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    • False-negative and False-Positive Outcomes of Computer-Aided Detection on Brain Metastasis: Secondary Analysis of a Multicenter, Multireader Study

    Xiao Luo,Yadi Yang,Shaohan Yin,Hui Li,Weijing Zhang,Guixiao Xu,Weixiong Fan,Dechun Zheng,Jianpeng Li,Dinggang Shen,Yaozong Gao,Ying Shao,Xiaohua Ban,Jing Li,Shanshan Lian,Cheng Zhang,Lidi Ma,Cuiping Lin,Yingwei Luo,Fan Zhou,Shiyuan Wang,Ying Sun,Rong Zhang,Chuanmiao Xie

    Neuro-oncology（2022）SCI 1区

    Sun Yat Sen Univ | Meizhou Peoples Hosp | Fujian Med Univ | Southern Med Univ | Shanghai United Imaging Intelligence Co Ltd

    Cited 1|Views145
    Abstract
    BACKGROUND:Errors have seldom been evaluated in computer-aided detection on brain metastases. This study aimed to analyze false negatives (FNs) and false positives (FPs) generated by a brain metastasis detection system (BMDS) and by readers.METHODS:A deep learning-based BMDS was developed and prospectively validated in a multicenter, multireader study. Ad hoc secondary analysis was restricted to the prospective participants (148 with 1,066 brain metastases and 152 normal controls). Three trainees and 3 experienced radiologists read the MRI images without and with the BMDS. The number of FNs and FPs per patient, jackknife alternative free-response receiver operating characteristic figure of merit (FOM), and lesion features associated with FNs were analyzed for the BMDS and readers using binary logistic regression.RESULTS:The FNs, FPs, and the FOM of the stand-alone BMDS were 0.49, 0.38, and 0.97, respectively. Compared with independent reading, BMDS-assisted reading generated 79% fewer FNs (1.98 vs 0.42, P < .001); 41% more FPs (0.17 vs 0.24, P < .001) but 125% more FPs for trainees (P < .001); and higher FOM (0.87 vs 0.98, P < .001). Lesions with small size, greater number, irregular shape, lower signal intensity, and located on nonbrain surface were associated with FNs for readers. Small, irregular, and necrotic lesions were more frequently found in FNs for BMDS. The FPs mainly resulted from small blood vessels for the BMDS and the readers.CONCLUSIONS:Despite the improvement in detection performance, attention should be paid to FPs and small lesions with lower enhancement for radiologists, especially for less-experienced radiologists.
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    brain neoplasms,deep learning,magnetic resonance imaging,radiographic image interpretation,ROC curve
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    Deep Learning Ensembles for Detecting Brain Metastases in Longitudinal Multi-Modal MRI Studies

    Bartosz Machura, Damian Kucharski,Oskar Bozek, Bartosz Eksner, Bartosz Kokoszka, Tomasz Pekala, Mateusz Radom,Marek Strzelczak,Lukasz Zarudzki,Benjamin Gutierrez-Becker,Agata Krason,Jean Tessier,
    Computerized Medical Imaging and Graphics 2024

    被引用1

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    【要点】：本研究分析了脑转移瘤检测系统中计算机辅助检测的假阴性（FNs）和假阳性（FPs）结果，指出尽管性能有所提高，但应注意FPs和低增强的小病变，尤其是对于经验较少的放射科医师。
    

    【方法】：通过多中心、多读者参与的二次分析，使用基于深度学习的脑转移瘤检测系统（BMDS）对148名患者的1066个脑转移瘤和152名正常对照的MRI图像进行独立阅读和BMDS辅助阅读，并分析FNs和FPs的数量和相关特征。
    

    【实验】：实验在148名患者和152名正常对照的MRI数据上进行，使用的是未公开指定的数据集。结果显示，BMDS的FNs、FPs和FOM分别为0.49、0.38和0.97，BMDS辅助阅读相较于独立阅读减少了79%的FNs，增加了41%的FPs，尤其是对于初级放射科医师增加了125%的FPs，并且FOM更高。对于放射科医师来说，小、多、形状不规则、信号强度低和非脑表面位置的病变更容易产生FNs；而对于BMDS，小、不规则和坏死的病变更常出现FNs。FPs主要是由于BMDS和放射科医师对小型血管的误判。