Abstract：
OBJECTIVE: To assess artificial intelligence (AI) ability to evaluate intraprostatic prostate cancer (PCa) on prostate-specific membrane antigen positron emission tomography (PSMA PET) scans prior to active treatment (radiotherapy or prostatectomy).
METHODS: This systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO identifier: CRD42023438706). A search was performed on Medline, Embase, Web of Science, and Engineering Village with the following terms: \'artificial intelligence\', \'prostate cancer\', and \'PSMA PET\'. All articles published up to February 2024 were considered. Studies were included if patients underwent PSMA PET scan to evaluate intraprostatic lesions prior to active treatment. The two authors independently evaluated titles, abstracts, and full text. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used.
RESULTS: Our search yield 948 articles, of which 14 were eligible for inclusion. Eight studies met the primary endpoint of differentiating high-grade PCa. Differentiating between International Society of Urological Pathology (ISUP) Grade Group (GG) ≥3 PCa had an accuracy between 0.671 to 0.992, sensitivity of 0.91, specificity of 0.35. Differentiating ISUP GG ≥4 PCa had an accuracy between 0.83 and 0.88, sensitivity was 0.89, specificity was 0.87. AI could identify non-PSMA-avid lesions with an accuracy of 0.87, specificity of 0.85, and specificity of 0.89. Three studies demonstrated ability of AI to detect extraprostatic extensions with an area under curve between 0.70 and 0.77. Lastly, AI can automate segmentation of intraprostatic lesion and measurement of gross tumour volume.
CONCLUSIONS: Although the current state of AI differentiating high-grade PCa is promising, it remains experimental and not ready for routine clinical application. Benefits of using AI to assess intraprostatic lesions on PSMA PET scans include: local staging, identifying otherwise radiologically occult lesions, standardisation and expedite reporting of PSMA PET scans. Larger, prospective, multicentre studies are needed.
METHODS: This systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO identifier: CRD42023438706). A search was performed on Medline, Embase, Web of Science, and Engineering Village with the following terms: \'artificial intelligence\', \'prostate cancer\', and \'PSMA PET\'. All articles published up to February 2024 were considered. Studies were included if patients underwent PSMA PET scan to evaluate intraprostatic lesions prior to active treatment. The two authors independently evaluated titles, abstracts, and full text. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used.
RESULTS: Our search yield 948 articles, of which 14 were eligible for inclusion. Eight studies met the primary endpoint of differentiating high-grade PCa. Differentiating between International Society of Urological Pathology (ISUP) Grade Group (GG) ≥3 PCa had an accuracy between 0.671 to 0.992, sensitivity of 0.91, specificity of 0.35. Differentiating ISUP GG ≥4 PCa had an accuracy between 0.83 and 0.88, sensitivity was 0.89, specificity was 0.87. AI could identify non-PSMA-avid lesions with an accuracy of 0.87, specificity of 0.85, and specificity of 0.89. Three studies demonstrated ability of AI to detect extraprostatic extensions with an area under curve between 0.70 and 0.77. Lastly, AI can automate segmentation of intraprostatic lesion and measurement of gross tumour volume.
CONCLUSIONS: Although the current state of AI differentiating high-grade PCa is promising, it remains experimental and not ready for routine clinical application. Benefits of using AI to assess intraprostatic lesions on PSMA PET scans include: local staging, identifying otherwise radiologically occult lesions, standardisation and expedite reporting of PSMA PET scans. Larger, prospective, multicentre studies are needed.
摘要：
目的：在主动治疗（放疗或前列腺切除术）之前，评估人工智能（AI）在前列腺特异性膜抗原正电子发射断层扫描（PSMAPET）上评估前列腺内前列腺癌（PCa）的能力。
方法：该系统评价已在国际前瞻性系统评价注册（PROSPERO标识符：CRD42023438706）上注册。在Medline上进行了搜索，Embase,WebofScience,和工程村，使用以下术语：“人工智能”，\'前列腺癌\',和“PSMAPET”。截至2024年2月发表的所有文章都被考虑在内。如果患者在积极治疗之前接受PSMAPET扫描以评估前列腺内病变，则纳入研究。两位作者独立评估了标题，摘要,和全文。使用预测模型偏差风险评估工具(PROBAST)。
结果：我们的搜索结果为948篇文章，其中14人符合入选条件。八项研究达到了区分高级PCa的主要终点。区分国际泌尿外科病理学会（ISUP）分级组（GG）≥3PCa的准确性在0.671至0.992之间，敏感性为0.91，特异性为0.35。鉴别ISUPGG≥4PCa的准确性在0.83～0.88之间，敏感性为0.89，特异性为0.87。AI可以以0.87的准确性，0.85的特异性和0.89的特异性识别非PSMA-aid病变。三项研究表明，AI能够检测前列腺外延伸，曲线下面积在0.70和0.77之间。最后,AI可以自动分割前列腺内病变和测量总肿瘤体积。
结论：尽管AI区分高级PCa的当前状态很有希望，它仍然是实验性的，还没有准备好进行常规的临床应用。使用AI在PSMAPET扫描中评估前列腺内病变的好处包括：局部分期，识别放射学隐匿性病变，PSMAPET扫描的标准化和加速报告。较大,prospective,需要进行多中心研究。
方法：该系统评价已在国际前瞻性系统评价注册（PROSPERO标识符：CRD42023438706）上注册。在Medline上进行了搜索，Embase,WebofScience,和工程村，使用以下术语：“人工智能”，\'前列腺癌\',和“PSMAPET”。截至2024年2月发表的所有文章都被考虑在内。如果患者在积极治疗之前接受PSMAPET扫描以评估前列腺内病变，则纳入研究。两位作者独立评估了标题，摘要,和全文。使用预测模型偏差风险评估工具(PROBAST)。
结果：我们的搜索结果为948篇文章，其中14人符合入选条件。八项研究达到了区分高级PCa的主要终点。区分国际泌尿外科病理学会（ISUP）分级组（GG）≥3PCa的准确性在0.671至0.992之间，敏感性为0.91，特异性为0.35。鉴别ISUPGG≥4PCa的准确性在0.83～0.88之间，敏感性为0.89，特异性为0.87。AI可以以0.87的准确性，0.85的特异性和0.89的特异性识别非PSMA-aid病变。三项研究表明，AI能够检测前列腺外延伸，曲线下面积在0.70和0.77之间。最后,AI可以自动分割前列腺内病变和测量总肿瘤体积。
结论：尽管AI区分高级PCa的当前状态很有希望，它仍然是实验性的，还没有准备好进行常规的临床应用。使用AI在PSMAPET扫描中评估前列腺内病变的好处包括：局部分期，识别放射学隐匿性病变，PSMAPET扫描的标准化和加速报告。较大,prospective,需要进行多中心研究。
