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Neoplasm Staging

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Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma: a step towards virtual biopsy.

International journal of surgery (London, England)
OBJECTIVES: Upper tract urothelial carcinoma (UTUC) is a rare, aggressive lesion, with early detection a key to its management. This study aimed to utilise computed tomographic urogram data to develop machine learning models for predicting tumour gra...

Prediction of recurrence risk in endometrial cancer with multimodal deep learning.

Nature medicine
Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopa...

Preoperative evaluation of visceral pleural invasion in peripheral lung cancer utilizing deep learning technology.

Surgery today
PURPOSE: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of...

Preoperatively predicting survival outcome for clinical stage IA pure-solid non-small cell lung cancer by radiomics-based machine learning.

The Journal of thoracic and cardiovascular surgery
OBJECTIVE: Clinical stage IA non-small cell lung cancer (NSCLC) showing a pure-solid appearance on computed tomography is associated with a worse prognosis. This study aimed to develop and validate machine-learning models using preoperative clinical ...

Radiomics and deep learning models for CT pre-operative lymph node staging in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis.

European journal of radiology
PURPOSE: To evaluate the diagnostic accuracy of computed tomography (CT)-based radiomic algorithms and deep learning models to preoperatively identify lymph node metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC).

Deep Learning Features and Metabolic Tumor Volume Based on PET/CT to Construct Risk Stratification in Non-small Cell Lung Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To build a risk stratification by incorporating PET/CT-based deep learning features and whole-body metabolic tumor volume (MTV), which was to make predictions about overall survival (OS) and progression-free survival (PFS) f...

A machine learning predictive model for recurrence of resected distal cholangiocarcinoma: Development and validation of predictive model using artificial intelligence.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: Distal Cholangiocarcinoma (dCCA) represents a challenge in hepatobiliary oncology, that requires nuanced post-resection prognostic modeling. Conventional staging criteria may oversimplify dCCA complexities, prompting the exploration of ...

Prostate-specific Membrane Antigen: Interpretation Criteria, Standardized Reporting, and the Use of Machine Learning.

PET clinics
Prostate-specific membrane antigen targeting positron emission tomography (PSMA-PET) is routinely used for the staging and restaging of patients with various stages of prostate cancer. For clear communication with referring physicians and to improve ...

Analysis of Bladder Cancer Staging Prediction Using Deep Residual Neural Network, Radiomics, and RNA-Seq from High-Definition CT Images.

Genetics research
Bladder cancer has recently seen an alarming increase in global diagnoses, ascending as a predominant cause of cancer-related mortalities. Given this pressing scenario, there is a burgeoning need to identify effective biomarkers for both the diagnosi...

Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

Abdominal radiology (New York)
PURPOSE: The purpose of our study is to investigate image quality, efficiency, and diagnostic performance of a deep learning-accelerated single-shot breath-hold (DLSB) against BLADE for T-weighted MR imaging (TWI) for gastric cancer (GC).