Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy.

BACKGROUND: Acute kidney injury (AKI) is a critical complication of immune checkpoint inhibitor therapy. Since the etiology of AKI in patients undergoing cancer therapy varies, clarifying underlying causes in individual cases is critical for optimal cancer treatment. Although it is essential to individually analyze immune checkpoint inhibitor-treated patients for underlying pathologies for each AKI episode, these analyses have not been realized. Herein, we aimed to individually clarify the underlying causes of AKI in immune checkpoint inhibitor-treated patients using a new clustering approach with Shapley Additive exPlanations (SHAP). METHODS: We developed a gradient-boosting decision tree-based machine learning model continuously predicting AKI within 7 days, using the medical records of 616 immune checkpoint inhibitor-treated patients. The temporal changes in individual predictive reasoning in AKI prediction models represented the key features contributing to each AKI prediction and clustered AKI patients based on the features with high predictive contribution quantified in time series by SHAP. We searched for common clinical backgrounds of AKI patients in each cluster, compared with annotation by three nephrologists. RESULTS: One hundred and twelve patients (18.2%) had at least one AKI episode. They were clustered per the key feature, and their SHAP value patterns, and the nephrologists assessed the clusters' clinical relevance. Receiver operating characteristic analysis revealed that the area under the curve was 0.880. Patients with AKI were categorized into four clusters with significant prognostic differences (p = 0.010). The leading causes of AKI for each cluster, such as hypovolemia, drug-related, and cancer cachexia, were all clinically interpretable, which conventional approaches cannot obtain. CONCLUSION: Our results suggest that the clustering method of individual predictive reasoning in machine learning models can be applied to infer clinically critical factors for developing each episode of AKI among patients with multiple AKI risk factors, such as immune checkpoint inhibitor-treated patients.

Proteinuria frequency and subsequent renal dysfunction in bevacizumab-treated patients: a single center, retrospective, observational study.

BACKGROUND: Proteinuria is a common adverse event observed during treatment with antivascular endothelial growth factor (VEGF) antibodies. Proteinuria is a risk factor for renal dysfunction and cardiovascular complications in patients with chronic kidney disease. However, the association between anti-VEGF antibody-induced proteinuria and renal dysfunction or cardiovascular complications remains unclear. METHODS: This retrospective, observational study included patients with cancer that were treated with bevacizumab (BV) at Kyoto University Hospital (Kyoto, Japan) between January 2006 and March 2018. Adverse event rates were compared between patients who developed qualitative ≥ 2 + proteinuria and those who developed < 1 + proteinuria. Adverse events were defined as renal dysfunction (i.e., ≥ 57% decrease in the eGFR, compared to the rate at the initial treatment) and hospitalization due to BV-associated cardiovascular complications and other adverse events. RESULTS: In total, 734 patients were included in this analysis. Renal dysfunction was more common in patients with ≥ 2 + proteinuria than in those with < 1 + proteinuria (13/199, 6.5% vs. 12/535, 2.3%). Seven of these 13 patients with ≥ 2 + proteinuria had transient reversible renal dysfunction. Only four (2.0%) patients had BV-associated renal dysfunction. Of the 734 patients, six patients, 16 patients, and 13 patients were hospitalized because of the adverse events of cardiovascular complications, thromboembolisms, and cerebrovascular complications, respectively. No relationship was observed between these adverse events and proteinuria. CONCLUSION: BV treatment-induced proteinuria was not associated with renal dysfunction or other adverse events. Continuing BV with caution is a possible treatment option, even after proteinuria develops, in patients with cancer and a limited prognosis.

Evaluation of Kidney Histological Images Using Unsupervised Deep Learning.

INTRODUCTION: Evaluating histopathology via machine learning has gained research and clinical interest, and the performance of supervised learning tasks has been described in various areas of medicine. Unsupervised learning of histological images has the advantage of reproducibility for labeling; however, the relationship between unsupervised evaluation and clinical information remains unclear in nephrology. METHODS: We propose an unsupervised approach combining convolutional neural networks (CNNs) and a visualization algorithm to cluster the histological images and calculate the score for patients. We applied the approach to the entire images or patched images of the glomerulus of kidney biopsy samples stained with hematoxylin and eosin obtained from 68 patients with immunoglobulin A nephropathy. We assessed the relationship between the obtained scores and clinical variables of urinary occult blood, urinary protein, serum creatinine (SCr), systolic blood pressure, and age. RESULTS: The glomeruli of the patients were classified into 12 distinct classes and 10 patches. The output of the fine-tuned CNN, which we defined as the histological scores, had significant relationships with assessed clinical variables. In addition, the clustering and visualization results suggested that the defined clusters captured important findings when evaluating renal histopathology. For the score of the patch-based cluster containing crescentic glomeruli, SCr (coefficient = 0.09, P = 0.019) had a significant relationship. CONCLUSION: The proposed approach could successfully extract features that were related to the clinical variables from the kidney biopsy images along with the visualization for interpretability. The approach could aid in the quantified evaluation of renal histopathology.

Sequential occurrence of microscopic polyangiitis and anti-glomerular basement membrane disease in a patient with small cell lung cancer: a case report.

BACKGROUND: The association between a preceding malignancy and the onset of anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) has been reported in several studies. While the co-existence of ANCA and anti-glomerular basement membrane (GBM) antibodies in an individual patient is not a common occurrence, this double-positive disease currently has no optimal treatment method. Herein, we report a case of a double-positive disease involving the sequential development of acute kidney injury (AKI) and diffuse alveolar hemorrhage (DAH) in a patient with small cell lung cancer (SCLC). CASE PRESENTATION: A 75-year-old Japanese woman was diagnosed with small cell lung cancer (cT3N2M1b cStage IV) and received chemotherapy. After one cycle of chemotherapy, she experienced fever and malaise. Her serum creatinine level rapidly increased, and she tested positive for myeloperoxidase (MPO)-ANCA and anti-GBM antibody. She was diagnosed with AKI due to microscopic polyangiitis (MPA) based on renal biopsy. Corticosteroid therapy was initiated, which improved her renal dysfunction. Eight days after she was discharged from the hospital, she complained of dyspnea and bloody sputum, and her condition rapidly progressed to respiratory failure. Upon chest imaging, ground-glass opacities were seen in her bilateral lower lungs. Laboratory examinations after admission revealed a lower MPO-ANCA titer and an elevated anti-GBM antibody titer compared to her previous admission. We diagnosed her with DAH due to an anti-GBM disease. After corticosteroid pulse therapy, plasma exchange was performed five times; her oxygen saturation and chest radiologic findings improved gradually. Following five cycles of plasma exchange, her oxygen saturation recovered to 95% in room air. CONCLUSIONS: To our knowledge, this is the first reported case of vasculitis caused by MPA and anti-GBM disease leading to the development of AKI and DAH during treatment of SCLC. SCLC, MPA, and anti-GBM disease may occur sequentially. A double-positive disease might have a worse prognosis; therefore, intensive therapy is more likely to achieve a better outcome.