Applied Quantum Computing Group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, T1090, Vienna, Austria.
Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria.
Eur J Nucl Med Mol Imaging. 2023 Nov;50(13):3826-3837. doi: 10.1007/s00259-023-06362-6. Epub 2023 Aug 4.
Cancer is a leading cause of death worldwide. While routine diagnosis of cancer is performed mainly with biopsy sampling, it is suboptimal to accurately characterize tumor heterogeneity. Positron emission tomography (PET)-driven radiomic research has demonstrated promising results when predicting clinical endpoints. This study aimed to investigate the added value of quantum machine learning both in simulator and in real quantum computers utilizing error mitigation techniques to predict clinical endpoints in various PET cancer patients.
Previously published PET radiomics datasets including 11C-MET PET glioma, 68GA-PSMA-11 PET prostate and lung 18F-FDG PET with 3-year survival, low-vs-high Gleason risk and 2-year survival as clinical endpoints respectively were utilized in this study. Redundancy reduction with 0.7, 0.8, and 0.9 Spearman rank thresholds (SRT), followed by selecting 8 and 16 features from all cohorts, was performed, resulting in 18 dataset variants. Quantum advantage was estimated by Geometric Difference (GD) score in each dataset variant. Five classic machine learning (CML) and their quantum versions (QML) were trained and tested in simulator environments across the dataset variants. Quantum circuit optimization and error mitigation were performed, followed by training and testing selected QML methods on the 21-qubit IonQ Aria quantum computer. Predictive performances were estimated by test balanced accuracy (BACC) values.
On average, QML outperformed CML in simulator environments with 16-features (BACC 70% and 69%, respectively), while with 8-features, CML outperformed QML with + 1%. The highest average QML advantage was + 4%. The GD scores were ≤ 1.0 in all the 8-feature cases, while they were > 1.0 when QML outperformed CML in 9 out of 11 cases. The test BACC of selected QML methods and datasets in the IonQ device without error mitigation (EM) were 69.94% BACC, while EM increased test BACC to 75.66% (76.77% in noiseless simulators).
We demonstrated that with error mitigation, quantum advantage can be achieved in real existing quantum computers when predicting clinical endpoints in clinically relevant PET cancer cohorts. Quantum advantage can already be achieved in simulator environments in these cohorts when relying on QML.
癌症是全球主要的死亡原因。虽然癌症的常规诊断主要通过活检采样进行，但要准确描述肿瘤异质性仍不理想。正电子发射断层扫描（PET）驱动的放射组学研究在预测临床终点方面显示出了有希望的结果。本研究旨在探讨量子机器学习在利用错误缓解技术预测各种 PET 癌症患者的临床终点方面的附加价值。
本研究利用了先前发表的 PET 放射组学数据集，包括 11C-MET PET 脑胶质瘤、68GA-PSMA-11 PET 前列腺癌和肺 18F-FDG PET，其临床终点分别为 3 年生存率、低风险 vs 高风险格利森评分和 2 年生存率。使用 0.7、0.8 和 0.9 Spearman 等级相关系数（SRT）的冗余减少方法，然后从所有队列中选择 8 和 16 个特征，共产生了 18 个数据集变体。在每个数据集变体中，使用几何差异（GD）评分估计量子优势。在数据集变体中，使用经典机器学习（CML）及其量子版本（QML）训练和测试了五个经典机器学习（CML）和它们的量子版本（QML）。在 21 量子比特的 IonQ Aria 量子计算机上进行了量子电路优化和错误缓解，然后对选定的 QML 方法进行了训练和测试。通过测试平衡准确性（BACC）值估计预测性能。
平均而言，在使用 16 个特征的模拟器环境中，QML 优于 CML（BACC 分别为 70%和 69%），而在使用 8 个特征的情况下，CML 优于 QML，优势为+1%。最高平均 QML 优势为+4%。在所有 8 个特征的情况下，GD 得分均≤1.0，而在 9 个案例中 QML 优于 CML 的情况下，GD 得分均>1.0。在没有错误缓解（EM）的 IonQ 设备中，选定 QML 方法和数据集的测试 BACC 为 69.94%BACC，而 EM 将测试 BACC 提高到 75.66%（无噪声模拟器中为 76.77%）。
我们证明了，在存在错误缓解的情况下，当预测临床上相关的 PET 癌症队列的临床终点时，量子优势可以在真实存在的量子计算机上实现。在这些队列的模拟器环境中，当依赖于 QML 时，量子优势已经可以实现。
