Optimizing Palliative Pelvic Radiotherapy in Gynecological Cancers: A Systematic Review and Analysis.

BACKGROUND: Palliative radiotherapy plays a crucial role in managing symptomatic gynecological cancers (GCs). This article aims to systematically review literature studies on palliative pelvic radiotherapy in cervical, endometrial, ovarian, vaginal, and vulvar cancers. The primary focus is centered around evaluating symptom relief, quality of life (QOL), and toxicity in order to ascertain optimal radiotherapy regimens. METHODOLOGY: For this thorough review, we mainly relied on Medline to gather papers published until November 2023. Selected studies specifically detailed symptomatology and QOL responses in palliative pelvic radiotherapy used for GCs. RESULTS: Thirty-one studies, mostly retrospective studies and those lacking standardized outcome measures, showed varied responses. Encouraging outcomes were noted in managing hemorrhage (55%) and pain control (70%). However, comprehensively assessing overall symptom response rates and toxicity remained challenging. Investigations into 10 Gy fractionation revealed benefits in addressing tumor-related bleeding and pain in female genital tract cancers. CONCLUSIONS: Palliative pelvic radiotherapy effectively manages symptomatic GCs. Nonetheless, unresolved dosing and fractionation considerations warrant further investigation. Embracing modern therapies alongside radiotherapy offers improved symptom control, emphasizing the importance of selecting suitable patients for successful GC palliation interventions.

Microsurgical Resection of a Thalamic Tumor Using an Endoscopic Contralateral Supracerebellar Infratentorial Keyhole Approach: 2-Dimensional Operative Video.

The surgical treatment of the thalamic lesions is one of the most technically challenging interventions because of their deep-seated location and the proximity of the surrounding vital neurovascular structures: thalamic nuclei, limbic system components, visual and oculomotricity pathways, deep cerebral veins, and posterior cerebral artery branches.1 Traditional microsurgical approaches for these lesions usually require large open craniotomies and further retraction or resection of neural tissue to facilitate better visualization, associating significant postoperative morbidity or even mortality. The recent improvements in high-resolution fiber optics technology have allowed neurosurgeons to expand their surgical armamentarium for tackling these difficult-to-reach lesions. The patient consented to the procedure, and in this surgical video, we try to emphasize the advantages of an endoscopic supracerebellar infratentorial approach over the classic microsurgical approach, along with the keyhole craniotomy and the sitting positioning of the patient. The endoscope provides better visualization-dynamic in-depth view of the anatomic and pathological structures, angled-optics help the surgeon to see "around-the-corner," and the entire procedure is a more comfortable experience for the surgeon and also for the patient, using the minimally invasive techniques. Moreover, we underline the need for a strong collaboration with an experienced endoscopic surgeon (for eg, ear, nose and throat surgeon), as for other endoscopic skull base approaches. The participants and any identifiable individuals consented to publication of his/her image.

The limitations for expression recognition in computer vision introduced by facial masks.

Facial Expression recognition is a computer vision problem that took relevant benefit from the research in deep learning. Recent deep neural networks achieved superior results, demonstrating the feasibility of recognizing the expression of a user from a single picture or a video recording the face dynamics. Research studies reveal that the most discriminating portions of the face surfaces that contribute to the recognition of facial expressions are located on the mouth and the eyes. The restrictions for COVID pandemic reasons have also revealed that state-of-the-art solutions for the analysis of the face can severely fail due to the occlusions of using the facial masks. This study explores to what extend expression recognition can deal with occluded faces in presence of masks. To a fairer comparison, the analysis is performed in different occluded scenarios to effectively assess if the facial masks can really imply a decrease in the recognition accuracy. The experiments performed on two public datasets show that some famous top deep classifiers expose a significant reduction in accuracy in presence of masks up to half of the accuracy achieved in non-occluded conditions. Moreover, a relevant decrease in performance is also reported also in the case of occluded eyes but the overall drop in performance is not as severe as in presence of the facial masks, thus confirming that, like happens for face biometric recognition, occluded faces by facial mask still represent a challenging limitation for computer vision solutions.

Cervical intranodal schwannoma - a rare diagnosis.

Intranodal schwannoma is a rare benign tumor, which originates from the peripheral nerve sheath (Schwann cells), fewer cases being reported with lymphatic involvement. We present the case of a middle-aged female patient, with one-year growing mass in the lateral-cervical area, in intimate relation with the vascular package of the neck. Preoperative cervical computed tomography examination showed the tumor features. There was no intraoperative complication, with the piece being completely removed. The morphological examination revealed the structure of a lymph node, and after Hematoxylin-Eosin staining, there were eosinophilic cytoplasm, euchromatic nuclei, with round, elongated or slightly wavy form and reduced pleomorphism, rare degenerative nuclear atypia, and no mitotic activity nor necrosis. The expression of S100 protein on immunohistochemistry, along with negative results for smooth muscle actin and desmin sustained the diagnosis of intranodal schwannoma of the neck. With a low index of cellular proliferation (Ki67), this case is in line with the reported features of schwannoma having extremely rare malignant transformation.

Utterance Level Feature Aggregation with Deep Metric Learning for Speech Emotion Recognition.

Emotion is a form of high-level paralinguistic information that is intrinsically conveyed by human speech. Automatic speech emotion recognition is an essential challenge for various applications; including mental disease diagnosis; audio surveillance; human behavior understanding; e-learning and human-machine/robot interaction. In this paper, we introduce a novel speech emotion recognition method, based on the Squeeze and Excitation ResNet (SE-ResNet) model and fed with spectrogram inputs. In order to overcome the limitations of the state-of-the-art techniques, which fail in providing a robust feature representation at the utterance level, the CNN architecture is extended with a trainable discriminative GhostVLAD clustering layer that aggregates the audio features into compact, single-utterance vector representation. In addition, an end-to-end neural embedding approach is introduced, based on an emotionally constrained triplet loss function. The loss function integrates the relations between the various emotional patterns and thus improves the latent space data representation. The proposed methodology achieves 83.35% and 64.92% global accuracy rates on the RAVDESS and CREMA-D publicly available datasets, respectively. When compared with the results provided by human observers, the gains in global accuracy scores are superior to 24%. Finally, the objective comparative evaluation with state-of-the-art techniques demonstrates accuracy gains of more than 3%.

DEEP-SEE: Joint Object Detection, Tracking and Recognition with Application to Visually Impaired Navigational Assistance.

In this paper, we introduce the so-called DEEP-SEE framework that jointly exploits computer vision algorithms and deep convolutional neural networks (CNNs) to detect, track and recognize in real time objects encountered during navigation in the outdoor environment. A first feature concerns an object detection technique designed to localize both static and dynamic objects without any a priori knowledge about their position, type or shape. The methodological core of the proposed approach relies on a novel object tracking method based on two convolutional neural networks trained offline. The key principle consists of alternating between tracking using motion information and predicting the object location in time based on visual similarity. The validation of the tracking technique is performed on standard benchmark VOT datasets, and shows that the proposed approach returns state-of-the-art results while minimizing the computational complexity. Then, the DEEP-SEE framework is integrated into a novel assistive device, designed to improve cognition of VI people and to increase their safety when navigating in crowded urban scenes. The validation of our assistive device is performed on a video dataset with 30 elements acquired with the help of VI users. The proposed system shows high accuracy (>90%) and robustness (>90%) scores regardless on the scene dynamics.

When Ultrasonic Sensors and Computer Vision Join Forces for Efficient Obstacle Detection and Recognition.

In the most recent report published by the World Health Organization concerning people with visual disabilities it is highlighted that by the year 2020, worldwide, the number of completely blind people will reach 75 million, while the number of visually impaired (VI) people will rise to 250 million. Within this context, the development of dedicated electronic travel aid (ETA) systems, able to increase the safe displacement of VI people in indoor/outdoor spaces, while providing additional cognition of the environment becomes of outmost importance. This paper introduces a novel wearable assistive device designed to facilitate the autonomous navigation of blind and VI people in highly dynamic urban scenes. The system exploits two independent sources of information: ultrasonic sensors and the video camera embedded in a regular smartphone. The underlying methodology exploits computer vision and machine learning techniques and makes it possible to identify accurately both static and highly dynamic objects existent in a scene, regardless on their location, size or shape. In addition, the proposed system is able to acquire information about the environment, semantically interpret it and alert users about possible dangerous situations through acoustic feedback. To determine the performance of the proposed methodology we have performed an extensive objective and subjective experimental evaluation with the help of 21 VI subjects from two blind associations. The users pointed out that our prototype is highly helpful in increasing the mobility, while being friendly and easy to learn.

Vagus nerve schwannoma in the parapharyngeal space: surgical, histological and immunohistochemical aspects. A case report.

Tumors of the parapharyngeal space are rare accounting approximately for 0.5% of all head and neck tumors. In the retrostyloid space, schwannomas are a more common finding, in contrast to other tumors. Usually, they present with a variety of slight symptoms until they grow in size and compress surrounding organs. Surgical treatment of parapharyngeal space tumors is difficult; due to the anatomical complex area, they develop in, and include several approaches, according to its size and relations. In this paper, we present a case of a 63-year-old female with a vagus nerve schwannoma in the parapharyngeal space. Beside the surgical difficulties, the resected tumor had a peculiar histopathological aspect (large areas of degeneration and atypia and little typical palisading) that compelled a thorough histological and immunohistochemical evaluation for positive and differential diagnosis.