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Head and Neck Neoplasms

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Dynamic stochastic deep learning approaches for predicting geometric changes in head and neck cancer.

Physics in medicine and biology
Modern radiotherapy stands to benefit from the ability to efficiently adapt plans during treatment in response to setup and geometric variations such as those caused by internal organ deformation or tumor shrinkage. A promising strategy is to develop...

Cascaded deep learning-based auto-segmentation for head and neck cancer patients: Organs at risk on T2-weighted magnetic resonance imaging.

Medical physics
PURPOSE: To investigate multiple deep learning methods for automated segmentation (auto-segmentation) of the parotid glands, submandibular glands, and level II and level III lymph nodes on magnetic resonance imaging (MRI). Outlining radiosensitive or...

Learning-based synthetic dual energy CT imaging from single energy CT for stopping power ratio calculation in proton radiation therapy.

The British journal of radiology
OBJECTIVE: Dual energy CT (DECT) has been shown to estimate stopping power ratio (SPR) map with a higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interactions. This work presents a learning-based...

Short-term and pathologic outcomes of robotic versus open pancreatoduodenectomy for periampullary and pancreatic head malignancy: an early experience.

Journal of robotic surgery
Open pancreatoduodenectomy (OPD) is associated with high perioperative morbidity. Adoption of robot-assisted pancreatoduodenectomy (RAPD) has been slow despite ergonomic advantages, improved visualization and dexterity. We aim to report our experienc...

The impact of training sample size on deep learning-based organ auto-segmentation for head-and-neck patients.

Physics in medicine and biology
To investigate the impact of training sample size on the performance of deep learning-based organ auto-segmentation for head-and-neck cancer patients, a total of 1160 patients with head-and-neck cancer who received radiotherapy were enrolled in this ...

Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models.

Radiation oncology (London, England)
BACKGROUND: Contour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a critical issue in this process. An atlas-based automatic segmentation was developed to improve the d...

[Robotics in otorhinolaryngology, head and neck surgery].

HNO
In many surgical specialities, e.g., visceral surgery or urology, the use of robotic assistance is widely regarded as standard for many interventions. By contrast, in European otorhinolaryngology, robotic-assisted surgery (RAS) is rarely conducted. T...

Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: Manual delineation of gross tumor volume (GTV) is essential for radiotherapy treatment planning, but it is time-consuming and suffers inter-observer variability (IOV). In clinics, CT, PET, and MRI are used to inform delineation accuracy d...

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This pla...