Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies.
Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies.
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DOI：
10.3390/cancers13133318
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发表时间：
2021-07-01
期刊：
影响因子：
5.2
通讯作者：
Punwani S
中科院分区：
文献类型：
--
作者：
Syer T;Mehta P;Antonelli M;Mallett S;Atkinson D;Ourselin S;Punwani S
放射科医生解释说，前列腺多参数磁共振成像(MpMRI)可以识别可能与前列腺癌相对应的异常，其状态后来通过MR引导的靶向活检得到确认。人工智能算法可能会提高放射科医生单独实现的诊断准确性，并缓解前列腺癌诊断路径上的压力，这些压力是由于病例发病率上升和缺乏专业放射科医生读取前列腺癌mpMRI造成的。在这篇综述文章中，我们考虑了比较放射科医生、人工智能算法以及在可能的情况下将两者结合起来的诊断准确性的研究。我们的审查发现，目前没有足够的证据表明人工智能算法的临床部署，这是由于研究设计中的缺陷，以及使用以单中心为主的小型患者队列进行性能比较所造成的偏差。提出了几项建议，以确保未来的研究具有更大的临床影响。使用人工智能(AI)的多参数磁共振成像(MpMRI)对前列腺癌进行计算机辅助诊断(CAD)，可以减少漏诊癌症和不必要的活检，增加放射科医生之间的观察者之间的一致性，并缓解因病例发生率上升和缺乏专业放射科医生阅读前列腺癌mpMRI而造成的压力。然而，需要设计良好的评估研究来证明其疗效高于目前的临床实践。对MEDLINE、EMBASE和arxiv电子数据库进行了系统的搜索，以进行研究，将MRI上前列腺癌检测或分类的CAD与放射科医生的解释和组织病理学参考标准进行比较，以治疗临床怀疑前列腺癌的天真男性。最终分析纳入了27项研究。由于所包括的研究具有很大的异质性，因此本文提出了一种叙事综合。有几项研究报告说，在小的内部患者数据集上，CAD的诊断准确率高于放射科医生的解释，尽管这在少数使用外部患者数据进行评估的研究中没有观察到。我们的审查发现，目前没有足够的证据表明人工智能算法可以在临床上部署。在考虑临床应用之前，还需要进一步的工作来制定和执行方法学标准，促进获得大量不同的数据集，并进行前瞻性评估。
Radiologists interpret prostate multiparametric magnetic resonance imaging (mpMRI) to identify abnormalities that may correspond to prostate cancer, whose status is later confirmed by MR-guided targeted biopsy. Artificial intelligence algorithms may improve the diagnostic accuracy achievable by radiologists alone, as well as alleviate pressures on the prostate cancer diagnostic pathway caused by rising case incidence and a shortage of specialist radiologists to read prostate mpMRI. In this review article, we considered studies that compared the diagnostic accuracy of radiologists, artificial intelligence algorithms, and where possible, a combination of the two. Our review found insufficient evidence to suggest the clinical deployment of artificial intelligence algorithms at present, due to flaws in study designs and biases caused by performance comparisons using small, predominantly single-center patient cohorts. Several recommendations are made to ensure future studies bear greater clinical impact. Computer-aided diagnosis (CAD) of prostate cancer on multiparametric magnetic resonance imaging (mpMRI), using artificial intelligence (AI), may reduce missed cancers and unnecessary biopsies, increase inter-observer agreement between radiologists, and alleviate pressures caused by rising case incidence and a shortage of specialist radiologists to read prostate mpMRI. However, well-designed evaluation studies are required to prove efficacy above current clinical practice. A systematic search of the MEDLINE, EMBASE, and arXiv electronic databases was conducted for studies that compared CAD for prostate cancer detection or classification on MRI against radiologist interpretation and a histopathological reference standard, in treatment-naïve men with a clinical suspicion of prostate cancer. Twenty-seven studies were included in the final analysis. Due to substantial heterogeneities in the included studies, a narrative synthesis is presented. Several studies reported superior diagnostic accuracy for CAD over radiologist interpretation on small, internal patient datasets, though this was not observed in the few studies that performed evaluation using external patient data. Our review found insufficient evidence to suggest the clinical deployment of artificial intelligence algorithms at present. Further work is needed to develop and enforce methodological standards, promote access to large diverse datasets, and conduct prospective evaluations before clinical adoption can be considered.
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影响因子：
5.9
作者：
Litjens GJ;Barentsz JO;Karssemeijer N;Huisman HJ
通讯作者：
Huisman HJ
影响因子：
5.9
作者：
Iyama, Yuji;Nakaura, Takeshi;Yamashita, Yasuyuki
通讯作者：
Yamashita, Yasuyuki
DOI：
10.2214/ajr.19.22573
发表时间：
2020-10
期刊：
AJR. American journal of roentgenology
影响因子：
--
作者：
Mehralivand S;Harmon SA;Shih JH;Smith CP;Lay N;Argun B;Bednarova S;Baroni RH;Canda AE;Ercan K;Girometti R;Karaarslan E;Kural AR;Purysko AS;Rais-Bahrami S;Tonso VM;Magi-Galluzzi C;Gordetsky JB;Macarenco RSES;Merino MJ;Gumuskaya B;Saglican Y;Sioletic S;Warren AY;Barrett T;Bittencourt L;Coskun M;Knauss C;Law YM;Malayeri AA;Margolis DJ;Marko J;Yakar D;Wood BJ;Pinto PA;Choyke PL;Summers RM;Turkbey B
通讯作者：
Turkbey B
影响因子：
2
作者：
Harada, Taisuke;Abe, Takashige;Shinohara, Nobuo
通讯作者：
Shinohara, Nobuo
影响因子：
5.9
作者：
Greer, Matthew D.;Lay, Nathan;Turkbey, Baris
通讯作者：
Turkbey, Baris