Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential to quantify and mitigate this risk before widespread clinical use. Here, we show that convolutional neural networks (CNNs) are highly susceptible to white- and black-box adversarial attacks in clinically relevant weakly-supervised classification tasks. Adversarially robust training and dual batch normalization (DBN) are possible mitigation strategies but require precise knowledge of the type of attack used in the inference. We demonstrate that vision transformers (ViTs) perform equally well compared to CNNs at baseline, but are orders of magnitude more robust to white- and black-box attacks. At a mechanistic level, we show that this is associated with a more robust latent representation of clinically relevant categories in ViTs compared to CNNs. Our results are in line with previous theoretical studies and provide empirical evidence that ViTs are robust learners in computational pathology. This implies that large-scale rollout of AI models in computational pathology should rely on ViTs rather than CNN-based classifiers to provide inherent protection against perturbation of the input data, especially adversarial attacks.
The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology slides, but it is unclear how well these predictions generalize to external datasets. We performed a systematic study on Deep-Learning-based prediction of genetic alterations from histology, using two large datasets of multiple tumor types. We show that an analysis pipeline that integrates self-supervised feature extraction and attention-based multiple instance learning achieves a robust predictability and generalizability.
The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from tissue morphology, but it is unclear how well these predictions generalize to external datasets. Here, we present a deep learning pipeline based on self-supervised feature extraction which achieves a robust predictability of genetic alterations in two large multicentric datasets of seven tumor types.
The aldosterone-to-renin ratio (ARR) is a widely used screening test for primary aldosteronism (PA). However, there are various confounding factors, including medication, that may influence the levels of renin and/or aldosterone and consequently the ARR. While withdrawal of antihypertensive treatment prior to screening is advisable, this is not always practical or safe. When it is not possible to interrupt treatment, medications with a neutral, or at least a negligible effect on the ARR are required for bridging the diagnostic period. Current guidelines recommend the use of non-dihydropyridine calcium channel blockers, alpha-adrenoceptor blocking drugs, and the vasodilator hydralazine as noninterfering medications, as these drugs allegedly do not influence the results of ARR testing. 1,2 Although several investigators have reported the effect of these medications on average levels of renin and aldosterone in groups of patients, the calculation of the magnitude of effect on the ARR cannot be inferred by simply dividing the mean values of the entire study population. Thus, drawing conclusions about whether or not the individual ARR is modified by such medication is not a sound approach. The objective of the present review is to identify medications that do and do not impact the ARR on the basis of robust evidence. Addressing the effects of these medications could help researchers and clinical practitioners in choosing a safe drug to use in severe hypertensives in whom temporary treatment withdrawal is not an option, while not significantly hampering the interpretations of the ARR.
The aldosterone-to-renin ratio (ARR) is a common screening test for primary aldosteronism in hypertensives. However, there are many factors which could confound the ARR test result and reduce the accuracy of this test. The present review's objective is to identify these factors and to describe to what extent they affect the ARR. Our analysis revealed that sex, age, posture, and sodium-intake influence the ARR, whereas assay techniques do not. Race and body mass index have an uncertain effect on the ARR. We conclude that several factors can affect the ARR. Not taking these factors into account could lead to misinterpretation of the ARR. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Histopathology plays a fundamental role in the diagnosis and subtyping of solid tumors and has become a cornerstone of modern precision oncology. Histopathological evaluation is typically performed manually by expert pathologists due to the complexity of visual data. However, in the last ten years, new artificial intelligence (AI) methods have made it possible to train computers to perform visual tasks with high performance, reaching similar levels as experts in some applications. In cancer histopathology, these AI tools could help automate repetitive tasks, making more efficient use of pathologists’ time. In research studies, AI methods have been shown to have an astounding ability to predict genetic alterations and identify prognostic and predictive biomarkers directly from routine tissue slides. Here, we give an overview of these recent applications of AI in computational pathology, focusing on new tools for cancer research that could be pivotal in identifying clinical biomarkers for better treatment decisions. Expected final online publication date for the Annual Review of Cancer Biology, Volume 7 is April 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
The aldosterone-to-renin ratio (ARR) is widely used as a screening test for primary aldosteronism, but its determinants in patients with essential hypertension are not fully known. The purpose of the present investigation is to identify the impact of age, sex and BMI on renin, aldosterone and the ARR when measured under strict, standardized conditions in hypertensive patients without primary aldosteronism.Methods: We analysed the data of 423 consecutive hypertensive patients with no concomitant cardiac or renal disorders from two different hospitals (Rotterdam and Maastricht) who had been referred for evaluation of their hypertension. Those who were diagnosed with secondary causes of hypertension, including primary aldosteronism, were excluded from analysis. Patients who used oral contraceptives or had hormonal replacement therapy were excluded as well. Plasma aldosterone concentration (PAC), active plasma renin concentration (APRC) and the ARR were measured under standardized conditions. All measurements were taken in the supine position at 10.00 h in the morning, with one subgroup of patients adhering to a sodium-restricted diet (55 mmol/day) for no less than 3 weeks, and the other subgroup maintaining an ad libitum diet. In those who were receiving antihypertensive treatment, all medications were discontinued at least 3 weeks before testing.Results: In neither group did aldosterone correlate with age. Renin, however, was inversely related to age both during low-salt diet (P < 0.001) and during ad lib salt intake (P ¼ 0.05). This resulted in a significant positive correlation between age and the ARR in both groups. Although on both dietary regimens, PAC and APRC were significantly higher in men when compared with women, the ARR was not significantly different between the two sexes. The age-relationships of renin and the ARR were comparable in men and women on both diets, albeit with greater variability in women. There was an upward trend between BMI and the ARR, which reached statistical significance only in men on low-salt diet. In multivariable regression analysis, age remained the only independent determinant of the ARR. Conclusion:In our essential hypertensive population, the ARR increased significantly with age but was not affected by sex or BMI.
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