatomic pathology selected abstracts

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Correspondence: Dr. J. L. Hornick at jhornick@bwh.harvard.edu

SATB2 expression in uterine sarcoma: a multicenter retrospective study

Uterine sarcomas are clinically challenging because they can be difficult to diagnose and certain subtypes have a poor prognosis. The authors conducted a study to evaluate the expression of the special AT-rich sequence-binding protein 2 (SATB2) in endometrial stromal sarcoma (ESS) and other types of uterine sarcoma using IHC. They analyzed the expression of SATB2 on 71 full tissue sections of endometrial stromal nodule, low-grade ESS, uterine leiomyoma and leiomyosarcoma, undifferentiated uterine sarcoma, adenosarcoma, and carcinosarcoma samples. Nuclear SATB2 expression was then evaluated in an extended sample set, including 78 additional uterine tumor samples, using a tissue microarray. Using a cutoff of 10 percent or more of tumor cells staining as positive, the nuclear SATB2 score was negative in all (n=10) endometrial stromal nodule samples and positive in 83 percent (29 of 35) of low-grade ESS samples, 40 percent (four of 10) of undifferentiated uterine sarcoma, 13 percent (two of 16) of leiomyosarcoma, 14 percent (three of 22) of adenosarcoma, and eight percent (two of 25) of carcinosarcoma samples. Furthermore, direct comparison of nuclear SATB2 scores with clinicopathologic parameters and other reported biomarkers, such as progesterone receptor and estrogen receptor, in ESS patients showed that nuclear SATB2 was associated with progesterone receptor expression and a decreased risk of disease-specific death (odds ratio, 0.06; 95 percent confidence interval, 0.04–0.81; P=0.04). The authors’ findings suggest that SATB2 could be relatively sensitive (83 percent) in distinguishing between endometrial stromal nodules and ESS and has potential prognostic value.

Le Page C, Almadani N, Turashvili G, et al. SATB2 expression in uterine sarcoma: a multicenter retrospective study. Int J Gynecol Pathol. 2021;40(5):487–494.

Correspondence: Dr. Kurosh Rahimi at kurosh.rahimi.chum@ssss.gouv.qc.ca

Utility of rescreening high-risk HPV-positive Pap tests initially interpreted as negative

Many laboratories rescreen Papanicolaou test slides initially interpreted as negative but positive for human papillomavirus high-risk types as a quality control measure. The authors evaluated the utility of this practice in the era of human papillomavirus (HPV) genotyping as a laboratory-improvement project. Between August 2016 and October 2019, they identified 3,618 rescreened Pap tests with follow-up biopsies. The biopsy results were grouped as negative; LSIL—HPV changes or low-grade squamous intraepithelial lesion; and HSIL—high-grade squamous intraepithelial lesion or carcinoma. HPV molecular testing results with subtyping for types 16 and 18 were available for 3,117 of these cases. A total of 530 of 2,812 (18.8 percent) Pap tests with positive HPV results were reinterpreted as cytologically abnormal after rescreening; 75 (14.2 percent) of them had a biopsy result of HSIL. The subset that was positive for HPV types 16 and 18 had 38 of 133 cytology-positive cases diagnosed as HSIL on biopsy versus 107 of 935 cytology-negative cases diagnosed as HSIL on biopsy (28.6 percent versus 11.4 percent, P< 0.0001). The subset that was positive for other high-risk HPV types had 37 of 397 cytology-positive follow-up HSIL diagnoses versus 84 of 1,288 cytology-negative follow-up HSIL diagnoses (9.3 versus 6.5 percent, P= 0.075). The authors concluded that rescreening has the highest yield in specimens positive for types 16 and 18. However, colposcopy is recommended for this group regardless of cytology findings, reducing the patient benefit of rescreening. Routine rescreening of cytology-negative/HPV-positive Pap tests has reduced utility when HPV subtyping is performed and should be reconsidered.

Narkcham S, Mody DR, Jones A, et al. Rescreening of high-risk HPV positive Papanicolaou tests initially screened as negative is a low yield procedure in the era of HPV genotyping. J Am Soc Cytopathol. 2021;10(6):558–564.

Correspondence: Dr. Michael J. Thrall at mjthrall@houstonmethodist.org

Distinguishing sarcomatoid malignant mesothelioma from benign spindle cell mesothelial proliferation

Sarcomatoid mesothelioma is an aggressive malignancy that can be difficult to distinguish from benign spindle cell mesothelial proliferations based on biopsy. This is of particular concern since the distinction is crucial to patient treatment and prognosis. A novel deep-learning–based classifier may be able to aid pathologists in making this critical distinction. The neural network-labeled SpindleMesoNet was trained on cases of malignant sarcomatoid mesothelioma and benign spindle cell mesothelial proliferations. Performance was assessed through cross-validation on the training set as well as on an independent set of challenging cases referred for expert opinion (referral test set) and an externally stained set from outside institutions (externally stained test set). SpindleMesoNet predicted the benign or malignant status of cases with an area under the receiver operating characteristic curve of 0.932, 0.925, and 0.989 on the cross-validation, referral, and external test sets, respectively. The accuracy of SpindleMesoNet on the referral set cases (92.5 percent) was comparable to the average accuracy of three experienced pathologists on the same slide set (91.7 percent). The authors concluded that SpindleMesoNet can distinguish sarcomatoid mesothelioma from benign spindle cell mesothelial proliferations. A deep-learning system of this type holds potential for future use as an ancillary test in diagnostic pathology.

Naso JR, Levine AB, Farahani H, et al. Deep-learning based classification distinguishes sarcomatoid malignant mesotheliomas from benign spindle cell mesothelial proliferation. Mod Pathol. 2021;34:2028–2035.

Correspondence: Dr. Andrew Churg at achurg@mail.ubc.ca