RESUMEN
Chagas disease is an infection caused by Trypanosoma cruzi, a parasite endemic in Latin America. Acute involvement of the CNS by Chagas has been considered rare, but presumed reactivation of chronic disease in immunosuppressed patients has been the subject of recent reports. Our objective is to describe the clinical and imaging characteristics of four patients with Chagas disease and CNS involvement, and the patients had to have available MRI and a diagnosis confirmed by biopsy. The imaging findings were similar, highlighting the presence of focal cerebral lesions with hypointensity on T2-WI, and these lesions assume a "bunch of acai berries appearance", a fruit involved in the transmission of T. cruzi. The post Gd T1-WI shows punctate enhancement. Knowledge of this pattern may be crucial to recognize this disease in immunocompromised patients from endemic areas.
Asunto(s)
Neoplasias del Sistema Nervioso Central , Enfermedad de Chagas , Euterpe , Trypanosoma cruzi , Humanos , Euterpe/parasitología , Enfermedad de Chagas/diagnóstico por imagen , Enfermedad de Chagas/epidemiología , Enfermedad de Chagas/parasitología , RadiografíaRESUMEN
Objective: To evaluate the results obtained with an artificial intelligence-based software for predicting the risk of malignancy in breast masses from ultrasound images. Materials and Methods: This was a retrospective, single-center study evaluating 555 breast masses submitted to percutaneous biopsy at a cancer referral center. Ultrasonographic findings were classified in accordance with the BI-RADS lexicon. The images were analyzed by using Koios DS Breast software and classified as benign, probably benign, low to intermediate suspicion, high suspicion, or probably malignant. The histological classification was considered the reference standard. Results: The mean age of the patients was 51 years, and the mean mass size was 16 mm. The radiologist evaluation had a sensitivity and specificity of 99.1% and 34.0%, respectively, compared with 98.2% and 39.0%, respectively, for the software evaluation. The positive predictive value for malignancy for the BI-RADS categories was similar between the radiologist and software evaluations. Two false-negative results were identified in the radiologist evaluation, the masses in question being classified as suspicious by the software, whereas four false-negative results were identified in the software evaluation, the masses in question being classified as suspicious by the radiologist. Conclusion: In our sample, the performance of artificial intelligence-based software was comparable to that of a radiologist.
Objetivo: O objetivo deste trabalho foi avaliar os resultados de um software baseado em algoritmo de inteligência artificial para predição do risco de malignidade em nódulos mamários. Materiais e Métodos: Estudo retrospectivo e unicêntrico que avaliou 555 nódulos mamários submetidos a biópsia percutânea em um centro de referência oncológico. Os achados ultrassonográficos foram classificados de acordo com o léxico do BI-RADS. As imagens foram analisadas pelo software Koios DS Breast e divididas em benigna ou provavelmente benigna, suspeita baixa ou intermediária, suspeita alta ou provavelmente maligna. O resultado histopatológico foi considerado como padrão ouro. Resultados: A média de idade das pacientes foi de 51 anos e o tamanho médio dos nódulos foi de 16 mm. A sensibilidade e a especificidade foram de 99,1% e 34,0% para o radiologista e 98,2% e 39,0% para o software, respectivamente. O valor preditivo positivo para malignidade para as categorias BIRADS foi semelhante para o radiologista e para o software. Foram identificados dois resultados falso-negativos na avaliação pelo radiologista que foram classificados como suspeitos pelo software, e quatro resultados falso-negativos na avaliação pelo software que foram classificados como suspeitos pelo radiologista. Conclusão: Na nossa amostra, o software de inteligência artificial demonstrou resultados comparáveis à avaliação pelo radiologista.
RESUMEN
Abstract Objective: To evaluate the results obtained with an artificial intelligence-based software for predicting the risk of malignancy in breast masses from ultrasound images. Materials and Methods: This was a retrospective, single-center study evaluating 555 breast masses submitted to percutaneous biopsy at a cancer referral center. Ultrasonographic findings were classified in accordance with the BI-RADS lexicon. The images were analyzed by using Koios DS Breast software and classified as benign, probably benign, low to intermediate suspicion, high suspicion, or probably malignant. The histological classification was considered the reference standard. Results: The mean age of the patients was 51 years, and the mean mass size was 16 mm. The radiologist evaluation had a sensitivity and specificity of 99.1% and 34.0%, respectively, compared with 98.2% and 39.0%, respectively, for the software evaluation. The positive predictive value for malignancy for the BI-RADS categories was similar between the radiologist and software evaluations. Two false-negative results were identified in the radiologist evaluation, the masses in question being classified as suspicious by the software, whereas four false-negative results were identified in the software evaluation, the masses in question being classified as suspicious by the radiologist. Conclusion: In our sample, the performance of artificial intelligence-based software was comparable to that of a radiologist.
Resumo Objetivo: O objetivo deste trabalho foi avaliar os resultados de um software baseado em algoritmo de inteligência artificial para predição do risco de malignidade em nódulos mamários. Materiais e Métodos: Estudo retrospectivo e unicêntrico que avaliou 555 nódulos mamários submetidos a biópsia percutânea em um centro de referência oncológico. Os achados ultrassonográficos foram classificados de acordo com o léxico do BI-RADS. As imagens foram analisadas pelo software Koios DS Breast e divididas em benigna ou provavelmente benigna, suspeita baixa ou intermediária, suspeita alta ou provavelmente maligna. O resultado histopatológico foi considerado como padrão ouro. Resultados: A média de idade das pacientes foi de 51 anos e o tamanho médio dos nódulos foi de 16 mm. A sensibilidade e a especificidade foram de 99,1% e 34,0% para o radiologista e 98,2% e 39,0% para o software, respectivamente. O valor preditivo positivo para malignidade para as categorias BIRADS foi semelhante para o radiologista e para o software. Foram identificados dois resultados falso-negativos na avaliação pelo radiologista que foram classificados como suspeitos pelo software, e quatro resultados falso-negativos na avaliação pelo software que foram classificados como suspeitos pelo radiologista. Conclusão: Na nossa amostra, o software de inteligência artificial demonstrou resultados comparáveis à avaliação pelo radiologista.