In recently published clinical trial results, hypoxia-modified therapies have shown to provide more positive outcomes to cancer patients, compared with standard cancer treatments. The development and validation of these hypoxia-modified therapies depend on an effective way of measuring tumor hypoxia, but a standardized measurement is currently unavailable in clinical practice. Different types of manual measurements have been proposed in clinical research, but in this paper we focus on a recently published approach that quantifies the number and proportion of hypoxic regions using high resolution (immuno-)fluorescence (IF) and hematoxylin and eosin (HE) stained images of a histological specimen of a tumor. We introduce new machine learning-based methodologies to automate this measurement, where the main challenge is the fact that the clinical annotations available for training the proposed methodologies consist of the total number of normoxic, chronically hypoxic, and acutely hypoxic regions without any indication of their location in the image. Therefore, this represents a weakly-supervised structured output classification problem, where training is based on a high-order loss function formed by the norm of the difference between the manual and estimated annotations mentioned above. We propose four methodologies to solve this problem: 1) a naive method that uses a majority classifier applied on the nodes of a fixed grid placed over the input images; 2) a baseline method based on a structured output learning formulation that relies on a fixed grid placed over the input images; 3) an extension to this baseline based on a latent structured output learning formulation that uses a graph that is flexible in terms of the amount and positions of nodes; and 4) a pixel-wise labeling based on a fully-convolutional neural network. Using a data set of 89 weakly annotated pairs of IF and HE images from eight tumors, we show that the quantitative results of methods (3) and (4) above are equally competitive and superior to the naive (1) and baseline (2) methods. All proposed methodologies show high correlation values with respect to the clinical annotations.

We introduce two new structured output models that use a latent graph, which is flexible in terms of the number of nodes and structure, where the training process minimises a high-order loss function using a weakly annotated training set. These models are developed in the context of microscopy imaging of malignant tumours, where the estimation of the number and proportion of classes of microcirculatory supply units (MCSU) is important in the assessment of the efficacy of common cancer treatments (an MCSU is a region of the tumour tissue supplied by a microvessel). The proposed methodologies take as input multimodal microscopy images of a tumour, and estimate the number and proportion of MCSU classes. This estimation is facilitated by the use of an underlying latent graph (not present in the manual annotations), where each MCSU is represented by a node in this graph, labelled with the MCSU class and image location. The training process uses the manual weak annotations available, consisting of the number of MCSU classes per training image, where the training objective is the minimisation of a high-order loss function based on the norm of the error between the manual and estimated annotations. One of the models proposed is based on a new flexible latent structure support vector machine (FLSSVM) and the other is based on a deep convolutional neural network (DCNN) model. Using a dataset of 89 weakly annotated pairs of multimodal images from eight tumours, we show that the quantitative results from DCNN are superior, but the qualitative results from FLSSVM are better and both display high correlation values regarding the number and proportion of MCSU classes compared to the manual annotations.

The efficacy of cancer treatments (e.g., radiotherapy, chemotherapy, etc.) has been observed to critically depend on the proportion of hypoxic regions (i.e., a region deprived of adequate oxygen supply) in tumor tissue, so it is important to estimate this proportion from histological samples. Medical imaging data can be used to classify tumor tissue regions into necrotic or vital and then the vital tissue into normoxia (i.e., a region receiving a normal level of oxygen), chronic or acute hypoxia. Currently, this classification is a lengthy manual process performed using (immuno-)fluorescence (IF) and hematoxylin and eosin (HE) stained images of a histological specimen, which requires an expertise that is not widespread in clinical practice. In this paper, we propose a fully automated way to detect and classify tumor tissue regions into necrosis, normoxia, chronic hypoxia and acute hypoxia using IF and HE images from the same histological specimen. Instead of relying on any single classification methodology, we propose a principled combination of the following current state-of-the-art classifiers in the field: Adaboost, support vector machine, random forest and convolutional neural networks. Results show that on average we can successfully detect and classify more than 87% of the tumor tissue regions correctly. This automated system for estimating the proportion of chronic and acute hypoxia could provide clinicians with valuable information on assessing the efficacy of cancer treatments.