AIMC Topic: Neoplasm Staging

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Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

Academic radiology
RATIONALE AND OBJECTIVES: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop...

Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature.

European radiology
OBJECTIVES: To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) METHODS: A total of 118 patients with pathologically proven LHSCC were enrolled in t...

Evaluation of mediastinal lymph node segmentation of heterogeneous CT data with full and weak supervision.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and assessing response to therapy. Current standard practice for quantifying lymph node size is based on a variety of criteria that use uni-d...

Oncologic outcomes of robot-assisted laparoscopy versus conventional laparoscopy for the treatment of apparent early-stage endometrioid adenocarcinoma of the uterus.

Gynecologic oncology
OBJECTIVE: To compare long-term oncologic outcomes in patients with clinically uterine-confined endometrioid endometrial cancer who underwent surgical staging with robot-assisted (RA) versus conventional laparoscopy.

PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer.

Nature communications
Occult nodal metastasis (ONM) plays a significant role in comprehensive treatments of non-small cell lung cancer (NSCLC). This study aims to develop a deep learning signature based on positron emission tomography/computed tomography to predict ONM of...

Ultra-High-Resolution T2-Weighted PROPELLER MRI of the Rectum With Deep Learning Reconstruction: Assessment of Image Quality and Diagnostic Performance.

Investigative radiology
OBJECTIVE: The aim of this study was to evaluate the impact of ultra-high-resolution acquisition and deep learning reconstruction (DLR) on the image quality and diagnostic performance of T2-weighted periodically rotated overlapping parallel lines wit...

Robotic retroperitoneal lymph node dissection for paratesticular rhabdomyosarcoma in adolescents: a case series.

Journal of robotic surgery
Robotic assisted (RA) retroperitoneal lymph node dissection (RPLND) has grown in popularity as it offers decreased morbidity and faster recovery compared to the open technique. Proponents of open surgery raised concerns about the oncological fidelity...

CT-Based Deep-Learning Model for Spread-Through-Air-Spaces Prediction in Ground Glass-Predominant Lung Adenocarcinoma.

Annals of surgical oncology
BACKGROUND: Sublobar resection is strongly associated with poor prognosis in early-stage lung adenocarcinoma, with the presence of tumor spread through air spaces (STAS). Thus, preoperative prediction of STAS is important for surgical planning. This ...

Robot-assisted retroperitoneal lymph node dissection: Initial experience in Japan.

Asian journal of endoscopic surgery
For patients with testicular tumors who need the surgical management, open retroperitoneal lymph node dissection (O-RPLND) is considered the gold standard treatment. However, recently, robot-assisted RPLND (R-RPLND) has gained popularity as a minimal...

A contemporary analysis of disease upstaging of Gleason 3 + 3 prostate cancer patients after robot-assisted laparoscopic prostatectomy.

Cancer medicine
BACKGROUND: Risk of biochemical recurrence (BCR) in localised prostate cancer can be stratified using the 5-tier Cambridge Prognostic Group (CPG) or 3-tier European Association of Urology (EAU) model. Active surveillance is the current recommendation...