Giant cell tumour of bone (GCTB) is a benign, locally aggressive primary bone neoplasm that represents 5% of all bone tumours. The principal treatment approach is surgery. Although generally GCTB is considered only a locally aggressive disease, it can metastasise, and lung metastases occur in 1–9% of patients. To date, only the use of denosumab has been approved as medical treatment for GCTB. Even more rarely, GCTB undergoes sarcomatous transformation into a malignant tumour (4% of all GCTB), but history of this malignant transformation is unclear and unpredictable. Considering the rarity of the event, the data in the literature are few. In this review, we summarise published data of GCTB malignant transformation and we analyse three cases of malignant transformation of GCTB, evaluating histopathology, genetics, and radiological aspects. Despite the rarity of this event, we conclude that a strict follow up is recommended to detect early malignant transformation.

Background: In this prospective study, we hypothesized that magnetic resonance imaging (MRI) may represent not only the tumor but also the microenvironment, reflecting the heterogeneity and microstructural complexity of neoplasms. We investigated the correlation between both diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced (DCE)-MRI with the pathological factors in oral cavity squamous cell carcinomas (OSCCs). Methods: A total of 37 patients with newly diagnosed OSCCs underwent an MR examination on a 3T system. The diffusion coefficient (D), the kurtosis parameter (K), the transfer constants K^trans and K_ep and the volume of extravascular extracellular space v_e were quantified. A histogram-based approach was proposed to investigate the associations between the imaging and the pathological factors based on the histology and immunochemistry. Results: Significant differences in the DCE-MRI and DKI parameters were found in relation to the inflammatory infiltrate, tumor grading, keratinization and desmoplastic reaction. Relevant relationships emerged between tumor-infiltrating lymphocytes (TILs) and DKI, with lower D and higher K values being associated with increased TILs. Conclusion: Although a further investigation is needed, these findings provide a more comprehensive biological characterization of OSCCs and may contribute to a better understanding of DKI-derived parameters, whose biophysical meaning is still not well-defined.

Purpose: To evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC). Materials and methods: 108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension—together with shape-based and intensity-based features—were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared. Results: MRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78–0.92) and 0.81 (0.64–0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57–0.78) and 0.69 (0.51–0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy. Conclusion: MRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis.

Background: Oropharyngeal squamous cell carcinoma (OPSCC) associated with human papillomavirus (HPV) has higher rates of locoregional control and a better prognosis than HPV-negative OPSCC. These differences are due to some unique biological characteristics that are also visible through advanced imaging modalities. We investigated the ability of a multifactorial model based on both clinical factors and diffusion-weighted imaging (DWI) to determine the HPV status in OPSCC. Methods: The apparent diffusion coefficient (ADC) and the perfusion-free tissue diffusion coefficient D were derived from DWI, both in the primary tumor (PT) and lymph node (LN). First- and second-order radiomic features were extracted from ADC and D maps. Different families of machine learning (ML) algorithms were trained on our dataset using five-fold cross-validation. Results: A cohort of 144 patients was evaluated retrospectively, which was divided into a training set (n = 95) and a validation set (n = 49). The 50th percentile of D_PT, the inverse difference moment of ADCLN, smoke habits, and tumor subsite (tonsil versus base of the tongue) were the most relevant predictors. Conclusions: DWI-based radiomics, together with patient-related parameters, allowed us to obtain good diagnostic accuracies in differentiating HPV-positive from HPV-negative patients. A substantial decrease in predictive power was observed in the validation cohort, underscoring the need for further analyses on a larger sample size.

Background: Undifferentiated soft-tissue sarcomas (USTS) are one of the most common sarcoma histotypes in adults. The standard of care is surgical excision plus adjuvant radiotherapy, while the use of perioperative chemotherapy is still controversial. The aim of this study was to investigate the value of pre-treatment [18F]FDG PET/CT conventional metrics and textural features in predicting disease-free survival (DFS) and overall survival (OS) in patients with USTS of the limbs and trunk. Methods: [18F]FDG PET/CT scans of 51 consecutive patients with locally advanced USTS were retrospectively evaluated. Conventional and textural PET parameters were analysed and tested as predictive factors for DFS and OS. Results: During a median follow up of 50.7 months, 23 (45.1%) and 29 (56.9%) patients had death or disease progression, respectively. Univariate analysis revealed a significant association for perioperative treatment, PET volumetric parameters and the textural feature GLCM_correlation with DFS and OS. In multivariate analysis, perioperative treatment and GLCM_correlation were the only independent factors, allowing stratification of the population into three different prognostic classes. Conclusion: GLCM_correlation can identify USTS at high risk of relapse and death, thus helping to optimize the perioperative treatment of patients.

Localization of metastases into the parotid gland is a very uncommon event. Usually they arise from primary tumors located in the head and neck, mainly melanoma or epidermoid carcinoma of the skin, while other histotypes, from others anatomical districts, hardly have a metastatic spread to the parotid. Myxoid liposarcoma (MLS) is a rare malignant tumor of the soft tissue that mainly occurs in the extremities, representing the second most common subtype of liposarcoma. Although it is typical for liposarcomas to metastasize to the lungs, it is known that MLS can spread also to extra pulmonary sites. The authors report a case of myxoid liposarcoma of the left thigh in a 64-year-old man, with an unusual metastatic double presentation to the contralateral forearm first and to the parotid gland then. MLS with metastatic disease to the parotid gland is an extremely rare event with very few cases reported in the English literature.