AIMC Topic: Lymphatic Metastasis

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Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.

Cancer investigation
We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified ...

Robotic-assisted pelvic lymph node dissection for prostate cancer: frequency of nodal metastases and oncological outcomes.

World journal of urology
PURPOSE: Limited data are available regarding the oncologic efficacy of pelvic lymph node dissection (PLND) performed during robotic-assisted laparoscopic prostatectomy (RALP) for prostate cancer. We aimed to determine the frequency of pelvic lymph n...

Robotic-assisted lateral lymph node dissection for lower rectal cancer: short-term outcomes in 50 consecutive patients.

Surgical endoscopy
BACKGROUND: The goal of this study was to evaluate the short-term outcomes of robotic-assisted lateral lymph node dissection for patients with advanced lower rectal cancer.

Predicting high lymph node positivity risk factors in nasopharyngeal carcinoma patients: A multi-model approach.

Medicine
Identifying patients at high risk of an elevated lymph node ratio (LNR) is critical for optimizing the management of nasopharyngeal carcinoma (NPC), as LNR, defined as the ratio of metastatic to examined lymph nodes, serves as a key prognostic indica...

Non-invasive multi-phase CT artificial intelligence for predicting pre-treatment enlarged lymph node status in colorectal cancer: a prospective validation study.

European radiology
OBJECTIVES: Benign lymph node enlargement can mislead surgeons into overstaging colorectal cancer (CRC), causing unnecessarily extended lymphadenectomy. This study aimed to develop and validate a machine learning (ML) classifier utilizing multi-phase...

Development of a Machine Learning Model Integrating Pathomics and Clinical Data to Predict Axillary Lymph Node Metastasis in Breast Cancer: A Two-Center Study.

Cancer reports (Hoboken, N.J.)
BACKGROUND: Accurately assessing the status of axillary lymph nodes (ALNs) is essential for devising optimal surgical plans and making informed treatment decisions in breast cancer (BC) patients.