Focal breast lesion characterization according to the BI-RADS US lexicon: role of a computer-aided decision-making support

Tommaso Vincenzo Bartolotta, Massimo Midiri, Roberto Lagalla, Domenica Matranga, Alessia Orlando, Maria Laura Di Vittorio, Francesco Amato, Raffele Ienzi, Alessandra Cirino, Vito Cantisani, Tommaso Vincenzo Bartolotta, Roberto Lagalla, Massimo Midiri, Domenica Matranga, Alessandra Cirino, Alessia Angela Maria Orlando, Francesco Amato, Maria Laura Di Vittorio

Risultato della ricerca: Articlepeer review

26 Citazioni (Scopus)


Objectives: to assess the diagnostic performance of a computer-guided decision- making software (S-Detect) in the US characterization of focal breast lesions (FBLs), according to the radiologist's experience.Materials and Methods: 300 FBLs (size: 2.6-47.2 mm; mean: 13.2 mm) in 255 patients (mean age: 51 years) were prospectively assessed in consensus according to BIRADS US lexicon by two experienced radiologists without and with S-Detect; to evaluate intra and inter-observer agreement, the same 300 FBLs were independently evaluated by two residents at baseline and after 3 months.Results: 120/300 (40%) FBLs were malignant, 2/300 (0.7%) high-risk and 178/300 (59.3%) benign. Experts review showed a not significant increase in Sensitivity, Specificity, PPV and NPV with S-Detect (97.5%, 86.5%, 83.2%, 98.1%) than without (91.8%, 81.5%, 77.2%, 93.6%) (p>0.05), as confirmed by ROC curve analysis (0.95 with and 0.92 without S-Detect [p=0.0735]). A significant higher area under the ROC curve (0.88) with S-Detect than without (0.85) was found for Resident #1 (p=0.0067) and Resident #2 (0.83 without and 0.87 with S-Detect [p=0.0302]). Intra-observer agreement (k score) improved with S-Detect from 0.69 to 0.78 for Resident #1 (p>0.05) and from 0.69 to 0.81 for Resident #2 (p>0.05). Inter-observer agreement improved with S-Detect from 0.67 to 0.7 (baseline; p>0.05) and from 0.63 to 0.77 (after 3 months; p>0.05). According to S-Detect-guided re-classification, 27/64 (42.2%) FBLs underwent a correct change in clinical management, 25/64 (39.1%) FBLs underwent no change and 12/68 (18.7%) FBLs underwent an uncorrect change.Conclusion: S-Detect can be used as an effective tool for classification of FBLs, especially for less experienced physicians.
Lingua originaleEnglish
pagine (da-a)498-506
Numero di pagine9
Stato di pubblicazionePublished - 2018

All Science Journal Classification (ASJC) codes

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