研究背景：憂鬱症在胰臟癌患者中非常普遍。雖然先前的研究已經揭示了多種情況下憂鬱症和發炎之間的關聯，但這種關係在胰臟癌中仍未被探索。因此，本研究旨在探討胰臟癌患者發炎指標與憂鬱症之間的關係。此外，本研究亦利用多種機器學習（machine learning, ML）技術結合發炎指標來構建該患者群體之憂鬱症預測模型。
研究方法：自2021年5月至2023年11月，共有328名胰臟癌患者收入於本研究。使用病人健康問卷（Patient Health Questionnaire, PHQ-9）以評估患者是否患有憂鬱症。患者資料源自於國立成功大學醫院的臨床數據庫，並收集其接受首次化療前以及之後的2、3、4、6、9及12個月之資料。使用廣義估計方程式（Generalized estimating equation, GEE）來分析包括人口統計和臨床變量之參數，並探討C反應蛋白（C-reactive protein, CRP）、嗜中性球-淋巴球比值（neutrophil-lymphocyte ratio, NLR）、血小板-淋巴球比值（platelet-lymphocyte ratio, PLR）和白蛋白（albumin）等發炎指標與憂鬱症（PHQ-9≥10）的相關性。為了構建胰臟癌患者憂鬱症的預測模型，採用了六種不同的機器學習演算法——邏輯回歸（Logistic Regression）、隨機森林（Random Forest）、支援向量機（Support Vector Machine）、K-近鄰演算法（K-Nearest Neighbors）、極限梯度提升（Extreme Gradient Boosting, XGBoost）和多層感知器（Multi-Layer Perceptron）——並在Quanta Omni Cloud Care for AI Medical Platform（QOCA Aim，2.0版本）平台上進行評估。模型的分類效能主要藉由接收者操作特徵曲線（receiver operating characteristic curve, AUC）下面積以及準確率（accuracy）、精確度（precision）、召回率（recall）和F1分數（F1-score）來評估；其後，使用排列重要性分析（permutation importance）來評估這些模型中預測因子的相對重要性。
研究結果：患者的平均年齡為65歲（標準差為10.83）；55.18%為男性，54.9%罹患第四期癌症。在患者中，35%的患者在基線時患有憂鬱症（PHQ-9 ≥ 10），在後續追蹤評估中憂鬱率有所下降。單變量廣義估計方程式結果顯示，未接受手術、有轉移性疾病、白蛋白、CRP、NLR及PLR與胰臟癌患者憂鬱症顯著相關（P < 0.05）。然而，當這些變項被納入多變量廣義估計方程式時，只有CRP（OR = 1.32，P = 0.001）和NLR（OR = 1.55，P = 0.001）仍與憂鬱症顯著相關。在六種機器學習演算法中，XGBoost分類器表現最佳，達到最高的AUC為0.77，準確率為0.81，精確度為0.84，召回率為0.91，F1分數為0.86。其後的排列重要性分析表明，CRP、NLR和PLR是多個機器學習模型中胰臟癌患者憂鬱症的最重要預測因子。
結論：本研究揭示了胰臟癌患者發炎與憂鬱症之間的複雜關係，表明容易獲得且可量化的發炎指標可以用來識別有憂鬱風險的患者。研究還突顯了機器學習演算法的有效性，不僅提供了一種更高效、有效的方法，還強調了機器學習在醫學科學中的變革潛力。鑒於減輕該患者群體憂鬱症總體負擔的需求尚未得到滿足，這些有希望的研究成果值得進一步研究探討。

Background: Depression is highly prevalent in patients with pancreatic cancer. While previous studies have revealed a link between depression and inflammation in multiple contexts, this relationship remains unexplored in pancreatic cancer. Accordingly, this study aimed to investigate the relationship between inflammatory markers and depression in patients with pancreatic cancer. Moreover, this study utilized diverse machine learning (ML) techniques in conjunction with inflammatory markers to construct prediction models for depression in this patient cohort.
Methods: From May 2021 to November 2023, 328 patients with pancreatic cancer were prospectively enrolled in the study. The presence of depression among the patients was assessed using the Patient Health Questionnaire (PHQ-9). Patients' data were sourced from the Clinical Data Warehouse of National Cheng Kung University Hospital and were collected before, as well as 2, 3, 4, 6, 9, and 12 months after receiving their first chemotherapy. A generalized estimating equation (GEE) was used to estimate the parameters, including demographic and clinical variables, and to determine whether inflammatory markers—such as C-reactive protein (CRP), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and albumin—were associated with depression (PHQ-9 ≥ 10). To construct prediction models for depression in pancreatic cancer patients, six different ML algorithms—Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron—were employed and evaluated using the QOCA Aim (Quanta Omni Cloud Care for AI Medical Platform, version 2.0) platform. The models' classification effectiveness was primarily gauged using the area under the receiver operating characteristic curve (AUC), along with accuracy, precision, recall, and F1-score; subsequently, permutation importance was used to assess the relative predictor importance within these models.
Results: The average age of patients was 65 years (with a standard deviation of 10.83); 55.18% were males, and 54.9% presented with stage IV cancer. In our cohort, 35% of patients had depression (PHQ-9 ≥ 10) at baseline, with the depression rates falling at follow-up assessments. The univariate generalized estimating equation results showed that not receiving surgical resection, having metastatic diseases, and levels of albumin, CRP, NLR, and PLR were significantly associated with depression in pancreatic cancer patients (P < 0.05). However, when these variables were included in the multivariate generalized estimating equations, only CRP (OR = 1.32, P = 0.001) and NLR (OR = 1.55, P = 0.001) remained significantly associated with depression. Among six ML algorithms, the XGBoost classifier emerged as the top performer, achieving the highest AUC of 0.77, with accuracy of 0.81, precision of 0.84, recall of 0.91, and F1-score of 0.86. Subsequent permutation importance analysis elucidated that CRP, NLR, and PLR were the most significant predictors of depression in pancreatic cancer patients across multiple ML models.
Conclusion: This study sheds light on the intricate relationship between inflammation and depression among pancreatic cancer patients, demonstrating that readily accessible and quantifiable inflammatory markers could serve to identify those at risk for depression. It also highlights the efficacy of ML algorithms, which not only provide a more efficient and effective approach but also underscore the transformative potential of ML in medical science. Given the substantial unmet need to alleviate the overall burden of depression in this patient cohort, these promising findings warrant further study.

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