Oncology/Hematology

Domain and Histopathology Adaptations-Based Classification for Malignancy Grading System.

Accurate proliferation rate quantification can be used to devise an appropriate treatment for breast...

Patient-reported outcome measures compared to clinician reported outcomes regarding incontinence and erectile dysfunction in localized prostate carcinoma after robot assisted radical prostatectomy: Impact on management.

PURPOSE/ BACKGROUND: Patient-reported outcome measures (PROMs) are widely used after robot assisted ...

Novel tools for early diagnosis and precision treatment based on artificial intelligence.

Lung cancer has the highest mortality rate among all cancers in the world. Hence, early diagnosis an...

Simultaneous object detection and segmentation for patient-specific markerless lung tumor tracking in simulated radiographs with deep learning.

BACKGROUND: Real-time tumor tracking is one motion management method to address motion-induced uncer...

Perioperative, renal function and oncological outcomes of robot-assisted radical nephroureterectomy for patients with upper tract urothelial carcinoma.

PURPOSE: To report perioperative, renal function and oncological outcomes of robot-assisted radical ...

Dose reduction and toxicity of lenalidomide-dexamethasone in multiple myeloma: A machine-learning prediction model.

PURPOSE: Lenalidomide remains an effective drug for multiple myeloma, but it is often associated wit...

Artificial Intelligence-Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens.

Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. ...

Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study.

PURPOSE: There are undetectable levels of fat in fat-poor angiomyolipoma. Thus, it is often misdiagn...

Robotic combined resection of sigmoid colon, hepatic metastasis (S3), and pelvic metastasis - A video vignette.

dMIL-Transformer: Multiple Instance Learning Via Integrating Morphological and Spatial Information for Lymph Node Metastasis Classification.

Automated classification of lymph node metastasis (LNM) plays an important role in the diagnosis and...

Uncertainty-Aware Multi-Dimensional Mutual Learning for Brain and Brain Tumor Segmentation.

Existing segmentation methods for brain MRI data usually leverage 3D CNNs on 3D volumes or employ 2D...

Natural language processing deep learning models for the differential between high-grade gliomas and metastasis: what if the key is how we report them?

OBJECTIVES: The differential between high-grade glioma (HGG) and metastasis remains challenging in c...

Artificial intelligence and Italian culture: an understanding of how artificial intelligence can transform the radiation therapy landscape.

The aim is to support the perception of artificial intelligence in the radiation therapy landscape.

Robot-assisted PSMA-radioguided Surgery to Assess Surgical Margins and Nodal Metastases in Prostate Cancer Patients: Report on Three Cases Using an Intraoperative PET-CT Specimen Imager.

INTRODUCTION: The aim of this feasibility study was to test the intraoperative use of this brand-new...

Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration.

Multiomics data including imaging radiomics and various types of molecular biomarkers have been incr...

Deep learning-based detection and quantification of brain metastases on black-blood imaging can provide treatment suggestions: a clinical cohort study.

OBJECTIVES: We aimed to evaluate whether deep learning-based detection and quantification of brain m...

A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign.

OBJECTIVES: With the popularization of chest computed tomography (CT) screening, there are more sub-...

Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.

PURPOSE: Deep learning reconstruction (DLR) has been recommended as useful for improving image quali...

Improving radiomics reproducibility using deep learning-based image conversion of CT reconstruction algorithms in hepatocellular carcinoma patients.

OBJECTIVES: CT reconstruction algorithms affect radiomics reproducibility. In this study, we evaluat...

Oral Cancer Prediction Using a Probability Neural Network (PNN).

OBJECTIVE: In India, usually, oral cancer is mostly identified at a progressive stage of malignancy....

Data-driven decision-making for precision diagnosis of digestive diseases.

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented o...