Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis.
Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis.
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DOI：
10.1038/s41746-022-00559-z
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发表时间：
2022-02-15
影响因子：
15.2
通讯作者：
Qiao Y
中科院分区：
文献类型：
--
作者：
Xue P;Wang J;Qin D;Yan H;Qu Y;Seery S;Jiang Y;Qiao Y
乳腺癌和宫颈癌的早期准确检测对于治疗成功至关重要。在这里，我们进行了一项荟萃分析，以评估深度学习（DL）算法在早期乳腺癌和宫颈癌识别中的诊断性能。还研究了四个亚组：癌症类型（乳腺癌或宫颈癌）、验证类型（内部或外部）、成像方式（乳腺X线摄影、超声、细胞学或阴道镜检查）和DL算法与临床医生。35项研究被认为有资格进行系统性综述，其中20项进行了荟萃分析，合并灵敏度为88%（95% CI 85-90%），特异性为84%（79-87%），AUC为0.92（0.90-0.94）。在所有亚组中突出显示了使用类似DL算法的可接受诊断性能。因此，DL算法可以用于使用医学成像检测乳腺癌和宫颈癌，具有与人类临床医生等同的性能。然而，这一初步断言是基于设计和报告相对较差的研究，这可能导致偏倚和高估算法性能。围绕研究方法和报告的循证标准化指南需要提高DL研究的质量。
Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer identification. Four subgroups are also investigated: cancer type (breast or cervical), validation type (internal or external), imaging modalities (mammography, ultrasound, cytology, or colposcopy), and DL algorithms versus clinicians. Thirty-five studies are deemed eligible for systematic review, 20 of which are meta-analyzed, with a pooled sensitivity of 88% (95% CI 85–90%), specificity of 84% (79–87%), and AUC of 0.92 (0.90–0.94). Acceptable diagnostic performance with analogous DL algorithms was highlighted across all subgroups. Therefore, DL algorithms could be useful for detecting breast and cervical cancer using medical imaging, having equivalent performance to human clinicians. However, this tentative assertion is based on studies with relatively poor designs and reporting, which likely caused bias and overestimated algorithm performance. Evidence-based, standardized guidelines around study methods and reporting are required to improve the quality of DL research.
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影响因子：
6.4
作者：
Hunt B;Fregnani JHTG;Brenes D;Schwarz RA;Salcedo MP;Possati-Resende JC;Antoniazzi M;de Oliveira Fonseca B;Santana IVV;de Macêdo Matsushita G;Castle PE;Schmeler KM;Richards-Kortum R
通讯作者：
Richards-Kortum R
影响因子：
15.2
作者：
Aggarwal R;Sounderajah V;Martin G;Ting DSW;Karthikesalingam A;King D;Ashrafian H;Darzi A
通讯作者：
Darzi A
影响因子：
13.8
作者：
Holmström O;Linder N;Kaingu H;Mbuuko N;Mbete J;Kinyua F;Törnquist S;Muinde M;Krogerus L;Lundin M;Diwan V;Lundin J
通讯作者：
Lundin J
人工智能辅助细胞学诊断系统在大规模宫颈癌筛查中的应用：70万女性人群队列研究
影响因子：
4
作者：
Bao H;Sun X;Zhang Y;Pang B;Li H;Zhou L;Wu F;Cao D;Wang J;Turic B;Wang L
通讯作者：
Wang L
影响因子：
105.7
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.