Construction and validation of a personalized nomogram of ultrasound for pretreatment prediction of breast cancer patients sensitive to neoadjuvant chemotherapy.

Autor:	Zhang MQ; Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China., Du Y; Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China., Zha HL; Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China., Liu XP; Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China., Cai MJ; Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China., Chen ZH; Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China., Chen R; Department of Breast surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China., Wang J; Department of Breast surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China., Wang SJ; Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China., Zhang JL; Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China., Li CY; Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Jazyk:	angličtina
Zdroj:	The British journal of radiology [Br J Radiol] 2022 Dec 01; Vol. 95 (1140), pp. 20220626. Date of Electronic Publication: 2022 Nov 15.
DOI:	10.1259/bjr.20220626
Abstrakt:	Objective: To construct a combined radiomics model based on pre-treatment ultrasound for predicting of advanced breast cancers sensitive to neoadjuvant chemotherapy (NAC). Methods: A total of 288 eligible breast cancer patients who underwent NAC before surgery were enrolled in the retrospective study cohort. Radiomics features reflecting the phenotype of the pre-NAC tumors were extracted. With features selected using the least absolute shrinkage and selection operator (LASSO) regression, radiomics signature (Rad-score) was established based on the pre-NAC ultrasound. Then, radiomics nomogram of ultrasound (RU) was established on the basis of the best radiomic signature incorporating independent clinical features. The performance of RU was evaluated in terms of calibration curve, area under the curve (AUC), and decision curve analysis (DCA). Results: Nine features were selected to construct the radiomics signature in the training cohort. Combined with independent clinical characteristics, the performance of RU for identifying Grade 4-5 patients was significantly superior than the clinical model and Rad-score alone ( p < 0.05, as per the Delong test), which achieved an AUC of 0.863 (95% CI, 0.814-0.963) in the training group and 0.854 (95% CI, 0.776-0.931) in the validation group. DCA showed that this model satisfactory clinical utility, suggesting its robustness as a response predictor. Conclusion: This study demonstrated that RU has a potential role in predicting drug-sensitive breast cancers. Advances in Knowledge: Aiming at early detection of Grade 4-5 breast cancer patients, the radiomics nomogram based on ultrasound has been approved as a promising indicator with high clinical utility. It is the first application of ultrasound-based radiomics nomogram to distinguish drug-sensitive breast cancers.
Databáze:	MEDLINE
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