Background and Aims: Limited methods exist to accurately characterize the risk of malignant progression of liver lesions. Enhancement pattern mapping (EPM) measures voxel-based root mean square deviation (RMSD) of parenchyma and the contrast-to-noise (CNR) ratio enhances in malignant lesions. This study investigates the utilization of EPM to differentiate between HCC versus cirrhotic parenchyma with and without benign lesions.
Methods: Patients with cirrhosis undergoing MRI surveillance were studied prospectively. Cases (n=48) were defined as patients with LI-RADS 3 and 4 lesions who developed HCC during surveillance. Controls (n=99) were patients with and without LI-RADS 3 and 4 lesions who did not develop HCC. Manual and automated EPM signals of liver parenchyma between cases and controls were quantitatively validated on an independent patient set using cross validation with manual methods avoiding parenchyma with artifacts or blood vessels.
Results: With manual EPM, RMSD of 0.37 was identified as a cutoff for distinguishing lesions that progress to HCC from background parenchyma with and without lesions on pre-diagnostic scans (median time interval 6.8 months) with an area under the curve (AUC) of 0.83 (CI: 0.73– 0.94) and a sensitivity, specificity, and accuracy of 0.65, 0.97, and 0.89, respectively. At the time of diagnostic scans, a sensitivity, specificity, and accuracy of 0.79, 0.93, and 0.88 were achieved with manual EPM with an AUC of 0.89 (CI: 0.82– 0.96). EPM RMSD signals of background parenchyma that did not progress to HCC in cases and controls were similar (case EPM: 0.22 ± 0.08, control EPM: 0.22 ± 0.09, p=0.8). Automated EPM produced similar quantitative results and performance.
Conclusion: With manual EPM, a cutoff of 0.37 identifies quantifiable differences between HCC cases and controls approximately six months prior to diagnosis of HCC with an accuracy of 89%.

Plain Language Summary: Current surveillance and diagnostic methods in hepatocellular carcinoma are suboptimal. Enhancement pattern mapping is an imaging technique that quantifies lesion signals and may be useful in diagnostic and surveillance methods. Enhancement pattern mapping describes quantifiable differences between malignant and benign liver tissue on contrast-enhanced MRI. It amplifies lesion signal and distinguishes malignancy in a surveillance population. The novel imaging technique was investigated at single institution and analyzed lesions compared to cirrhotic parenchyma. Future efforts will include further risk stratification across LI-RADS group categories. The results provide evidence that enhancement pattern mapping uses available imaging data to distinguish hepatocellular carcinoma from non-cancerous parenchyma with and without benign lesions on scans six months prior to diagnosis with standard MRI. The technique introduces a prospective modality to improve diagnostic accuracy and early detection with the goal of improving clinical outcomes.

Keywords: LI-RADS, MRI, radiomics, artificial intelligence, liver cancer, machine learning


中文翻译：

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肝硬化患者肝细胞癌早期检测的增强模式图谱

背景和目的：准确表征肝脏病变恶性进展风险的方法有限。增强模式映射（EPM）测量实质的基于体素的均方根偏差（RMSD），并且恶性病变中的对比度与噪声（CNR）比增强。本研究探讨了利用 EPM 来区分 HCC 与肝硬化实质（有无良性病变）。
方法：对接受 MRI 监测的肝硬化患者进行前瞻性研究。病例 (n=48) 被定义为在监测期间发生 HCC 的 LI-RADS 3 和 4 病变患者。对照 (n=99) 是有或没有 LI-RADS 3 和 4 病变且未发展为 HCC 的患者。使用交叉验证和手动方法，在独立患者组上定量验证病例和对照之间肝实质的手动和自动 EPM 信号，避免实质上存在伪影或血管。
结果：使用手动 EPM，RMSD 0.37 被确定为区分诊断前扫描（中位时间间隔 6.8 个月）中进展为 HCC 的病变与有无病变的背景实质的临界值（中位时间间隔 6.8 个月），曲线下面积 (AUC) 为0.83（CI：0.73-0.94），灵敏度、特异性和准确度分别为 0.65、0.97 和 0.89。在诊断扫描时，手动 EPM 的灵敏度、特异性和准确度分别为 0.79、0.93 和 0.88，AUC 为 0.89（CI：0.82-0.96）。病例和对照中未进展为 HCC 的背景实质的 EPM RMSD 信号相似（病例 EPM：0.22 ± 0.08，对照 EPM：0.22 ± 0.09，p=0.8）。自动化 EPM 产生了类似的定量结果和性能。
结论：使用手动 EPM，在诊断 HCC 前大约六个月，以 0.37 为截止值识别 HCC 病例和对照之间的可量化差异，准确度为 89%。

通俗易懂的语言总结：目前肝细胞癌的监测和诊断方法并不理想。增强模式映射是一种量化病变信号的成像技术，可用于诊断和监测方法。增强模式映射描述了对比增强 MRI 上恶性和良性肝组织之间的可量化差异。它放大病变信号并区分监测人群中的恶性肿瘤。这种新颖的成像技术在单一机构进行了研究，并与肝硬化实质进行了比较分析。未来的工作将包括进一步对 LI-RADS 组类别进行风险分层。结果提供证据表明，在标准 MRI 诊断前 6 个月的扫描中，增强模式映射使用可用的成像数据来区分肝细胞癌与有或没有良性病变的非癌性实质。该技术引入了一种前瞻性模式来提高诊断准确性和早期检测，以改善临床结果。

关键词： LI-RADS，MRI，放射组学，人工智能，肝癌，机器学习