Oncology/Hematology

PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning.

BACKGROUND: Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy.

Clinical service evaluation of the feasibility and reproducibility of novel artificial intelligence based-echocardiographic quantification of global longitudinal strain and left ventricular ejection fraction in trastuzumab-treated patients.

INTRODUCTION: Cardiotoxicity is a potential prognostically important complication of certain chemotherapeutic agents that may result in preclinical or overt clinical heart failure. In some cases, chemotherapy must be withheld when left ventricular (LV) systolic function becomes significantly impaired, to protect cardiac function at the expense of a change in the oncological treatment plan, leading...

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation.

BACKGROUND: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has sug...

Biochemical outcome after curative treatment for localized prostate cancer with external beam radiotherapy: a cross-sectional study.

Although many patients who receive definitive radiotherapy (RT) for localised prostate cancer (CaP) experience long-term disease-free survival and bet...

Understanding the need for digital twins' data in patient advocacy and forecasting oncology.

Digital twins are made of a real-world component where data is measured and a virtual component where those measurements are used to parameterize comp...

Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing.

Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the ca...

Development of a hybrid hydrogel for submucosal injection in endoscopic resection of gastrointestinal neoplasm: From laboratory to clinical trial.

Submucosal injection solution is essential for successful endoscopic resection of the early gastrointestinal tumor. We evaluated a new endoscopic hyd...

An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer.

In this study, we present an algorithmic framework integrated within the created software platform tailored for the discovery of novel small-molecule ...

From cancer big data to treatment: Artificial intelligence in cancer research.

In recent years, developing the idea of "cancer big data" has emerged as a result of the significant expansion of various fields such as clinical rese...

A combined encoder-transformer-decoder network for volumetric segmentation of adrenal tumors.

BACKGROUND: The morphology of the adrenal tumor and the clinical statistics of the adrenal tumor area are two crucial diagnostic and differential diag...

A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks.

Breast cancer is one of the most common cancers affecting women worldwide. It includes a group of malignant neoplasms with a variety of biological, cl...

A survey on cancer detection via convolutional neural networks: Current challenges and future directions.

Cancer is a condition in which abnormal cells uncontrollably split and damage the body tissues. Hence, detecting cancer at an early stage is highly es...

Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists.

OBJECTIVES: To develop and externally validate multiphase CT-based deep learning (DL) models for differentiating adrenal metastases from benign lesion...

A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis.

BACKGROUND: The precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial go...

Decentralized Data-Privacy Preserving Deep-Learning Approaches for Enhancing Inter-Database Generalization in Automatic Sleep Staging.

Automatic sleep staging has been an active field of development. Despite multiple efforts, the area remains a focus of research interest. Indeed, whil...

ASO Author Reflections: Deep-Learning Radiomics Nomogram Based on Enhanced CT to Predict the Effect of Neoadjuvant Chemotherapy on Metastatic Lymph Nodes in Locally Advanced Gastric Cancer.

Photon Absorption Remote Sensing Imaging of Breast Needle Core Biopsies Is Diagnostically Equivalent to Gold Standard H&E Histologic Assessment.

Photon absorption remote sensing (PARS) is a new laser-based microscope technique that permits cellular-level resolution of unstained fresh, frozen, a...

CT scan pancreatic cancer segmentation and classification using deep learning and the tunicate swarm algorithm.

Pancreatic cancer (PC) is a very lethal disease with a low survival rate, making timely and accurate diagnoses critical for successful treatment. PC c...

Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

BACKGROUND: We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) im...

Framework for Radiation Oncology Department-wide Evaluation and Implementation of Commercial Artificial Intelligence Autocontouring.

PURPOSE: Artificial intelligence (AI)-based autocontouring in radiation oncology has potential benefits such as standardization and time savings. Howe...