Uterine embryonal rhabdomyosarcoma in adult women: a case report on the challenging diagnosis and treatment.

BACKGROUND: Uterine embryonal rhabdomyosarcoma (uERMS) in adult women is a very rare malignant entity. The study aim was to report a case of adult uERMS and to discuss the implications of histopathological diagnosis on the treatment and prognosis. CASE PRESENTATION: We present here the clinicopathological features of a uERMS case in an adult woman. The study has been approved by the institutional Ethics Committee and an informed consent has been obtained (IJB∕CE3005). A 45-year-old woman presented to her gynecologist with intermenstrual bleedings and polypoid cervical mass (initially interpreted as benign polyp). A second biopsy was sent to our Department of Pathology at the Jules Bordet Institute, Brussels, Belgium for revision and was reinterpreted as botryoid-type uERMS. The patient underwent a total hysterectomy. The final pathology confirms a 3 cm cervical ERMS, and a simple surveillance was decided by our multidisciplinary team. Six months later, pelvic magnetic resonance imaging control showed a recurrence in the right pelvic lymph nodes. Multi-drug chemotherapy and radiotherapy were done before surgical resection. Pathological examination of the resected pelvic mass confirmed uERMS recurrence of 60 mm, with large zones of necrosis and the presence of cartilaginous structures. The patient is free of disease 60 months after diagnosis. CONCLUSIONS: Adult uERMS is rare and the pathological examination is the main element for diagnosis and treatment. It is often confused with other benign entities, at least at the time of diagnosis. ERMS should be included in the differential diagnosis of cervical and uterine polyp of adult women. Long-term survival is possible with a multimodal therapy approach.

Interobserver variability in upfront dichotomous histopathological assessment of ductal carcinoma in situ of the breast: the DCISion study.

Histopathological assessment of ductal carcinoma in situ, a nonobligate precursor of invasive breast cancer, is characterized by considerable interobserver variability. Previously, post hoc dichotomization of multicategorical variables was used to determine the "ideal" cutoffs for dichotomous assessment. The present international multicenter study evaluated interobserver variability among 39 pathologists who performed upfront dichotomous evaluation of 149 consecutive ductal carcinomas in situ. All pathologists independently assessed nuclear atypia, necrosis, solid ductal carcinoma in situ architecture, calcifications, stromal architecture, and lobular cancerization in one digital slide per lesion. Stromal inflammation was assessed semiquantitatively. Tumor-infiltrating lymphocytes were quantified as percentages and dichotomously assessed with a cutoff at 50%. Krippendorff's alpha (KA), Cohen's kappa and intraclass correlation coefficient were calculated for the appropriate variables. Lobular cancerization (KA = 0.396), nuclear atypia (KA = 0.422), and stromal architecture (KA = 0.450) showed the highest interobserver variability. Stromal inflammation (KA = 0.564), dichotomously assessed tumor-infiltrating lymphocytes (KA = 0.520), and comedonecrosis (KA = 0.539) showed slightly lower interobserver disagreement. Solid ductal carcinoma in situ architecture (KA = 0.602) and calcifications (KA = 0.676) presented with the lowest interobserver variability. Semiquantitative assessment of stromal inflammation resulted in a slightly higher interobserver concordance than upfront dichotomous tumor-infiltrating lymphocytes assessment (KA = 0.564 versus KA = 0.520). High stromal inflammation corresponded best with dichotomously assessed tumor-infiltrating lymphocytes when the cutoff was set at 10% (kappa = 0.881). Nevertheless, a post hoc tumor-infiltrating lymphocytes cutoff set at 20% resulted in the highest interobserver agreement (KA = 0.669). Despite upfront dichotomous evaluation, the interobserver variability remains considerable and is at most acceptable, although it varies among the different histopathological features. Future studies should investigate its impact on ductal carcinoma in situ prognostication. Forthcoming machine learning algorithms may be useful to tackle this substantial diagnostic challenge.