AIMC Topic: Retrospective Studies

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Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer.

Radiation oncology (London, England)
BACKGROUND: The evaluation of automatic segmentation algorithms is commonly performed using geometric metrics. An analysis based on dosimetric parameters might be more relevant in clinical practice but is often lacking in the literature. The aim of t...

Impact of the severity of urethrovesical anastomotic leakage on urinary continence following robot-assisted laparoscopic prostatectomy.

Journal of robotic surgery
We assessed whether the severity of anastomotic urinary leakage detected during routine cystourethrography after robot-assisted laparoscopic prostatectomy (RALP) affects urinary continence recovery. Around 302 patients who underwent RALP between Augu...

Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours.

European radiology
OBJECTIVE: To evaluate if a deep learning model can be used to characterise breast cancers on contrast-enhanced spectral mammography (CESM).

Assessing pentafecta achievement after robot-assisted radical cystectomy and its association with surgical experience: Results from a high-volume institution.

Urologic oncology
OBJECTIVES: Radical cystectomy (RC) represents the gold standard treatment for high-risk bladder cancer. Despite evidence suggesting that surgical experience correlates with perioperative and oncologic outcomes of robot-assisted RC (RARC), validated ...

Does type of robotic platform make a difference in the final cost of robotic-assisted radical prostatectomy?

Journal of robotic surgery
This study evaluates the difference of robot-assisted radical prostatectomy (RARP) costs in patients with similar preoperative characteristics operated on using the da Vinci SP and Xi robotic platforms. We performed a retrospective analysis on 71 con...

A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: To improve the performance of less experienced clinicians in the diagnosis of benign and malignant spinal fracture on MRI, we applied the ResNet50 algorithm to develop a decision support system.

: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning.

Computational intelligence and neuroscience
Pulmonary fibrosis is a severe chronic lung disease that causes irreversible scarring in the tissues of the lungs, which results in the loss of lung capacity. The Forced Vital Capacity () of the patient is an interesting measure to investigate this d...

Image Quality and Lesion Detectability of Lower-Dose Abdominopelvic CT Obtained Using Deep Learning Image Reconstruction.

Korean journal of radiology
OBJECTIVE: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images.

Deep learning for image classification in dedicated breast positron emission tomography (dbPET).

Annals of nuclear medicine
OBJECTIVE: This study aimed to investigate and determine the best deep learning (DL) model to predict breast cancer (BC) with dedicated breast positron emission tomography (dbPET) images.

Frameless robot-assisted stereotactic biopsies for lesions of the brainstem-a series of 103 consecutive biopsies.

Journal of neuro-oncology
PURPOSE: Targeted treatment for brainstem lesions requires above all a precise histopathological and molecular diagnosis. In the current technological era, robot-assisted stereotactic biopsies represent an accurate and safe procedure for tissue diagn...