Multi-scale segmentation in GBM treatment using diffusion tensor imaging.

Glioblastoma (GBM) is the commonest primary malignant brain tumor in adults, and despite advances in multi-modality therapy, the outlook for patients has changed little in the last 10 years. Local recurrence is the predominant pattern of treatment failure, hence improved local therapies (surgery and radiotherapy) are needed to improve patient outcomes. Currently segmentation of GBM for surgery or radiotherapy (RT) planning is labor intensive, especially for high-dimensional MR imaging methods that may provide more sensitive indicators of tumor phenotype. Automating processing and segmentation of these images will aid treatment planning. Diffusion tensor magnetic resonance imaging is a recently developed technique (DTI) that is exquisitely sensitive to the ordered diffusion of water in white matter tracts. Our group has shown that decomposition of the tensor information into the isotropic component (p - shown to represent tumor invasion) and the anisotropic component (q - shown to represent the tumor bulk) can provide valuable prognostic information regarding tumor infiltration and patient survival. However, tensor decomposition of DTI data is not commonly used for neurosurgery or radiotherapy treatment planning due to difficulties in segmenting the resultant image maps. For this reason, automated techniques for segmentation of tensor decomposition maps would have significant clinical utility. In this paper, we modified a well-established convolutional neural network architecture (CNN) for medical image segmentation and used it as an automatic multi-sequence GBM segmentation based on both DTI image maps (p and q maps) and conventional MRI sequences (T2-FLAIR and T1 weighted post contrast (T1c)). In this proof-of-concept work, we have used multiple MRI sequences, each with individually defined ground truths for better understanding of the contribution of each image sequence to the segmentation performance. The high accuracy and efficiency of our proposed model demonstrates the potential of utilizing diffusion MR images for target definition in precision radiation treatment planning and surgery in routine clinical practice.CRUK Project grant - PRaM-GBM study (C9216/A19732) NIHR Clinician Scientist Fellowship (project reference NIHR/CS/009/011) and an NIHR Career Development Fellowship (project reference CDF-2018-11-ST2-003) for SJP

A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks

Abstract: Breast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time-consuming, and limited by the quality of the specimen and the experience of the pathologist. This study's objective was to determine if deep convolutional neural networks can be trained, with transfer learning, on a set of histopathological images independent of breast tissue to segment tumor nuclei of the breast. Various deep convolutional neural networks were evaluated for the study, including U-Net, Mask R-CNN, and a novel network (GB U-Net). The networks were trained on a set of Hematoxylin and Eosin (H&E)-stained images of eight diverse types of tissues. GB U-Net demonstrated superior performance in segmenting sites of invasive diseases (AJI = 0.53, mAP = 0.39 & AJI = 0.54, mAP = 0.38), validated on two hold-out datasets exclusively containing breast tissue images of approximately 7,582 annotated cells. The results of the networks, trained on images independent of breast tissue, demonstrated that tumor nuclei of the breast could be accurately segmented

Quantitative thermal imaging biomarkers to detect acute skin toxicity from breast radiation therapy using supervised machine learning

Purpose Radiation-induced dermatitis is a common side effect of breast radiation therapy (RT). Current methods to evaluate breast skin toxicity include clinical examination, visual inspection, and patient-reported symptoms. Physiological changes associated with radiation-induced dermatitis, such as inflammation, may also increase body-surface temperature, which can be detected by thermal imaging. Quantitative thermal imaging markers were identified and used in supervised machine learning to develop a predictive model for radiation dermatitis. Methods and Materials Ninety patients treated for adjuvant whole-breast RT (4250 cGy/fx = 16) were recruited for the study. Thermal images of the treated breast were taken at 4 intervals: before RT, then weekly at fx = 5, fx = 10, and fx = 15. Parametric thermograms were analyzed and yielded 26 thermal-based features that included surface temperature (°C) and texture parameters obtained from (1) gray-level co-occurrence matrix, (2) gray-level run-length matrix, and (3) neighborhood gray-tone difference matrix. Skin toxicity was evaluated at the end of RT using the Common Terminology Criteria for Adverse Events (CTCAE) guidelines (Ver.5). Binary group classes were labeled according to a CTCAE cut-off score of ≥2, and thermal features obtained at fx = 5 were used for supervised machine learning to predict skin toxicity. The data set was partitioned for model training, independent testing, and validation. Fifteen patients (∼17% of the whole data set) were randomly selected as an unseen test data set, and 75 patients (∼83% of the whole data set) were used for training and validation of the model. A random forest classifier with leave-1-patient-out cross-validation was employed for modeling single and hybrid parameters. The model performance was reported using receiver operating characteristic analysis on patients from an independent test set. Results Thirty-seven patients presented with adverse skin effects, denoted by a CTCAE score ≥2, and had significantly higher local increases in skin temperature, reaching 36.06°C at fx = 10 (P = .029). However, machine-learning models demonstrated early thermal signals associated with skin toxicity after the fifth RT fraction. The cross-validated model showed high prediction accuracy on the independent test data (test accuracy = 0.87) at fx = 5 for predicting skin toxicity at the end of RT. Conclusions Early thermal markers after 5 fractions of RT are predictive of radiation-induced skin toxicity in breast RT

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.</jats:p

Machine Learning and Digital Histopathology Analysis for Tissue Characterization and Treatment Response Prediction in Breast Cancer

Breast cancer is the most common type of diagnosed cancer and the leading cause of cancer-related death in women. Early diagnosis and prognosis in breast cancer patients can permit more therapeutic options and possibly improve their survival and quality of life. The gold standard approach for breast cancer diagnosis and characterization is histopathology assessment on biopsy specimens, which is time and resource-demanding. In this dissertation project, state-of-the-art machine learning (ML) methods have been developed and investigated for breast tissue characterization, nuclei segmentation, and chemotherapy response prediction in breast cancer patients using pre-treatment digitized histopathology images. First, a novel multi-scale attention-guided deep learning model is introduced to characterize breast tissue on digital pathology images according to four histological types. Evaluation results on the test set show the effectiveness of the proposed approach in accurate histopathology image classification with an accuracy of 97.5%. In the next step, a cascaded deep-learning-based model is proposed to delineate tumor nuclei in digital pathology images accurately, which is an essential step for extracting hand-crafted quantitative features for analysis with conventional ML models. The proposed model could achieve an F1 score of 0.83 on an independent test set. At the end, two novel ML frameworks are introduced and investigated for chemotherapy response prediction. In the first approach, a digital histopathology image analysis framework has been developed to extract various subsets of quantitative features from the segmented digitized slides for conventional ML model development. Several ML experiments have been conducted with different feature sets to develop prediction models of therapy response using a gradient boosting machine with decision trees. The proposed model with the optimal feature set could achieve an accuracy of 84%, sensitivity of 85% and specificity of 82% on an independent test set. The second approach introduces a hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using digital histopathology images of pre‑treatment tumor biopsies. The whole slide images (WSIs) are processed automatically through the proposed hierarchical framework consisting of patch-level and tumor-level processing modules followed by a patient-level response prediction component. A combination of convolutional and transformer modules is utilized at each processing level. The proposed framework could outperform the conventional ML models with a test accuracy, sensitivity, and specificity of 86%, 87%, and 83%, respectively. The proposed methods and the reported results in this dissertation are steps toward streamlining the histopathology workflow and implementing response-guided precision oncology for breast cancer patients

The differences between normal and obese patient handling: re- structural analysis of two questionnaires

Abstract Background Precise causes of musculoskeletal complaints among nurses are not known well, but many studies have pointed to manual patient handling tasks. Subjective judgment and decision-making process for patient lifting is crucial for gathering data regards patient handling. The aim of this study was to consider reliability and validity and re-structure of two special tools for patient handling’s tasks. Methods In this cross- sectional study 249 nurses were fully participated. As recommended by literature for cultural adaptation of instruments, forward/backward translation method was applied. Reliability of the translated version was assessed by Cronbach’s alpha coefficient. Validity testing for the two scales was based on content validity index/ratio analysis and also Exploratory Factor Analysis was run to extract latent factors. Results Reliability estimated by internal consistency reached a Cronbach’s Alpha of above 0.7 for all subscales of two questionnaires. After testing the validity, the final version of questionnaires was remained by 14 and 15 questions respectively. Conclusions These instruments evaluated for manual handling of normal and obese patients had acceptable validity and reliability in Iranian Nursing context. So, these tools can be used in further studies with the same cultures

Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics

Progress in computing power and advances in medical imaging over recent decades have culminated in new opportunities for artificial intelligence (AI), computer vision, and using radiomics to facilitate clinical decision-making. These opportunities are growing in medical specialties, such as radiology, pathology, and oncology. As medical imaging and pathology are becoming increasingly digitized, it is recently recognized that harnessing data from digital images can yield parameters that reflect the underlying biology and physiology of various malignancies. This greater understanding of the behaviour of cancer can potentially improve on therapeutic strategies. In addition, the use of AI is particularly appealing in oncology to facilitate the detection of malignancies, to predict the likelihood of tumor response to treatments, and to prognosticate the patients' risk of cancer-related mortality. AI will be critical for identifying candidate biomarkers from digital imaging and developing robust and reliable predictive models. These models will be used to personalize oncologic treatment strategies, and identify confounding variables that are related to the complex biology of tumors and diversity of patient-related factors (ie, mining “big data”). This commentary describes the growing body of work focussed on AI for precision oncology. Advances in AI-driven computer vision and machine learning are opening new pathways that can potentially impact patient outcomes through response-guided adaptive treatments and targeted therapies based on radiomic and pathomic analysis