Mansour Sahar, Kamal Rasha, Hussein Samar Ahmed, Emara Mostafa, Kassab Yomna, Taha Sherif Nasser, Gomaa Mohammed Mohammed Mohammed
Women's Imaging Unit, Radiology Department - Kasr ElAiny Hospital- Cairo University, Baheya center for early breast cancer detection and treatment, Egypt.
Baheya center for early breast cancer detection and treatment, Egypt.
Eur J Radiol Open. 2024 Dec 25;14:100629. doi: 10.1016/j.ejro.2024.100629. eCollection 2025 Jun.
To investigate the impact of artificial intelligence (AI) reading digital mammograms in increasing the chance of detecting missed breast cancer, by studying the AI- flagged early morphology indictors, overlooked by the radiologist, and correlating them with the missed cancer pathology types.
Mammograms done in 2020-2023, presenting breast carcinomas (n = 1998), were analyzed in concordance with the prior one year's result (2019-2022) assumed negative or benign. Present mammograms reviewed for the descriptors: asymmetry, distortion, mass, and microcalcifications. The AI presented abnormalities by overlaying color hue and scoring percentage for the degree of suspicion of malignancy.
Prior mammogram with AI marking compromised 54 % (n = 555), and in the present mammograms, AI targeted 904 (88 %) carcinomas. The descriptor proportion of "asymmetry" was the common presentation of missed breast carcinoma (64.1 %) in the prior mammograms and the highest detection rate for AI was presented by "distortion" (100 %) followed by "grouped microcalcifications" (80 %). AI performance to predict malignancy in previously assigned negative or benign mammograms showed sensitivity of 73.4 %, specificity of 89 %, and accuracy of 78.4 %.
Reading mammograms with AI significantly enhances the detection of early cancerous changes, particularly in dense breast tissues. The AI's detection rate does not correlate with specific pathological types of breast cancer, highlighting its broad utility. Subtle mammographic changes in postmenopausal women, not corroborated by ultrasound but marked by AI, warrant further evaluation by advanced applications of digital mammograms and close interval AI-reading mammogram follow up to minimize the potential for missed breast carcinoma.
通过研究人工智能（AI）标记的早期形态学指标（放射科医生忽略的指标）并将其与漏诊癌症的病理类型相关联，探讨人工智能读取数字化乳腺钼靶片对提高乳腺癌漏诊检出几率的影响。
分析2020 - 2023年进行的乳腺钼靶片（其中有乳腺癌病例1998例），并与之前一年（2019 - 2022年）假定为阴性或良性的结果进行对照。对当前的乳腺钼靶片检查描述符：不对称、变形、肿块和微钙化。人工智能通过叠加颜色色调和对恶性怀疑程度进行百分比评分来呈现异常情况。
之前有AI标记的乳腺钼靶片占54%（n = 555），在当前的乳腺钼靶片中，AI检测出904例（88%）癌症。“不对称”描述符比例是之前乳腺钼靶片中漏诊乳腺癌的常见表现（64.1%），AI检测率最高的是“变形”（100%），其次是“成簇微钙化”（80%）。AI在预测先前判定为阴性或良性的乳腺钼靶片中恶性病变的表现为：灵敏度73.4%，特异度89%，准确度78.4%。
使用人工智能读取乳腺钼靶片可显著提高早期癌变的检测率, 尤其是在致密乳腺组织中。人工智能的检测率与乳腺癌的特定病理类型无关，这突出了其广泛的实用性。绝经后女性乳腺钼靶片的细微变化，虽未得到超声证实但被人工智能标记，需要通过数字化乳腺钼靶片的高级应用进行进一步评估，并密切间隔进行人工智能读取乳腺钼靶片随访，以尽量减少乳腺癌漏诊的可能性。
