Better prognostic markers for nonmuscle invasive papillary urothelial carcinomas

Bladder cancer is a common type of cancer, especially among men in developed countries. Most cancers in the urinary bladder are papillary urothelial carcinomas. They are characterized by a high recurrence frequency (up to 70 %) after local resection. It is crucial for prognosis to discover these recurrent tumours at an early stage, especially before they become muscle-invasive. Reliable prognostic biomarkers for tumour recurrence and stage progression are lacking. This is why patients diagnosed with a non-muscle invasive bladder cancer follow extensive follow-up regimens with possible serious side effects and with high costs for the healthcare systems. WHO grade and tumour stage are two central biomarkers currently having great impact on both treatment decisions and follow-up regimens. However, there are concerns regarding the reproducibility of WHO grading, and stage classification is challenging in small and fragmented tumour material. In Paper I, we examined the reproducibility and the prognostic value of all the individual microscopic features making up the WHO grading system. Among thirteen extracted features there was considerable variation in both reproducibility and prognostic value. The only feature being both reasonably reproducible and statistically significant prognostic was cell polarity. We concluded that further validation studies are needed on these features, and that future grading systems should be based on well-defined features with true prognostic value. With the implementation of immunotherapy, there is increasing interest in tumour immune response and the tumour microenvironment. In a search for better prognostic biomarkers for tumour recurrence and stage progression, in Paper II, we investigated the prognostic value of tumour infiltrating immune cells (CD4, CD8, CD25 and CD138) and previously investigated cell proliferation markers (Ki-67, PPH3 and MAI). Low Ki 67 and tumour multifocality were associated with increased recurrence risk. Recurrence risk was not affected by the composition of immune cells. For stage progression, the only prognostic immune cell marker was CD25. High values for MAI was also strongly associated with stage progression. However, in a multivariate analysis, the most prognostic feature was a combination of MAI and CD25. BCG-instillations in the bladder are indicated in intermediate and high-risk non-muscle invasive bladder cancer patients. This old-fashion immunotherapy has proved to reduce both recurrence- and progression-risk, although it is frequently followed by unpleasant side-effects. As many as 30-50% of high-risk patients receiving BCG instillations, fail by develop high-grade recurrences. They do not only suffer from unnecessary side-effects, but will also have a delay in further treatment. Together with colleagues at three different Dutch hospitals, in Paper III, we looked at the prognostic and predictive value of T1-substaging. A T1-tumour invades the lamina propria, and we wanted to separate those with micro- from those with extensive invasion. We found that BCG-failure was more common among patients with extensive invasion. Furthermore, T1-substaging was associated with both high-grade recurrence-free and progression-free survival. Finally, in Paper IV, we wanted to investigate the prognostic value of two classical immunohistochemical markers, p53 and CK20, and compare them with previously investigated proliferation markers. p53 is a surrogate marker for mutations in the gene TP53, considered to be a main characteristic for muscle-invasive tumours. CK20 is a surrogate marker for luminal tumours in the molecular classification of bladder cancer, and is frequently used to distinguish reactive urothelial changes from urothelial carcinoma in situ. We found both positivity for p53 and CK20 to be significantly associated with stage progression, although not performing better than WHO grade and stage. The proliferation marker MAI, had the highest prognostic value in our study. Any combination of variables did not perform better in a multivariate analysis than MAI alone

Semi-supervised tissue segmentation of histological images

Supervised learning of convolutional neural networks (CNN) used for image classification and segmentation has produced state-of-the art results, including in many medical image applications. In the medical field, making ground truth labels would typically require an expert opin ion, and a common problem is the lack of labeled data. Consequently, the models might not be general enough. Digitized histological microscopy images of tissue biopsies are very large, and detailed truth markings for tissue-type segmentation are scarce or non-existing. However, in many cases, large amounts of unlabeled data that could be exploited are readily accessible. Methods for semi-supervised learning exists, but are hardly explored in the context of computational pathology. This paper deals with semi-supervised learning on the application of tissue-type classifi cation in histological whole-slide images of urinary bladder cancer. Two semi-supervised approaches utilizing the unlabeled data in combination with a small set of labeled data is presented. A multiscale, tile-based segmentation technique is used to classify tissue into six different classes by the use of three individual CNNs. Each CNN is presented tissue at different magnification levels in order to detect different feature types, later fused in a fully-connected neural network. The two self-training ap proaches are: using probabilities and using a clustering technique. The clustering method performed best and increased the overall accuracy of the tissue tile classification model from 94.6% to 96% compared to using supervised learning with labeled data. In addition, the clustering method generated visually better segmentation images.publishedVersio

Invasive cancerous area detection in non-muscle invasive bladder cancer whole slide images

Bladder cancer patients’ stratification into risk groups relies on grade, stage and clinical factors. For non-muscle invasive bladder cancer, T1 tumours that invade the subepithelial tissue are high-risk lesions with a high probability to progress into an aggressive muscle-invasive disease. Detecting invasive cancerous areas is the main factor for dictating the treatment strategy for the patient. However, defining invasion is often subject to intra/interobserver variability among pathologists, thus leading to over or undertreatment. Computer-aided diagnosis systems can help pathologists reduce overheads and erratic reproducibility. We propose a multi-scale model that detects invasive cancerous areas patterns across the whole slide image. The model extracts tiles of different tissue types at multiple magnification levels and processes them to predict invasive patterns based on local and regional information for accurate T1 staging. Our proposed method yields an F1 score of 71.9, in controlled settings 74.9, and without infiltration 90.0.acceptedVersio

T1 Substaging of Nonmuscle Invasive Bladder Cancer is Associated with bacillus Calmette-Guérin Failure and Improves Patient Stratification at Diagnosis

Purpose: Currently, markers are lacking that can identify patients with high risk nonmuscle invasive bladder cancer who will fail bacillus Calmette-Guérin treatment. Therefore, we evaluated the prognostic value of T1 substaging in patients with primary high risk nonmuscle invasive bladder cancer. Materials and Methods: Patients with primary high risk nonmuscle invasive bladder cancer who received ≥5 bacillus Calmette-Guérin induction instillations were included. All tumors were centrally reviewed, which included T1 substaging (microinvasion vs extensive invasion of the lamina propria). T1 patients were stratified into high risk or highest risk subgroups according to major urology guidelines. Primary end point was bacillus Calmette-Guérin failure, defined as development of a high grade recurrence. Secondary end points were high grade recurrence-free survival, defined as time from primary diagnosis to biopsy-proven high grade recurrence and progression-free survival. Time-to-event analyses were used to predict survival. Results: A total of 264 patients with high risk nonmuscle invasive bladder cancer had tumor invasion of the lamina propria, of which 73% were classified as extensive invasion and 27% as microinvasion. Median followup was 68 months (IQR 43–98) and bacillus Calmette-Guérin failure was more common among patients with extensive vs microinvasive tumors (41% vs 21%, p=0.002). The 3-year high grade recurrence-free survival (defined as bacillus Calmette-Guerin failure) for patients with extensive vs microinvasive tumors was 64% vs 83% (p=0.004). In multivariate analysis, T1 substaging was an independent predictor of high grade recurrence-free survival (HR 3.2, p=0.005) and progression-free survival (HR 3.0, p=0.009). Patients with highest risk/microinvasive disease have an improved progression-free survival as compared to highest risk/T1e disease (p.adj=0.038). Conclusions: T1 substaging provides important prognostic information on patients with primary high risk nonmuscle invasive bladder cancer treated with bacillus Calmette-Guérin. The risk of bacillus Calmette-Guérin failure is higher in extensive vs microinvasive tumors. Substaging of T1 high risk nonmuscle invasive bladder cancer has the potential to guide treatment decisions on bacillus Calmette-Guérin vs alternative strategies at diagnosis.publishedVersio

Essensiell tremor med hovedvekt på patofysiologi

Essential tremor is the most prevalent tremor disorder. Despite of that, very little is known about the responsible mechanisms. This is a review article emphasizing the pathophysiology of essential tremor, but it also includes epidemiology, clinics and treatment. Clinically essential tremor shows great heterogeneity. It is characterized by a 4-12 hz kinetic and/ or postural tremor mainly affecting the upper extremities. Tremor can also be seen in the neck, lower extremities, voice, face and truncus. Essential tremor is a progressive disorder, in worst case leaving the patient unable to eat and dress. Medical treatment, mainly by propranolol and/ or primidon, alleviates the symptoms in 50 %. Alternatively “deep brain stimulation” (DBS) can be done. Many cases show autosomal dominant pattern of inheritance, but some are sporadic. The tremor is probably generated by one or more central oscillators. We know that GABAergic mechanisms are involved. The question is where in the CNS the oscillator is situated. Wherever the origin, the causative signals are sent from thalamus to the cortical motor areas. Thalamus could of course be the oscillator, but both clinical and image studies point to cerebellum. A lesion in the cerebellum may convincingly cure the essential tremor. Oliva inferior, which is another step retrograde, is debated, but totally it seems that the evidence suggesting this is not strong enough. Because of the heterogeneity of the disorder, one must not forget the possibility that essential tremor constitutes a number of disease entities with many different pathophysiological mechanisms

Better prognostic markers for nonmuscle invasive papillary urothelial carcinomas

Bladder cancer is a common type of cancer, especially among men in developed countries. Most cancers in the urinary bladder are papillary urothelial carcinomas. They are characterized by a high recurrence frequency (up to 70 %) after local resection. It is crucial for prognosis to discover these recurrent tumours at an early stage, especially before they become muscle-invasive. Reliable prognostic biomarkers for tumour recurrence and stage progression are lacking. This is why patients diagnosed with a non-muscle invasive bladder cancer follow extensive follow-up regimens with possible serious side effects and with high costs for the healthcare systems. WHO grade and tumour stage are two central biomarkers currently having great impact on both treatment decisions and follow-up regimens. However, there are concerns regarding the reproducibility of WHO grading, and stage classification is challenging in small and fragmented tumour material. In Paper I, we examined the reproducibility and the prognostic value of all the individual microscopic features making up the WHO grading system. Among thirteen extracted features there was considerable variation in both reproducibility and prognostic value. The only feature being both reasonably reproducible and statistically significant prognostic was cell polarity. We concluded that further validation studies are needed on these features, and that future grading systems should be based on well-defined features with true prognostic value. With the implementation of immunotherapy, there is increasing interest in tumour immune response and the tumour microenvironment. In a search for better prognostic biomarkers for tumour recurrence and stage progression, in Paper II, we investigated the prognostic value of tumour infiltrating immune cells (CD4, CD8, CD25 and CD138) and previously investigated cell proliferation markers (Ki-67, PPH3 and MAI). Low Ki 67 and tumour multifocality were associated with increased recurrence risk. Recurrence risk was not affected by the composition of immune cells. For stage progression, the only prognostic immune cell marker was CD25. High values for MAI was also strongly associated with stage progression. However, in a multivariate analysis, the most prognostic feature was a combination of MAI and CD25. BCG-instillations in the bladder are indicated in intermediate and high-risk non-muscle invasive bladder cancer patients. This old-fashion immunotherapy has proved to reduce both recurrence- and progression-risk, although it is frequently followed by unpleasant side-effects. As many as 30-50% of high-risk patients receiving BCG instillations, fail by develop high-grade recurrences. They do not only suffer from unnecessary side-effects, but will also have a delay in further treatment. Together with colleagues at three different Dutch hospitals, in Paper III, we looked at the prognostic and predictive value of T1-substaging. A T1-tumour invades the lamina propria, and we wanted to separate those with micro- from those with extensive invasion. We found that BCG-failure was more common among patients with extensive invasion. Furthermore, T1-substaging was associated with both high-grade recurrence-free and progression-free survival. Finally, in Paper IV, we wanted to investigate the prognostic value of two classical immunohistochemical markers, p53 and CK20, and compare them with previously investigated proliferation markers. p53 is a surrogate marker for mutations in the gene TP53, considered to be a main characteristic for muscle-invasive tumours. CK20 is a surrogate marker for luminal tumours in the molecular classification of bladder cancer, and is frequently used to distinguish reactive urothelial changes from urothelial carcinoma in situ. We found both positivity for p53 and CK20 to be significantly associated with stage progression, although not performing better than WHO grade and stage. The proliferation marker MAI, had the highest prognostic value in our study. Any combination of variables did not perform better in a multivariate analysis than MAI alone

A Multiscale Approach for Whole-Slide Image Segmentation of five Tissue Classes in Urothelial Carcinoma Slides

In pathology labs worldwide, we see an increasing number of tissue samples that need to be assessed without the same increase in the number of pathologists. Computational pathology, where digital scans of histological samples called whole-slide images (WSI) are processed by computational tools, can be of help for the pathologists and is gaining research interests. Most research effort has been given to classify slides as being cancerous or not, localization of cancerous regions, and to the “big-four” in cancer: breast, lung, prostate, and bowel. Urothelial carcinoma, the most common form of bladder cancer, is expensive to follow up due to a high risk of recurrence, and grading systems have a high degree of inter- and intra-observer variability. The tissue samples of urothelial carcinoma contain a mixture of damaged tissue, blood, stroma, muscle, and urothelium, where it is mainly muscle and urothelium that is diagnostically relevant. A coarse segmentation of these tissue types would be useful to i) guide pathologists to the diagnostic relevant areas of the WSI, and ii) use as input in a computer-aided diagnostic (CAD) system. However, little work has been done on segmenting tissue types in WSIs, and on computational pathology for urothelial carcinoma in particular. In this work, we are using convolutional neural networks (CNN) for multiscale tile-wise classification and coarse segmentation, including both context and detail, by using three magnification levels: 25x, 100x, and 400x. 28 models were trained on weakly labeled data from 32 WSIs, where the best model got an F1-score of 96.5% across six classes. The multiscale models were consistently better than the single-scale models, demonstrating the benefit of combining multiple scales. No tissue-class ground-truth for complete WSIs exist, but the best models were used to segment seven unseen WSIs where the results were manually inspected by a pathologist and are considered as very promising.publishedVersio

Automatic diagnostic tool for predicting cancer grade in bladder cancer patients using deep learning

The most common type of bladder cancer is urothelial carcinoma, which is among the cancer types with the highest recurrence rate and lifetime treatment cost per patient. Diagnosed patients are stratified into risk groups, mainly based on grade and stage. However, it is well known that correct grading of bladder cancer suffers from intra- and interobserver variability and inconsistent reproducibility between pathologists, potentially leading to under- or overtreatment of the patients. The economic burden, unnecessary patient suffering, and additional load on the health care system illustrate the importance of developing new tools to aid pathologists. We propose a pipeline, called TRI grade , that will identify diagnostic relevant regions in the whole-slide image (WSI) and collectively predict the grade of the current WSI. The system consists of two main models, trained on weak slide-level grade labels. First, a WSI is segmented into the different tissue classes (urothelium, stroma, muscle, blood, damaged tissue, and background). Next, tiles are extracted from the diagnostic relevant urothelium tissue from three magnification levels (25x, 100x, and 400x) and processed sequentially by a convolutional neural network (CNN) based model. Ten models were trained for the slide-level grading experiment, where the best model achieved an F1-score of 0.90 on a test set consisting of 50 WSIs. The best model was further evaluated on a smaller segmentation test set, consisting of 14 WSIs where low- and high-grade regions were annotated by a pathologist. The TRI grade pipeline achieved an average F1-score of 0.91 for both the low-grade and high-grade classes.publishedVersio

Semi-supervised tissue segmentation of histological images

Supervised learning of convolutional neural networks (CNN) used for image classification and segmentation has produced state-of-the art results, including in many medical image applications. In the medical field, making ground truth labels would typically require an expert opin ion, and a common problem is the lack of labeled data. Consequently, the models might not be general enough. Digitized histological microscopy images of tissue biopsies are very large, and detailed truth markings for tissue-type segmentation are scarce or non-existing. However, in many cases, large amounts of unlabeled data that could be exploited are readily accessible. Methods for semi-supervised learning exists, but are hardly explored in the context of computational pathology. This paper deals with semi-supervised learning on the application of tissue-type classifi cation in histological whole-slide images of urinary bladder cancer. Two semi-supervised approaches utilizing the unlabeled data in combination with a small set of labeled data is presented. A multiscale, tile-based segmentation technique is used to classify tissue into six different classes by the use of three individual CNNs. Each CNN is presented tissue at different magnification levels in order to detect different feature types, later fused in a fully-connected neural network. The two self-training ap proaches are: using probabilities and using a clustering technique. The clustering method performed best and increased the overall accuracy of the tissue tile classification model from 94.6% to 96% compared to using supervised learning with labeled data. In addition, the clustering method generated visually better segmentation images