Head and Neck Tumors: Management of Primary Undifferentiated Pleomorphic Sarcoma

Undifferentiated pleomorphic sarcoma in the maxillary sinus: a case report

BACKGROUND

Undifferentiated pleomorphic sarcoma, previously called malignant fibrous histiocytoma, is a type of malignant mesenchymal tumor (sarcoma) of soft tissue and sometimes bone. It is uncommon in the oral cavity and very sporadic in the maxillary sinus. Microscopic diagnosis of this malignancy in the maxillary sinus can be very challenging, because there is a range of features that may overlap with other benign and malignant tumors.

CASE PRESENTATION

In this paper, we report a case of undifferentiated pleomorphic sarcoma in the maxillary sinus of a 61-year-old Iranian man who was referred to the maxillofacial surgery ward due to pain and swelling of the upper jaw and visual problems in the right eye. In the initial incisional biopsy, peripheral giant cell granuloma was diagnosed in the hospital service. Yet, on request of the surgeon, during reviewing the slides in the oral pathology service of the School of Dentistry, and using an immunohistochemical method, undifferentiated pleomorphic sarcoma was diagnosed. In this paper, a case of undifferentiated pleomorphic sarcoma in the maxillary sinus is reported, with an emphasis on the management of its problems and diagnostic errors.

CONCLUSION

This study reviews the challenges and histopathological diagnostic errors of this uncommon tumor in this rare location. This lesion may be similar to other malignant tumors, and its correct diagnosis requires a detailed and complete examination.

Deep Convolutional Neural Network for Accurate Classification of Myofibroblastic Lesions on Patch-Based Images

OBJECTIVE

This study aimed to implement and evaluate a Deep Convolutional Neural Network for classifying myofibroblastic lesions into benign and malignant categories based on patch-based images.

METHODS

A Residual Neural Network (ResNet50) model, pre-trained with weights from ImageNet, was fine-tuned to classify a cohort of 20 patients (11 benign and 9 malignant cases). Following annotation of tumor regions, the whole-slide images (WSIs) were fragmented into smaller patches (224 × 224 pixels). These patches were non-randomly divided into training (308,843 patches), validation (43,268 patches), and test (42,061 patches) subsets, maintaining a 78:11:11 ratio. The CNN training was caried out for 75 epochs utilizing a batch size of 4, the Adam optimizer, and a learning rate of 0.00001.

RESULTS

ResNet50 achieved an accuracy of 98.97%, precision of 99.91%, sensitivity of 97.98%, specificity of 99.91%, F1 score of 98.94%, and AUC of 0.99.

CONCLUSIONS

The ResNet50 model developed exhibited high accuracy during training and robust generalization capabilities in unseen data, indicating nearly flawless performance in distinguishing between benign and malignant myofibroblastic tumors, despite the small sample size. The excellent performance of the AI model in separating such histologically similar classes could be attributed to its ability to identify hidden discriminative features, as well as to use a wide range of features and benefit from proper data preprocessing.

Poorly differentiated sarcoma of the maxillary sinus: a histopathology dilemma of a rare tumor

Sarcomas are a rare heterogeneous group of neoplasms of mesenchymal origin. In the redistribution of all head and neck malignancies, sarcomas are represented by only 1%. Herein, we report a case of a 66-year-old patient with right maxillary sinus sarcoma that spread through the ostiomeatal complex, infiltrated the septum, all ethmoid cells, frontal sinus, involved the entire right nasal cavity and penetrated to the nasopharynx. Patient was treated with neoadjuvant chemotherapy, surgery and adjuvant radiotherapy. The histopathology indicated poorly differentiated sarcoma with elements of Ewing's sarcoma, but also with elements consistent with osteosarcoma. Molecular pathological analysis excluded Ewing's sarcoma. Samples were also sent for review to the other Pathology Clinics. They suggested poorly differentiated high-grade pleomorphic sarcoma with elements of osteosarcoma. The accurate diagnosis of the head and neck sarcoma type can be a histopathology dilemma posing a great challenge in the choice of therapeutic approach, and thus the treatment outcome.

Giant pleomorphic sarcoma of the tongue base - a cured clinical case report and literature review

Pleomorphic sarcoma of the tongue base is an extremely rare pathology finding. Our review of current databases returned fewer than 10 articles available free full text on this subject. We review the current state of art management guidelines for this type of tumor. Our case presented surprisingly a favorable evolution despite the huge dimensions, the tumor type, and associated pathology. The patient received radiation therapy and oncological treatment followed by revision surgery consisting of partial glossectomy for the residual tumor. Histological examination of the operatory specimen showed a residual tumor of pleomorphic sarcoma type, with clear margins. The evolution was without relapse after 18 months.

Nomogram for predicting cancer-specific survival in undifferentiated pleomorphic sarcoma: A Surveillance, Epidemiology, and End Results -based study

INTRODUCTION

The purpose of this study was to construct and validate a nomogram for predicting cancer-specific survival (CSS) in undifferentiated pleomorphic sarcoma (UPS) patients at 3, 5, and 8 years after the diagnosis.

METHODS

Data for UPS patients were extracted from the SEER (Surveillance, Epidemiology, and End Results) database. The patients were randomly divided into a training cohort (70%) and a validation cohort (30%). The backward stepwise Cox regression model was used to select independent prognostic factors. All of the factors were integrated into the nomogram to predict the CSS rates in UPS patients at 3, 5, and 8 years after the diagnosis. The nomogram' s performance was then validated using multiple indicators, including the area under the time-dependent receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, decision-curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI).

RESULTS

This study included 2,009 UPS patients. Ten prognostic factors were identified after analysis of the Cox regression model in the training cohort, which were year of diagnosis, age, race, primary site, histological grade, T, N, M stage, surgery status, and insurance status. The nomogram was then constructed and validated internally and externally. The relatively high C-indexes and AUC values indicated that the nomogram has good discrimination ability. The calibration curves revealed that the nomogram was well calibrated. NRI and IDI values were both improved, indicating that our nomogram was superior to the AJCC (American Joint Committee on Cancer) system. DCA curves demonstrated that the nomogram was clinically useful.

CONCLUSIONS

The first nomogram for predicting the prognosis of UPS patients has been constructed and validated. Its usability and performance showed that the nomogram can be applied to clinical practice. However, further external validation is still needed.