Da Mutten Raffaele, Zanier Olivier, Bottini Massimo, Baumann Yves, Ciobanu-Caraus Olga, Regli Luca, Serra Carlo, Staartjes Victor E
Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Department of Computer Science, ETH Zurich, Zurich, Switzerland.
Neuroimage Rep. 2025 Jan 29;5(1):100233. doi: 10.1016/j.ynirp.2025.100233. eCollection 2025 Mar.
The Zurich Pituitary Score (ZPS) is an externally validated radiological grading scale to predict the likelihood of gross total resection (GTR) on coronal T1w magnetic resonance imaging of pituitary adenomas. The ZPS is based on the ratio of maximum tumor horizontal diameter and minimum intercarotid distance and on carotid artery encasement. While the interobserver agreement of the ZPS was relatively good, automated grading would be beneficial.
A nnU-Net algorithm was trained to segment the manually labeled tumor tissue and the cavernous segment of the internal carotid artery. Subsequently, maximum horizontal tumor diameter and minimum intercarotid distance were extracted. Last, a seed-growing algorithm checked for encasement of the carotid to determine the ZPS.
213 patients were included, of which 128 (60%) had non-functioning adenomas, 49 (23%) a growth-hormone secreting and 19 (9%) a prolactin producing tumor. Accordingly, ZPS gradings were I = 63 (30%), II = 94 (44%), III = 41 (19%) and IV = 15 (7%). Dice score (mean ± standard deviation) for the tumor, left carotid, and right carotid in training validation of 0.78 ± 0.24, 0.62 ± 0.31, 0.62 ± 0.30 and during holdout testing of 0.79 ± 0.24, 0.59 ± 0.32, 0.58 ± 0.33 was reached. After the exclusion of two cases with poor segmentation results, intraclass correlation coefficients [95% CI] for the intercarotid distance, maximum horizontal tumor diameter, and the ZPS ratio of the two measurements were 0.89 [0.80, 0.94], 0.91 [0.82, 0.96], 0.80 [0.66, 0.89] respectively. Cohen's weighted Kappa for the final ZPS grading was 0.79 [0.68, 0.90] and Spearman rank correlation was 0.83.
We developed and internally validated a machine learning-based method for fully automated grading of the ZPS. Generally, robust segmentation performance was achieved. While ZPS grading generally worked well, human ratings remain superior in many situations. Especially for raters with low experience, our approach offers a solid and objective alternative.
苏黎世垂体评分（ZPS）是一种经过外部验证的放射学分级量表，用于在垂体腺瘤的冠状位T1加权磁共振成像上预测大体全切（GTR）的可能性。ZPS基于肿瘤最大水平直径与颈内动脉最小间距之比以及颈动脉包绕情况。虽然ZPS的观察者间一致性相对较好，但自动化分级会更有益。
训练一个nnU-Net算法来分割手动标记的肿瘤组织和颈内动脉的海绵窦段。随后，提取肿瘤最大水平直径和颈内动脉最小间距。最后，使用种子生长算法检查颈动脉包绕情况以确定ZPS。
纳入213例患者，其中128例（60%）为无功能腺瘤，49例（23%）为生长激素分泌型肿瘤，19例（9%）为催乳素分泌型肿瘤。相应地，ZPS分级为I级 = 63例（30%），II级 = 94例（44%），III级 = 41例（19%），IV级 = 15例（7%）。在训练验证中，肿瘤、左侧颈动脉和右侧颈动脉的骰子系数（均值±标准差）分别为0.78±0.24、0.62±0.31、0.62±0.30，在保留测试中分别为0.79±0.24、0.59±0.32、0.58±0.33。排除两例分割结果较差的病例后，颈内动脉间距、肿瘤最大水平直径以及两者测量值的ZPS比值的组内相关系数[95%CI]分别为0.89[0.80, 0.94]、0.91[0.82, 0.96]、0.80[0.66, 0.89]。最终ZPS分级 Cohen加权Kappa值为0.79[0.68, 0.90]，Spearman等级相关性为0.83。
我们开发并在内部验证了一种基于机器学习的方法，用于ZPS的全自动分级。总体而言，实现了稳健的分割性能。虽然ZPS分级总体效果良好，但在许多情况下人工评分仍更具优势。特别是对于经验不足的评分者，我们的方法提供了一种可靠且客观的替代方案。
