Search

Contains fulltext : 292740.pdf (Publisher’s version ) (Open Access), (1) Background: Histopathological assessment of Wilms tumors (WT) is crucial for risk group classification to guide postoperative stratification in chemotherapy pre-treated WT cases. However, due to the heterogeneous nature of the tumor, significant interobserver variation between pathologists in WT diagnosis has been observed, potentially leading to misclassification and suboptimal treatment. We investigated whether artificial intelligence (AI) can contribute to accurate and reproducible histopathological assessment of WT through recognition of individual histopathological tumor components. (2) Methods: We assessed the performance of a deep learning-based AI system in quantifying WT components in hematoxylin and eosin-stained slides by calculating the Sørensen-Dice coefficient for fifteen predefined renal tissue components, including six tumor-related components. We trained the AI system using multiclass annotations from 72 whole-slide images of patients diagnosed with WT. (3) Results: The overall Dice coefficient for all fifteen tissue components was 0.85 and for the six tumor-related components was 0.79. Tumor segmentation worked best to reliably identify necrosis (Dice coefficient 0.98) and blastema (Dice coefficient 0.82). (4) Conclusions: Accurate histopathological classification of WT may be feasible using a digital pathology-based AI system in a national cohort of WT patients.

Contains fulltext : 245928.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 252269.pdf (Publisher’s version ) (Open Access)

Item does not contain fulltext, Artificial Intelligence (AI) has become of increasing interest over the past decade. While digital image analysis (DIA) is already being used in radiology, it is still in its infancy in pathology. One of the reasons is that large-scale digitization of glass slides has only recently become available. With the advent of digital slide scanners, that digitize glass slides into whole slide images, many labs are now in a transition phase towards digital pathology. However, only few departments worldwide are currently fully digital. Digital pathology provides the ability to annotate large datasets and train computers to develop and validate robust algorithms, similar to radiology. In this opinionated overview, we will give a brief introduction into AI in pathology, discuss the potential positive and negative implications and speculate about the future role of AI in the field of pediatric pathology.

Item does not contain fulltext, PURPOSE: Breast terminal duct lobular units (TDLUs) are the main source of breast cancer (BC) precursors. Higher serum concentrations of hormones and growth factors have been linked to increased TDLU numbers and to elevated BC risk, with variable effects by menopausal status. We assessed associations of circulating factors with breast histology among premenopausal women using artificial intelligence (AI) and preliminarily tested whether parity modifies associations. METHODS: Pathology AI analysis was performed on 316 digital images of H&E-stained sections of normal breast tissues from Komen Tissue Bank donors ages ≤ 45 years to assess 11 quantitative metrics. Associations of circulating factors with AI metrics were assessed using regression analyses, with inclusion of interaction terms to assess effect modification. RESULTS: Higher prolactin levels were related to larger TDLU area (p < 0.001) and increased presence of adipose tissue proximate to TDLUs (p < 0.001), with less significant positive associations for acini counts (p = 0.012), dilated acini (p = 0.043), capillary area (p = 0.014), epithelial area (p = 0.007), and mononuclear cell counts (p = 0.017). Testosterone levels were associated with increased TDLU counts (p < 0.001), irrespective of parity, but associations differed by adipose tissue content. AI data for TDLU counts generally agreed with prior visual assessments. CONCLUSION: Among premenopausal women, serum hormone levels linked to BC risk were also associated with quantitative features of normal breast tissue. These relationships were suggestively modified by parity status and tissue composition. We conclude that the microanatomic features of normal breast tissue may represent a marker of BC risk.

Contains fulltext : 245928.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 252269.pdf (Publisher’s version ) (Open Access)

Item does not contain fulltext, Artificial Intelligence (AI) has become of increasing interest over the past decade. While digital image analysis (DIA) is already being used in radiology, it is still in its infancy in pathology. One of the reasons is that large-scale digitization of glass slides has only recently become available. With the advent of digital slide scanners, that digitize glass slides into whole slide images, many labs are now in a transition phase towards digital pathology. However, only few departments worldwide are currently fully digital. Digital pathology provides the ability to annotate large datasets and train computers to develop and validate robust algorithms, similar to radiology. In this opinionated overview, we will give a brief introduction into AI in pathology, discuss the potential positive and negative implications and speculate about the future role of AI in the field of pediatric pathology.

Contains fulltext : 252105.pdf (Publisher’s version ) (Closed access), PURPOSE: Breast terminal duct lobular units (TDLUs) are the main source of breast cancer (BC) precursors. Higher serum concentrations of hormones and growth factors have been linked to increased TDLU numbers and to elevated BC risk, with variable effects by menopausal status. We assessed associations of circulating factors with breast histology among premenopausal women using artificial intelligence (AI) and preliminarily tested whether parity modifies associations. METHODS: Pathology AI analysis was performed on 316 digital images of H&E-stained sections of normal breast tissues from Komen Tissue Bank donors ages ≤ 45 years to assess 11 quantitative metrics. Associations of circulating factors with AI metrics were assessed using regression analyses, with inclusion of interaction terms to assess effect modification. RESULTS: Higher prolactin levels were related to larger TDLU area (p < 0.001) and increased presence of adipose tissue proximate to TDLUs (p < 0.001), with less significant positive associations for acini counts (p = 0.012), dilated acini (p = 0.043), capillary area (p = 0.014), epithelial area (p = 0.007), and mononuclear cell counts (p = 0.017). Testosterone levels were associated with increased TDLU counts (p < 0.001), irrespective of parity, but associations differed by adipose tissue content. AI data for TDLU counts generally agreed with prior visual assessments. CONCLUSION: Among premenopausal women, serum hormone levels linked to BC risk were also associated with quantitative features of normal breast tissue. These relationships were suggestively modified by parity status and tissue composition. We conclude that the microanatomic features of normal breast tissue may represent a marker of BC risk.

Contains fulltext : 231951.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 233603.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 231951.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 233603.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 233603.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 231951.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 217309.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 225966.pdf (publisher's version ) (Open Access), Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems (scanners) and cycle-GAN-based normalization on algorithm performance, by comparing different deep learning models to automatically detect prostate cancer in whole-slide images. Specifically, we compare U-Net, DenseNet and EfficientNet. Models were developed on a multi-center cohort with 582 WSIs and subsequently evaluated on two independent test sets including 85 and 50 WSIs, respectively, to show the robustness of the proposed method to differing staining protocols and scanner types. We also investigated the application of normalization as a pre-processing step by two techniques, the whole-slide image color standardizer (WSICS) algorithm, and a cycle-GAN based method. For the two independent datasets we obtained an AUC of 0.92 and 0.83 respectively. After rescanning the AUC improves to 0.91/0.88 and after style normalization to 0.98/0.97. In the future our algorithm could be used to automatically pre-screen prostate biopsies to alleviate the workload of pathologists.

Contains fulltext : 217309.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 225966.pdf (publisher's version ) (Open Access), Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems (scanners) and cycle-GAN-based normalization on algorithm performance, by comparing different deep learning models to automatically detect prostate cancer in whole-slide images. Specifically, we compare U-Net, DenseNet and EfficientNet. Models were developed on a multi-center cohort with 582 WSIs and subsequently evaluated on two independent test sets including 85 and 50 WSIs, respectively, to show the robustness of the proposed method to differing staining protocols and scanner types. We also investigated the application of normalization as a pre-processing step by two techniques, the whole-slide image color standardizer (WSICS) algorithm, and a cycle-GAN based method. For the two independent datasets we obtained an AUC of 0.92 and 0.83 respectively. After rescanning the AUC improves to 0.91/0.88 and after style normalization to 0.98/0.97. In the future our algorithm could be used to automatically pre-screen prostate biopsies to alleviate the workload of pathologists.

Contains fulltext : 225966.pdf (publisher's version ) (Open Access), Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems (scanners) and cycle-GAN-based normalization on algorithm performance, by comparing different deep learning models to automatically detect prostate cancer in whole-slide images. Specifically, we compare U-Net, DenseNet and EfficientNet. Models were developed on a multi-center cohort with 582 WSIs and subsequently evaluated on two independent test sets including 85 and 50 WSIs, respectively, to show the robustness of the proposed method to differing staining protocols and scanner types. We also investigated the application of normalization as a pre-processing step by two techniques, the whole-slide image color standardizer (WSICS) algorithm, and a cycle-GAN based method. For the two independent datasets we obtained an AUC of 0.92 and 0.83 respectively. After rescanning the AUC improves to 0.91/0.88 and after style normalization to 0.98/0.97. In the future our algorithm could be used to automatically pre-screen prostate biopsies to alleviate the workload of pathologists.

Contains fulltext : 217309.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 209655.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 204286.pdf (publisher's version ) (Closed access), Hypomagnesemia (blood Mg(2+) concentration <0.7 mM) is a common electrolyte disorder in patients with type 2 diabetes (T2D), but the etiology remains largely unknown. In patients with T2D, reduced blood Mg(2+) levels are associated with an increased decline in renal function, independent of glycemic control and hypertension. To study the underlying mechanism of this phenomenon, we investigated the renal effects of hypomagnesemia in high-fat-diet (HFD)-fed mice. In mice fed a low dietary Mg(2+), the HFD resulted in severe hypomagnesemia within 4 wk. Renal or intestinal Mg(2+) wasting was not observed after 16 wk on the diets. Despite the absence of urinary or fecal Mg(2+) loss, the HFD induced a reduction in the mRNA expression transient receptor potential melastatin type 6 in both the kidney and colon. mRNA expression of distal convoluted tubule (DCT)-specific genes was down-regulated by the LowMg-HFD, indicating atrophy of the DCT. The low dietary Mg(2+) resulted in severe HFD-induced proximal tubule phospholipidosis, which was absent in mice on a NormalMg-HFD. This was accompanied by albuminuria, moderate renal damage, and alterations in renal energy metabolism, including enhanced gluconeogenesis and cholesterol synthesis. In conclusion, this study shows that hypomagnesemia is a consequence of diet-induced obesity and insulin resistance. Moreover, hypomagnesemia induces major structural changes in the diabetic kidney, including proximal tubular phospholipidosis, providing a novel mechanism for the increased renal decline in patients with hypomagnesemic T2D.-Kurstjens, S., Smeets, B., Overmars-Bos, C., Dijkman, H. B., den Braanker, D. J. W., de Bel, T., Bindels, R. J. M., Tack, C. J. J., Hoenderop, J. G. J., de Baaij, J. H. F. Renal phospholipidosis and impaired magnesium handling in high-fat-diet-fed mice.

Contains fulltext : 209655.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 204286.pdf (publisher's version ) (Closed access), Hypomagnesemia (blood Mg(2+) concentration <0.7 mM) is a common electrolyte disorder in patients with type 2 diabetes (T2D), but the etiology remains largely unknown. In patients with T2D, reduced blood Mg(2+) levels are associated with an increased decline in renal function, independent of glycemic control and hypertension. To study the underlying mechanism of this phenomenon, we investigated the renal effects of hypomagnesemia in high-fat-diet (HFD)-fed mice. In mice fed a low dietary Mg(2+), the HFD resulted in severe hypomagnesemia within 4 wk. Renal or intestinal Mg(2+) wasting was not observed after 16 wk on the diets. Despite the absence of urinary or fecal Mg(2+) loss, the HFD induced a reduction in the mRNA expression transient receptor potential melastatin type 6 in both the kidney and colon. mRNA expression of distal convoluted tubule (DCT)-specific genes was down-regulated by the LowMg-HFD, indicating atrophy of the DCT. The low dietary Mg(2+) resulted in severe HFD-induced proximal tubule phospholipidosis, which was absent in mice on a NormalMg-HFD. This was accompanied by albuminuria, moderate renal damage, and alterations in renal energy metabolism, including enhanced gluconeogenesis and cholesterol synthesis. In conclusion, this study shows that hypomagnesemia is a consequence of diet-induced obesity and insulin resistance. Moreover, hypomagnesemia induces major structural changes in the diabetic kidney, including proximal tubular phospholipidosis, providing a novel mechanism for the increased renal decline in patients with hypomagnesemic T2D.-Kurstjens, S., Smeets, B., Overmars-Bos, C., Dijkman, H. B., den Braanker, D. J. W., de Bel, T., Bindels, R. J. M., Tack, C. J. J., Hoenderop, J. G. J., de Baaij, J. H. F. Renal phospholipidosis and impaired magnesium handling in high-fat-diet-fed mice.

Contains fulltext : 209655.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 179531.pdf (Publisher’s version ) (Open Access), Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems.

Contains fulltext : 179531.pdf (Publisher’s version ) (Open Access), Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems.