AIMC Topic: Biopsy

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Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers.

EBioMedicine
BACKGROUND: For patients with early-stage breast cancers, neoadjuvant treatment is recommended for non-luminal tumors instead of luminal tumors. Preoperative distinguish between luminal and non-luminal cancers at early stages will facilitate treatmen...

A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies.

Medical physics
BACKGROUND: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breast cancer (BC) patients. However, pCR rates to NAC are less than 30%, depending on the BC subtype. Ea...

AutoFibroNet: A deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD.

Alimentary pharmacology & therapeutics
BACKGROUND: Liver fibrosis is the strongest histological risk factor for liver-related complications and mortality in metabolic dysfunction-associated fatty liver disease (MAFLD). Second harmonic generation/two-photon excitation fluorescence (SHG/TPE...

Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.

Investigative radiology
OBJECTIVES: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be perfor...

Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review.

Medicina (Kaunas, Lithuania)
The development of liver fibrosis as a consequence of continuous inflammation represents a turning point in the evolution of chronic liver diseases. The recent developments of artificial intelligence (AI) applications show a high potential for impro...

Current state of radiomics in pediatric neuro-oncology practice: a systematic review.

Pediatric radiology
BACKGROUND: Radiomics is the process of converting radiological images into high-dimensional data that may be used to create machine learning models capable of predicting clinical outcomes, such as disease progression, treatment response and survival...

Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study.

Gut
OBJECTIVE: To develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis.

Deep learning-based multi-model approach on electron microscopy image of renal biopsy classification.

BMC nephrology
BACKGROUND: Electron microscopy is important in the diagnosis of renal disease. For immune-mediated renal disease diagnosis, whether the electron-dense granule is present in the electron microscope image is of vital importance. Deep learning methods ...

Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence.

PloS one
In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either "malignant", "other or benign" or "insufficient". An endometrial biopsy is a key step in diagnosis of endomet...

Enhancing Nodule Biopsy Through Technology Integration.

Innovations (Philadelphia, Pa.)
Technology in navigating to peripheral pulmonary nodules has improved in recent years. The recent integration of a robotic platform using shape-sensing technology and mobile cone-beam computed tomography imaging technology has enhanced confidence in ...