Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Mais filtros

Base de dados
Ano de publicação
Tipo de documento
Intervalo de ano de publicação
1.
Mod Pathol ; 37(9): 100552, 2024 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-38942115

RESUMO

PLAG1 gene fusions were recently identified in a subset of uterine myxoid leiomyosarcomas (M-LMS). However, we have encountered cases of PLAG1-rearranged uterine sarcomas lacking M-LMS-like morphology and/or any expression of smooth muscle markers. To better characterize their clinicopathologic features, we performed a multiinstitutional search that yielded 11 cases. The patients ranged in age from 34 to 72 years (mean, 57 years). All tumors arose in the uterine corpus, ranging in size from 6.5 to 32 cm (mean, 15 cm). The most common stage at presentation was pT1b (n = 6), and 3 cases had stage pT1 (unspecified), and 1 case each presented in stages pT2a and pT3b. Most were treated only with hysterectomy and adnexectomy. The follow-up (range, 7-71 months; median, 39 months) was available for 7 patients. Three cases (7-21 months of follow-up) had no evidence of disease. Three of the 4 remaining patients died of disease within 55 to 71 months, while peritoneal spread developed in the last patient, and the patient was transferred for palliative care at 39 months. Morphologically, the tumors showed a high intertumoral and intratumoral heterogeneity. M-LMS-like and epithelioid leiomyosarcoma-like morphology were present in 3 and 5 primary tumors, respectively, the remaining mostly presented as nondescript ovoid or spindle cell sarcomas. Unusual morphologic findings included prominently hyalinized stroma (n = 3), adipocytic differentiation with areas mimicking myxoid liposarcoma (n = 2), osteosarcomatous differentiation (n = 1), and undifferentiated pleomorphic sarcoma-like areas (n = 1). The mitotic activity ranged from 3 to 24 mitoses per 10 high-power fields (mean, 9); 3 of 10 cases showed necrosis. In 3 of 11 cases, no expression of smooth muscle actin, h-caldesmon, or desmin was noted, whereas 5 of 5 cases expressed PLAG1. By RNA sequencing, the following fusion partners were identified: PUM1, CHCHD7 (each n = 2), C15orf29, CD44, MYOCD, FRMD6, PTK2, and TRPS1 (each n = 1). One case only showed PLAG1 gene break by fluorescence in situ hybridization. Our study documents a much broader morphologic spectrum of PLAG1-rearranged uterine sarcomas than previously reported, encompassing but not limited to M-LMS-like morphology with occasional heterologous (particularly adipocytic) differentiation. As it is currently difficult to precisely define their line of differentiation, for the time being, we suggest using a descriptive name "PLAG1-rearranged uterine sarcoma."

2.
Arch Pathol Lab Med ; 2024 Apr 23.
Artigo em Inglês | MEDLINE | ID: mdl-38649149

RESUMO

CONTEXT.­: Artificial intelligence is a transforming technology for anatomic pathology. Involvement within the workforce will foster support for algorithm development and implementation. OBJECTIVE.­: To develop a supportive ecosystem that enables pathologists with variable expertise in artificial intelligence to create algorithms in a development environment with seamless transition to a production environment. RESULTS.­: The development team considered internal development and vended solutions. Because of the extended timeline and resource requirements for internal development, a decision was made to use a vended solution. Vendor proposals were solicited and reviewed by pathologists, IT, and security groups. A vendor was selected and pipelines for development and production were established. Proposals for development were solicited from the pathology department. Eighty-four investigators were selected for the initial cohort, receiving training and access to dedicated subject matter experts. A total of 30 of 31 projects progressed through the model development process of annotating, training, and validation. Based on these projects, 15 abstracts were submitted to national meetings. CONCLUSIONS.­: Democratizing artificial intelligence by creating an ecosystem to support pathologists with varying levels of expertise can break down entry barriers, reduce overall cost of algorithm development, improve algorithm quality, and enhance the speed of adoption.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA