Background

To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC).

Methods

One hundred two patients with histopathologically proven PDAC were retrospectively assessed as training cohort, and 30 prospectively accrued and retrospectively enrolled patients served as independent validation cohort (IVC). Tumors were segmented on preoperative apparent diffusion coefficient (ADC) maps, and radiomic features were extracted. A random forest ML algorithm was fit to the training cohort and tested in the IVC. Histopathological subtype of tumor samples was assessed by immunohistochemistry in 21 IVC patients. Individual radiomic feature importance was evaluated by assessment of tree node Gini impurity decrease and recursive feature elimination. Fisher’s exact test, 95% confidence intervals (CI), and receiver operating characteristic area under the curve (ROC-AUC) were used.

Results

The ML algorithm achieved 87% sensitivity (95% IC 67.3–92.7), 80% specificity (95% CI 74.0–86.7), and ROC-AUC 90% for the prediction of above- versus below-median OS in the IVC. Heterogeneity-related features were highly ranked by the model. Of the 21 patients with determined histopathological subtype, 8/9 patients predicted to experience below-median OS exhibited the quasi-mesenchymal subtype, whilst 11/12 patients predicted to experience above-median OS exhibited a non-quasi-mesenchymal subtype (p < 0.001).

Conclusion

ML application to ADC radiomics allowed OS prediction with a high diagnostic accuracy in an IVC. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging in PDAC pre-operative subtyping and prognosis.

中文翻译：

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通过术前弥散加权成像预测胰腺导管腺癌生存率和肿瘤亚型的机器学习模型。

背景

开发一种监督的机器学习（ML）算法预测上文对从弥散加权成像衍生radiomic特征患者胰腺导管腺癌（PDAC）以下的中值总存活期（OS）。

方法

回顾性评估了经组织病理学证实的PDAC的102例患者作为训练队列，将30名前瞻性应征和回顾性纳入的患者作为独立验证队列（IVC）。在术前表观扩散系数（ADC）图上对肿瘤进行分割，并提取放射学特征。随机森林ML算法适合于训练队列，并在IVC中进行了测试。通过免疫组织化学评估了21名IVC患者的肿瘤样品的组织病理学亚型。通过评估树节点吉尼杂质减少和递归特征消除来评估个体放射性特征的重要性。使用费舍尔精确测试，95％置信区间（CI）和曲线下的接收器工作特征区域（ROC-AUC）。

结果

ML算法在IVC中预测高于或低于中位OS时，实现了87％的灵敏度（95％的IC 67.3-92.7），80％的特异性（95％的CI 74.0-86.7）和ROC-AUC 90％。与异质性相关的特征在模型中排名很高。在确定了组织病理学亚型的21例患者中，预计经历中度OS低于中值的8/9患者表现为准间质亚型，而预测经历中度OS高于中值OS的11/12患者表现为非准间质亚型（p < 0.001）。

结论

ML在ADC放射学中的应用允许在IVC中以较高的诊断精度进行OS预测。临床相关的组织病理学亚型与模型预测的高度重叠，突显了定量成像在PDAC术前亚型和预后中的潜力。