Breast lesion classification based on absorption and composition parameters: a look at SOLUS first outcomes
Breast lesion classification based on absorption and composition parameters: a look at SOLUS first outcomes
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基于吸收和成分参数的乳腺病变分类：SOLUS 的首要结果
DOI：
10.1117/12.2648945
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Maffeis G
中科院分区：
文献类型：
--
作者：
Maffeis G
在SOLUS数据库中应用了一种机器学习分类算法，基于漫射光学断层扫描检索到的吸收和成分属性来区分乳腺良恶性病变。Mann-Whitney试验表明氧合血红蛋白（p值= 0.0007）和脂质（0.0387）是病变分类中最重要的成分，但进一步的分析工作仍在进行中。与灵敏度（91%）、特异性（75%）和ROC曲线下面积（0.83）一起，不平衡数据集（27%的恶性病变）的特殊指标被应用于机器学习结果：平衡精度（83%）和马修斯相关系数（0.65）。初步结果强调了光学数据有希望的信息内容。
A machine learning classification algorithm is applied to the SOLUS database to discriminate benign and malignant breast lesions, based on absorption and composition properties retrieved through diffuse optical tomography. The Mann-Whitney test indicates oxy-hemoglobin (p-value = 0.0007) and lipids (0.0387) as the most significant constituents for lesion classification, but work is in progress for further analysis. Together with sensitivity (91%), specificity (75%) and the Area Under the ROC Curve (0.83), special metrics for imbalanced datasets (27% of malignant lesions) are applied to the machine learning outcome: balanced accuracy (83%) and Matthews Correlation Coefficient (0.65). The initial results underline the promising informative content of optical data.
具有集成 TDC 的大面积、快速门控数字 SiPM，适用于便携式和可穿戴时域 NIRS
影响因子：
5.4
作者：
E. Conca;V. Sesta;M. Buttafava;F. Villa;L. D. Sieno;A. D. Mora;D. Contini;P. Taroni;A. Torricelli;A. Pifferi;F. Zappa;A. Tosi
通讯作者：
A. Tosi