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Sand Red Nail Polish. Importance of a second opinion in breast surgical pathology and therapeutic implications. Pregnancy can cause many changes to occur in your skin. Predictors of life expectancy Age and gender are individual level variables. Conclusion Multilevel survival models are flexible and efficient tools in studying health inequalities of life expectancy or survival time data with a geographic structure of more than 2 levels. Secondary outcome.

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Points within each grade group are adjusted horizontally to avoid overlap. The black dots indicate image analysis classified low-intermediate tumor grade and the red dots indicate image analysis classified high-grade tumors. Image analysis accuracy for predicting molecular characteristics was also high. However, tumor grade is strongly associated with ER status in most patient populations, and we were interested in increasing accuracy among patients with low-to-intermediate grade tumors where genomic testing is most likely to influence patient care.

Thus, we also employed a training strategy that weighted samples to ensure that low and intermediate grade distributions were similar between ER-positive and ER-negative tumors. Using the same weighting strategy, we trained a classifier to predict Basal-like vs. To examine the potential clinical relevance of using this image analysis technique, we determined the sensitivity and specificity of image analysis and the ability to predict whether or not a tumor is classified as having high vs.

In addition to using image analysis to predict tumor grade, we also tested this approach using histologic subtype, another visual feature of the tumor Table 4. Considering age, race, grade, stage, lymph node status, ER status, Ki67 status, and mitotic tumor grade, no significant differences in accuracy of image-based ER assignment were observed.

We gained further insight into the performance of our method by examining the class predictions across cores from the same patient and within each core. Figure 2 shows four cores from a single patient, along with the class predictions over different regions of the image. While three cores are predicted ER negative and Basal-like intrinsic subtype, the fourth is predicted mostly ER negative and non-Basal-like, indicating that some intra-tumoral heterogeneity might be present between cores.

Uncertainty in the prediction is indicated by white. This patient was labeled as high grade, ER negative, Basal-like intrinsic subtype, ductal histologic subtype, and high ROR. Further details on the image analysis techniques are given in the Methods section.

First, we found that the agreement between image analysis and the pathologist-classified grade was only slightly lower than that observed for two study pathologists, and we obtained high agreement and kappa values. Previous literature based on comparing two pathologists shows that image assessment is subject to some disagreement, 18 particularly among the intermediate grade tumors as we observed between the image analysis and pathologist classification in our study.

Other groups have reported inter-rater kappa statistics of 0. Elsewhere in the literature lower kappa values around 0. It is particularly promising that histologic subtype and molecular marker status could be predicted using image analysis. While we did perform grade-weighting within ER classification, there may be other image features of ER-positive tumors that are not readily discernible and are driving the higher accuracy of ER-positive images over ER negative.

The high rate of agreement between pathologist-scored and image analysis based histologic subtype was also compelling kappa 0. We observed high accuracy of image analysis to predict ductal versus lobular histologic subtype. The high accuracy may be due to the arrangement of epithelial and stromal cells characteristic of ductal and lobular tumors whereby lobular tumors are characterized by non-cohesive single file lines of epithelial cells infiltrating the stroma and ductal tumors are characterized by sheets or nests of epithelial cells embedded in the surrounding stroma.

With respect to intrinsic PAM50 subtype based solely upon gene expression values, previous studies have not evaluated image-based analysis for predicting intrinsic subtype or the risk of recurrence using a score-based method, ROR-PT. RNA-based subtyping for Basal-like vs. That is, Allott et al. As with other studies, our work should be viewed in light of some limitations.

Our sample size was limited in our testing set to patients, but this resulted in nearly TMA cores available for use in our image analysis. Using a larger set of samples with data on RNA-based subtype to balance training for each predictor could be useful.

For example, the fact that Luminal B patients had a higher error rate might suggest there are some features of Luminal B breast cancers that are distinct and image-detectable, and a larger sample size would be helpful in identifying these. Deep learning may be utilizing these features, but in our small sample set, we are unable to tune our data to specifically identify those features or to clarify what they are in intuitive language.

Additionally, the use of binary classification systems for training our digital algorithms i.

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Black porn videos of hot ebony ex girlfriends homemade Currently, U. However, future work should extend these approaches to multiclass classification. Image-based risk prediction has potential clinical value. Gene expression data on tumor tissue samples is not uniformly available for all patients and is costly to obtain in both a clinical and epidemiologic setting.

These results could be used to identify patients who would benefit from further genomic testing. Furthermore, even ER testing is not routinely performed in countries with limited laboratory testing resources and predicting ER status by morphologic features may have utility for guiding endocrine therapy in low-resource settings.

Methods for CBCS have been described elsewhere. After giving informed consent, patients were enrolled under an Institutional Review Board protocol that maintains approval at the University of North Carolina. CBCS eligibility criteria included being female, a first diagnosis of invasive breast cancer, aged 20—74 years at diagnosis, and residence in specified counties.

The training and test sets were formed by a random partition of the data. The total number of patients available for the training and test set from CBCS3 was Patients in the final training and test sets had information for tumor grade and histologic subtype, determined via centralized breast pathologist review within CBCS, along with biomarker data for ER status, PAM50 intrinsic breast cancer subtype, and risk of recurrence ROR-PT where noted.

As has been described in detail by Allott et al. The marked areas were selected for coring and 1—4 tumor tissue cores per participant were used in the TMA construction at the Translational Pathology Laboratory at UNC. Cores with insufficient tumor cellularity were eliminated from the analysis. RNA was isolated from 2, 1. Nanostring assays, which use RNA counting as a measure of gene expression, were conducted.

Color and intensity normalization was first applied to standardize the appearance across core images, countering effects due to different stain amounts and protocols, as well as slide fading. Most automated analyses of histology images use features that describe the properties of cells such as statistics of shape and color.

We instead captured tissue properties with a Convolutional Neural Network CNN , which has been shown more successful for classification tasks on histology. Similar to human visual processing, the low level filters detect small structures such as edges and blobs. Intermediate layers capture increasingly complex properties like shape and texture. The top layers of the network are able to represent object parts like faces or bicycle tires.

The convolution filters are learned from data, creating discriminating features at multiple levels of abstraction. There is no need to hand craft features. Although ImageNet contains a vastly different type of image, CNNs trained on this data set have been shown to transfer well to other data sets, 35 , 36 , 37 including those from biomedical applications.

The lower layers only capture smaller-scale features, which do not provide enough discriminating ability, while the upper layers are so specific to ImageNet that they do not generalize well to histology. Intermediate layers are both generalizable and discriminative for other tasks.

In transferring to histology, we must search for the layer that transfers best to our task. Output from each set of convolutional layers, before max pooling, was extracted over each image at full resolution to form a set of features for the image. Output from the fourth set of convolutional layers was chosen because it performed better than the outputs from other layers.

The fourth set of convolutional layers outputs features of dimension These lower CNN layers are convolutional, meaning that they can be run on any image size. In training a model to predict the class or characteristic group of a tumor, such as high or low grade, we utilize patient-level labels.

Further, applying the original CNN fully convolutionally would produce features that are not generalizable to histology. Thus, some modifications to the VGG16 approach are necessary. A new classifier must be trained to operate on the intermediate level features from VGG Simply taking the mean of each feature over the image would limit our insight into which parts of the image contributed to the classification.

The patient-level labels are weak compared to detailed patch- or pixel-level annotations used in most prior work, necessitating a different classification framework called multiple instance learning. In this setting, we were given a set of tumors, each containing one or more image regions.

We were given a label for each tumor: Due to the diverse appearance of tissue in a single image, learning the model with the patient label applied to every image region did not perform well in initial experiments. Heterogeneity of image region labels in each image is instead accounted for while training the model.

In order to account for intra-tumor heterogeneity, a probabilistic model was formed for how likely each image region is to belong to each class, with these probabilities aggregated across all image regions to form a prediction for the tumor as a whole. A linear support vector machine SVM 39 calibrated with isotonic regression 40 was used to predict the probability for each region.

Isotonic regression fits a piecewise-constant non-decreasing function, transforming the distance from the separating hyperplane learned by the SVM to a probability that an image region belongs to each class. This assumes that the SVM can rank image regions accurately and only needs the distances converted to probabilities.

Each image region was labeled with the class of the tumor from which it belongs. For each fold, an SVM was learned on the training set and calibration was learned on the calibration set with isotonic regression, thus forming an ensemble. An ensemble of size five was selected to balance the desirability of a large training set, a reasonably sized validation set, and the simultaneous desirability of limiting the computation time.

Predictions on the test set were made by averaging probabilities from the five models. This ensemble method also helped to soften any noise in the predictions caused by incorrect image region labels due to heterogeneity. Predictions for tumors were made by first forming a quantile function inverse cumulative distribution of the calibrated SVM ensemble predictions for the image regions using 16 equally spaced quantiles from images in the training set.

The quantiles of the training images were used to train another linear SVM to predict the class label for the whole tumor, with sigmoid calibration transforming the SVM output into probabilities. This method allowed predictions to be made for individual image regions, while also aggregating to overall tumor predictions. When training the previously described SVM classifiers, we initially weighted each class, including tumor grade, ER status, and Basal-like vs.

To reduce the leverage of grade in predicting ER status and intrinsic subtype, sample weighting was applied using weights inversely proportional to the number of samples in the group, i. The probabilities computed on the image regions from all cores were aggregated into a quantile function and the second SVM was used to predict the class for the whole tumor.

Cut points were determined for each tumor characteristic based on the achievement of optimal sensitivity, specificity, and accuracy of each core being correctly classified relative to the pathology or biomarker data. To classify tumor grade, image analysis assigned a probability score of being a high-grade vs. A cut point of greater than 0. Independently, traditional pathologist scoring methods were used to classify tumors as a combined grade of low, intermediate, or high.

To classify patients as ER positive based on image analysis, the same principles were used as those described for tumor grade where each core was assigned a probability of ER-positivity. A probability of greater than 0. For Basal-like vs. These results were compared against the PAMbased intrinsic subtype classification methods using gene expression described previously.

Histologic subtype was restricted to ductal and lobular tumors and was based on a cut point of 0. For core-level comparisons, image region probabilities were calculated of being a high-grade tumor, ER positive, Basal-like subtype, lobular subtype, or high ROR-PT.

Accurate classification was defined as identical classification based on histologic image analysis and biomarker data for the same core. All statistical analyses were done in SAS version 9. De-identified data, including selected covariates and histological images, are available upon request. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Dunnwald, L. Hormone receptor status, tumor characteristics, and prognosis: Breast Cancer Res. Parker, J. Supervised risk predictor of breast cancer based on intrinsic subtypes. Sparano, J. Development of the gene assay and its application in clinical practice and clinical trials. Carlson, J. The impact of the Oncotype Dx breast cancer assay in clinical practice: Beck, A.

Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Yuan, Y. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Veta, M. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Image Anal. Khan, A. A global covariance descriptor for nuclear atypia scoring in breast histopathology images.

IEEE J. Basavanhally, A. SPIE , Popovici, V. Joint analysis of histopathology image features and gene expression in breast cancer. BMC Bioinforma. Zhou, Y. Classification of histology sections via multispectral convolutional sparse coding. CVPR , Vu, T. Histopathological image classification using discriminative feature-oriented dictionary learning.

IEEE Trans. Imaging 35 , — Cruz-Roa, A. A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. Lecture Notes in Computer Science, Wang, D. Deep learning for identifying metastatic breast cancer Preprint at http: Short one today. Did 3 jumps today then the mountain closed due to wind.

Packed my stuff up and got going. Plan was to reach Lauterbrunnen perhaps meet an old friend there, though it seemed like he might not make it. Weather also seemed less good according to forecasts so I was hesitant as I set out. There are three ways to pass Gotthard, the 19km tunnel avoid! Guess what I wanted to try?

Starting to head up the pass. But as I started to climb I could see the weather I was about to enter. It did not look good. With the advice from my Hamburg host "if weather is bad, go south" I decided to go south instead. It got warmer and warmer as I descended from the high elevation.

Maybe 2 weeks ago I remember reading about a heat wave in Italy, seems it isn't quite over yet. Didn't expect it to get this pretty. Italian border. Border police shouted at me for taking this picture, ops. I started looking for accommodation thinking I'd treat myself to a hotel but then I remembered it's Saturday and high season.

So I settled for a campground. With this view I'm not bummed at all. First time in Italy so of course I had to find a Pizzeria and try some pizza. Having learnt my lesson I've already booked something for tomorrow online. Now I have to make some sort of phrasebook, Italian speaking part of Switzerland all spoke good English.

I cross one little border now I get blank stares more often than not when I open my mouth. Jan 14, Oddometer: Dead parrot is funnier Great pics and reports! Attached Files: Prillok , Aug 27, This will be a long post. Broke camp early to avoid the heat.

Heading down to lake Como. Huge lake with lots of watersports going on. The line up ahead is because only one direction through the village can be open at any given time, it's to narrow. There were many of these today. Made sure to stop for breakfast before Switzerland. Just across the border, going up.

Then the road was closed all of a sudden. Not sure what was going on but traffic was diverted on a small gravel road, then I saw what had happened. I'd accidentally navigated tot he mudslide I'd heard about a week ago. The mudslide had hit the road and covered a little village in mud. We were being herded through a detour by police so no chance of stopping.

Picture from internet Check: Anyway the road continued up. St Moritz valley, the road snaked along the side of the water. Later on the lake was such an unbelievable turquoise, I tried but completely failed to capture it, you have to use your imagination.

My one big regret is not bringing my big camera. I passed through St Moritz and started the climb up Umbrail pass. There was an incredible amount of motorbikes around, and surprisingly a ton of bicycles as well. Can you see the 7 levels of road in this picture? Most intense switchbacks I've ever driven. Well above the treeline now.

Camera really starting to struggle with these lightning conditions. The top of Umbrail pass. That is Passo dello Stelvio in the background. Even higher but when I looked at the map I realized a needed to go down the valley to the right. But it was ok too.

Very ok. These bicyclists are impressive. Not only do they pedal up but they are really fast on the way down. I had an entire gang of 7 bikes following me down all the way to the valley, 18km 11 miles and I couldn't shake them. One even managed to pass me by doing an insane stunt in oncoming traffic. Crazy I tell you on those skinny little tires and no suspension.

Stopped in Bormio for lunch. Ok one more pass to go before my hotel, no problem This was tough. I don't have many pictures of this pass, I was getting pretty tired by now, but take my word for it, Gavia Pass is epic. War monument of some sort, that was all my Italian allowed me to figure out.

At the top of Gavia pass, looking down the other side. I was getting a bit lightheaded from the exertion and thin air. The Varadero is an older model with carburetor, it was noticeably lower on power too. I didn't expect this at all but looking it up: It is the tenth highest paved road in the Alps.

Road down the otherside was intense, see that corner up ahead? It is not wide enough to fit a bike and a car. I didn't know we had roads like this in civilized Europe, and it went on like this for many kilometers. Coming down this road I met a bunch of people going up on vespas, the really really old model. I could smell the 2 stroke fumes. Brave or stupid? Maybe both?

My hotel was in Vezza d'Oglio. How can Italy have so many pretty villages. It is astonishing. Walking back after sightseeing I smelt something burning. Then I heard the firetruck. Then it got stuck in traffic. Never did figure out if there was a fire or not though. Must be newly renovated. Super clean. Look at my balcony. And my view. That is Corno Baitone in the middle. Sitting in my hotel room I figured out what's up with all the bicyclists and writing on the road.

Turns out some of the mountain passes here are used for bicycle races so avid bicyclists come here to test their mettle and imagine themselves as Lance Armstrong. Today's ride was km miles and it was stunning the entire way. Looking back at the pictures from this morning I can't believe that was today, it feels so far away, only a tiny bit made it into this report.

Top 3 ride of my life I think. Blader54 , Aug 27, So glad for you! That you had a wonderful ride! Your beautiful photos are a plus! Thanks for sharing this "top 3 lifetime ride" with us! Interesting that you found few English-speakers in Northern Italy. I encounter the same in Sicily.

Maybe it is a general phenomenon for all of Italy? Pongo , Aug 27, Feb 22, Oddometer: Victoria, BC Canada. Saving that one for the ride of your lifetime? Prillok , Aug 28, I'm really not planning far ahead here, I thought I was going to be leaving Switzerland for Italy today. I just hade to scramble and plan a route for today, and book somewhere to stay in less than 25 minutes.

Hopefully I can recharge my computer tonight. For some reason Italy uses several kinds of electrical sockets and this part uses one that is not compatible with my computer. Managed with just enough time to post this: I'll just have to come back some other time and do the more northern passes and Stelvio east side that I missed this time.

Aug 30, Oddometer: Brilliant R. Oh and did I mention breakfast was included? Climbing up Tonale Pass m ft. Some sort of war memorial for WWI soldiers. I can't get used to seeing all these ski slopes in summer. Feels like I'm here at the wrong time. Taking a detour over the mountain to avoid the flat and busy Trento valley.

Stopped in Pinzolo for lunch. Pizza by the slide, Two slices for 4. I figured I'd stop for gasoline and gelato. Big mistake. Trying to find my way around the city in the oppressive heat was awful. I had to try several petrol stations before finding one that would accept visa. Might have been the best ice-cream I've ever had though. The route up to the Dolomites was very scenic and started of with many kilometers of grape plantations.

I think they were making grappa here. Passed maybe a dosen of these today. Chased by some light summer rain. Really helped by lowering the temperature which was wonderful. Uh oh. I entered the village I had decided to stay in just before the rain hit.

I could not find the hotel. Up and down the narrow cobblestone streets desperately trying to find shelter before the rain hit. After figuring out that google maps didn't know shit I found it by asking around. Minutes later it was pouring down. Not challenging but very scenic. The mountains over here have very dramatic peaks.

After crossing the Trento valley something interesting happened. This part of Italy mainly speaks German, Italian second and there is a minority language called "Ladin". You can tell by the street signs which language the valley you are in speak. Architecture also changed a bit, feels more like Austrian alps and I saw advertisement for oktoberfest and lederhosen brass orchestras.

Also they've changed electrical outlets again and I can connect my laptop again. Route for the last few days. I'm going to stay here and do some hiking maybe. Plan the route ahead. I've got some route suggestions from my Hamburg host and it seems I have two choices, north where there are more mountain passes like Timmelsjoch and Grossglockner or a route that will take me more south through Slovenia and perhaps some dirt roads.

We'll see.

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This of course begs the question that "is it worth flying business in a long haul flight? In the end, it was plagued by the same lack of accountability for which it criticised the old civil rights leaders.


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