{"title":"改进数字乳房 X 光照片的病变体积测量方法","authors":"","doi":"10.1016/j.media.2024.103269","DOIUrl":null,"url":null,"abstract":"<div><p>Lesion volume is an important predictor for prognosis in breast cancer. However, it is currently impossible to compute lesion volumes accurately from digital mammography data, which is the most popular and readily available imaging modality for breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model that allows to estimate lesion volumes on processed mammogram. Processed mammograms are the images routinely used by radiologists in clinical practice as well as in breast cancer screening and are available in medical centers. Processed mammograms are obtained from raw mammograms, which are the X-ray data coming directly from the scanner, by applying certain vendor-specific non-linear transformations. At the core of our volume estimation method is a physics-based algorithm for measuring lesion volumes on raw mammograms. We subsequently extend this algorithm to processed mammograms via a deep learning image-to-image translation model that produces synthetic raw mammograms from processed mammograms in a multi-vendor setting. We assess the reliability and validity of our method using a dataset of 1778 mammograms with an annotated mass. Firstly, we investigate the correlations between lesion volumes computed from mediolateral oblique and craniocaudal views, with a resulting Pearson correlation of 0.93 [95% confidence interval (CI) 0.92 – 0.93]. Secondly, we compare the resulting lesion volumes from true and synthetic raw data, with a resulting Pearson correlation of 0.998 [95%CI 0.998 – 0.998] . Finally, for a subset of 100 mammograms with a malignant mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0.81 [95%CI 0.73 – 0.87] for consistency and 0.78 [95%CI 0.66 – 0.86] for absolute agreement. In conclusion, we developed an algorithm to measure mammographic lesion volume that reached excellent reliability and good validity, when using MRI as ground truth. The algorithm may play a role in lesion characterization and breast cancer prognostication on mammograms.</p></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":null,"pages":null},"PeriodicalIF":10.7000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1361841524001944/pdfft?md5=491b6626e250d1b4cbbb721fb33c4dc3&pid=1-s2.0-S1361841524001944-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Improving lesion volume measurements on digital mammograms\",\"authors\":\"\",\"doi\":\"10.1016/j.media.2024.103269\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Lesion volume is an important predictor for prognosis in breast cancer. However, it is currently impossible to compute lesion volumes accurately from digital mammography data, which is the most popular and readily available imaging modality for breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model that allows to estimate lesion volumes on processed mammogram. Processed mammograms are the images routinely used by radiologists in clinical practice as well as in breast cancer screening and are available in medical centers. Processed mammograms are obtained from raw mammograms, which are the X-ray data coming directly from the scanner, by applying certain vendor-specific non-linear transformations. At the core of our volume estimation method is a physics-based algorithm for measuring lesion volumes on raw mammograms. We subsequently extend this algorithm to processed mammograms via a deep learning image-to-image translation model that produces synthetic raw mammograms from processed mammograms in a multi-vendor setting. We assess the reliability and validity of our method using a dataset of 1778 mammograms with an annotated mass. Firstly, we investigate the correlations between lesion volumes computed from mediolateral oblique and craniocaudal views, with a resulting Pearson correlation of 0.93 [95% confidence interval (CI) 0.92 – 0.93]. Secondly, we compare the resulting lesion volumes from true and synthetic raw data, with a resulting Pearson correlation of 0.998 [95%CI 0.998 – 0.998] . Finally, for a subset of 100 mammograms with a malignant mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0.81 [95%CI 0.73 – 0.87] for consistency and 0.78 [95%CI 0.66 – 0.86] for absolute agreement. In conclusion, we developed an algorithm to measure mammographic lesion volume that reached excellent reliability and good validity, when using MRI as ground truth. 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引用次数: 0
摘要
病灶体积是预测乳腺癌预后的重要指标。然而,目前还无法从数字乳腺 X 射线照片数据中准确计算病灶体积,而数字乳腺 X 射线照片是最流行、最容易获得的乳腺癌成像模式。我们开发了一种模型,可以估算处理过的乳腺 X 射线照片上的病灶体积,为更精确地测量数字乳腺 X 射线照片上的病灶体积迈出了一步。经过处理的乳房 X 光照片是放射科医生在临床实践和乳腺癌筛查中经常使用的图像,可在医疗中心获得。处理后的乳房 X 光照片是由原始乳房 X 光照片(即直接来自扫描仪的 X 光数据)通过应用某些供应商特定的非线性变换获得的。我们的体积估算方法的核心是一种基于物理学的算法,用于测量原始乳房 X 光照片上的病变体积。随后,我们通过深度学习图像到图像转换模型将该算法扩展到处理后的乳房X光照片,该模型可在多供应商环境下从处理后的乳房X光照片生成合成的原始乳房X光照片。我们使用 1778 张带有肿块注释的乳房 X 光照片数据集来评估我们方法的可靠性和有效性。首先,我们研究了从内外侧斜切面和颅尾切面计算出的病灶体积之间的相关性,得出的皮尔逊相关性为 0.93 [95% 置信区间 (CI) 0.92 - 0.93]。其次,我们比较了真实原始数据和合成原始数据得出的病灶体积,得出的皮尔逊相关性为 0.998 [95%CI 0.998 - 0.998] 。最后,我们分析了 100 张有恶性肿块且同时有核磁共振检查的乳房 X 光照片子集的病灶体积与核磁共振成像之间的一致性,结果显示一致性的类内相关系数为 0.81 [95%CI 0.73 - 0.87],绝对一致性为 0.78 [95%CI 0.66 - 0.86]。总之,我们开发了一种测量乳腺X线病变体积的算法,该算法以磁共振成像为基本真相,具有极佳的可靠性和有效性。该算法可在乳腺 X 线造影的病灶特征描述和乳腺癌预后中发挥作用。
Improving lesion volume measurements on digital mammograms
Lesion volume is an important predictor for prognosis in breast cancer. However, it is currently impossible to compute lesion volumes accurately from digital mammography data, which is the most popular and readily available imaging modality for breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model that allows to estimate lesion volumes on processed mammogram. Processed mammograms are the images routinely used by radiologists in clinical practice as well as in breast cancer screening and are available in medical centers. Processed mammograms are obtained from raw mammograms, which are the X-ray data coming directly from the scanner, by applying certain vendor-specific non-linear transformations. At the core of our volume estimation method is a physics-based algorithm for measuring lesion volumes on raw mammograms. We subsequently extend this algorithm to processed mammograms via a deep learning image-to-image translation model that produces synthetic raw mammograms from processed mammograms in a multi-vendor setting. We assess the reliability and validity of our method using a dataset of 1778 mammograms with an annotated mass. Firstly, we investigate the correlations between lesion volumes computed from mediolateral oblique and craniocaudal views, with a resulting Pearson correlation of 0.93 [95% confidence interval (CI) 0.92 – 0.93]. Secondly, we compare the resulting lesion volumes from true and synthetic raw data, with a resulting Pearson correlation of 0.998 [95%CI 0.998 – 0.998] . Finally, for a subset of 100 mammograms with a malignant mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0.81 [95%CI 0.73 – 0.87] for consistency and 0.78 [95%CI 0.66 – 0.86] for absolute agreement. In conclusion, we developed an algorithm to measure mammographic lesion volume that reached excellent reliability and good validity, when using MRI as ground truth. The algorithm may play a role in lesion characterization and breast cancer prognostication on mammograms.
期刊介绍:
Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.