RESUMO
OBJECTIVE: This retrospective study evaluated the model from populations with different breast densities and showed the model's performance on malignancy prediction. METHODS: A total of 608 mammograms were collected from Northern Jiangsu People's Hospital in Yangzhou City. The data from this province have not been used in the training or evaluation data set. The model consists of three submodules, lesion detection (Mask-rcnn), lesion registration between craniocaudal view and mediolateral oblique view, malignancy prediction network (ResNet). The data set used to train the model was obtained from nine institutions across six cities. For normal cases, there were no annotations. Here, we adopted the free-response receiver operating characteristic (FROC) curve as the indicator to evaluate the detection performance of all cancers and triple-negative breast cancer (TNBC). The FROC curves are also shown for mass/distortion/asymmetry and typical benign calcification in two kinds of populations with four types of breast density. RESULTS: The sensitivity to mass/distortion/asymmetry for the four types of breast (A, B, C, D) are 0.94, 0.92, 0.89, and 0.72, respectively, when false positive per image is 0.25, while these values are 1.00, 0.95, 0.92, and 0.90, respectively, for the amorphous calcification lesions. The sensitivity for the cancer is 0.85 at the same false-positive rate. The TNBC accounts for about 10%-20% of all breast cancers and is more aggressive with poor prognosis than other breast cancers. Herein, we also evaluated performance on the TNBC cases. Our results show that Yizhun AI could detect 75% TNBC lesions at the same false-positive level mentioned above. CONCLUSION: The Yizhun AI model used in our work has good diagnostic efficiency for different types of breast, even for the extremely dense breast. It has a guiding role in the clinical diagnosis of breast cancer. The performance of Yizhun AI on mass/distortion/asymmetry is affected by breast density significantly compared to that on amorphous calcification.
Assuntos
Densidade da Mama , Neoplasias da Mama/diagnóstico por imagem , Aprendizado Profundo , Mamografia/métodos , Adulto , Idoso , Calcinose/diagnóstico por imagem , Feminino , Humanos , Pessoa de Meia-Idade , Curva ROC , Estudos Retrospectivos , Sensibilidade e Especificidade , Neoplasias de Mama Triplo Negativas/diagnóstico por imagemRESUMO
In the mol-ecule of the title compound, C(19)H(19)N(3)O(2), the central pyrazole ring makes dihedral angles of 9.89â (3) and 66.06â (5)° with the two phenyl rings, and the two phenyl rings form an angle of 74.05â (5)°. An intra-molecular C-Hâ¯O hydrogen bond forms a six-membered ring, producing an S(6) ring motif. In the crystal structure, inter-molecular N-Hâ¯O and C-Hâ¯O hydrogen bonds link each mol-ecule to two others, forming an infinite one-dimensional supra-molecular structure along the c axis.