A case of desmoplastic leiomyosarcoma: a rare variant of cutaneous leiomyosarcoma.

Desmoplastic leiomyosarcoma is a rare histologic variant of cutaneous leiomyosarcoma seen more commonly in men in their 50s and 60s. This neoplasm typically presents as a solitary, enlarging red-pink nodule or plaque on the extensor surfaces of lower extremities. Its unusual histology mimics other cutaneous desmoplastic lesions and the knowledge of this entity and use of an appropriate immunohistochemical panel is essential to arrive at the correct diagnosis. We report a rare case of desmoplastic leiomyosarcoma of the left flank in a 66-year-old male who presented with itching and pain in a long-standing skin lesion. Histopathology showed the presence of individual and small aggregates of spindle to pleomorphic cells with numerous mitoses in a densely fibrotic stroma. Immunohistochemically, the cells were positive for smooth muscle actin, heavy chain myosin, and desmin, confirming their smooth muscle origin. A diagnosis of desmoplastic leiomyosarcoma was made. We discuss the case with a short review of the literature.

A simple method to combine multiple molecular biomarkers for dichotomous diagnostic classification.

BACKGROUND: In spite of the recognized diagnostic potential of biomarkers, the quest for squelching noise and wringing in information from a given set of biomarkers continues. Here, we suggest a statistical algorithm that--assuming each molecular biomarker to be a diagnostic test--enriches the diagnostic performance of an optimized set of independent biomarkers employing established statistical techniques. We validated the proposed algorithm using several simulation datasets in addition to four publicly available real datasets that compared i) subjects having cancer with those without; ii) subjects with two different cancers; iii) subjects with two different types of one cancer; and iv) subjects with same cancer resulting in differential time to metastasis. RESULTS: Our algorithm comprises of three steps: estimating the area under the receiver operating characteristic curve for each biomarker, identifying a subset of biomarkers using linear regression and combining the chosen biomarkers using linear discriminant function analysis. Combining these established statistical methods that are available in most statistical packages, we observed that the diagnostic accuracy of our approach was 100%, 99.94%, 96.67% and 93.92% for the real datasets used in the study. These estimates were comparable to or better than the ones previously reported using alternative methods. In a synthetic dataset, we also observed that all the biomarkers chosen by our algorithm were indeed truly differentially expressed. CONCLUSION: The proposed algorithm can be used for accurate diagnosis in the setting of dichotomous classification of disease states.