Prediction of tumor response to neoadjuvant chemotherapy in high-grade osteosarcoma using clustering-based analysis of magnetic resonance imaging: an exploratory study.

Purpose: To evaluate the ability of magnetic resonance imaging (MRI)-based clustering analysis to predict the pathological response to neoadjuvant chemotherapy (NACT) in patients with primary high-grade osteosarcoma.

Materials And Methods: Twenty-two patients were included in this retrospective study. All patients underwent MRIs before and after NACT.

The landscape of circulating tumor HPV DNA and TTMV-HPVDNA for surveillance of HPV-oropharyngeal carcinoma: systematic review and meta-analysis.

Background: Human papilloma virus (HPV) related cancers of the oropharynx are rapidly increasing in incidence and may soon represent the majority of all head and neck cancers. Improved monitoring and surveillance methods are thus an urgent need in public health.

Main Text: The goal is to highlight the current potential and limitations of liquid biopsy through a meta analytic study on ctHPVDNA and TTMV-HPVDNA.

Circulating tumor HPV DNA in the management of HPV+ oropharyngeal cancer and its correlation with MRI.

Background: First aim was to compare ddPCR assays of ctHPVDNA with p16 IHC and qualitative HPV PCR. Second aim was to carry out longitudinal blood sampling to test for association of ctHPVDNA with histological confirmed recurrence. Third aim was to perform a multidimensional assessment which included: (1) clinical features; (2) ctHPVDNA; (3) MRI-based tumor size measurements of primary tumor (PT) and cervical lymph node metastases (CLNM).

Myopericytoma: a rare case of parapharyngeal localization.

This case study presents a 66-year-old man referred to the Otolaryngology and Head and Neck Surgery department due to a one-history of persistent pain in the left posterior cervical region. No abnormalities were detected in the oral and pharyngeal regions during clinical and endoscopic examinations. Subsequently, a magnetic resonance imaging revealed a lesion (14 × 12 × 14 mm) into the left parapharyngeal space, with high signal intensity on T2-weighted images, enhancement after contrast medium, restricted signal on diffusion weighted imaging and high vascularization on perfusion MRI.

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

Objective: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer.

Methods: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis.

Combined TP53 status in tumor-free resection margins and circulating microRNA profiling predicts the risk of locoregional recurrence in head and neck cancer.

Locoregional recurrences represent a frequently unexpected problem in head and neck squamous cell carcinoma (HNSCC). Relapse often (10-30%) occurs in patients with histologically negative resection margins (RMs), probably due to residual tumor cells or hidden pre-cancerous lesions in normal mucosa, both missed by histopathological examination. Therefore, definition of a 'clean' or tumor-negative RM is controversial, demanding for novel approaches to be accurately explored.

Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome.

Objective: The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome.

Methods: A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 -); (3) presence of the hormone receptor (HR + vs HR -); and (4) presence of luminal subtypes of breast cancer.

It is time to improve the diagnostic workup of oropharyngeal cancer with circulating tumor HPV DNA: Systematic review and meta-analysis.

The possibility of detecting circulating tumor HPV DNA (ctHPVDNA) in plasma in patients with oropharyngeal cancer has been demonstrated in several reports. However, these data are from small cohorts and available tests for detection of ctHPVDNA are not fully validated. The aim is to evaluate sensitivity, specificity, and accuracy of ctHPVDNA by ddPCR to define its efficacy in the clinical setting for the diagnosis of HPV + OPSCC.

Magnetic resonance imaging-based prediction models for tumor stage and cervical lymph node metastasis of tongue squamous cell carcinoma.

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.

Pretreated / Lung Adenocarcinoma With Leptomeningeal Disease Achieving Long-Lasting Disease Control on Osimertinib, Dabrafenib, and Trametinib: A Case Report.

Oncogene-addicted NSCLC inevitably becomes resistant to targeted therapy by developing acquired resistance through on- or off-target mechanisms, potentially detectable by liquid biopsy. We present the first reported case of a patient with pretreated / lung adenocarcinoma and symptomatic leptomeningeal metastasis obtaining durable clinical benefit on osimertinib, dabrafenib, and trametinib treatment.

Machine learning in laryngeal cancer: A pilot study to predict oncological outcomes and the role of adverse features.

Background: Laryngeal carcinoma (LC) remains a significant economic and emotional problem to the healthcare system and severe social morbidity. New tools as Machine Learning could allow clinicians to develop accurate and reproducible treatments.

Methods: This study aims to evaluate the performance of a ML-algorithm in predicting 1- and 3-year overall survival (OS) in a cohort of patients surgical treated for LC.

Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis.

Objective: This study aimed to develop a clinical-radiomic model based on radiomic features extracted from digital breast tomosynthesis (DBT) images and clinical factors that may help to discriminate between benign and malignant breast lesions.

Materials And Methods: A total of 150 patients were included in this study. DBT images acquired in the setting of a screening protocol were used.

Combi-TED: a new trial testing Tedopi with docetaxel or nivolumab in metastatic non-small-cell lung cancer progressing after first line.

Despite the positive results obtained by first-line chemoimmunotherapy in patients with metastatic non-small-cell lung cancer (NSCLC), only a few second-line options are available after disease progression. Combi-TED is a phase II international study that will assess the efficacy of Tedopi, a cancer vaccine, combined with either docetaxel or nivolumab and compared with docetaxel monotherapy in patients with metastatic NSCLC after chemoimmunotherapy. The study, currently in the recruitment phase, will assess 1-year overall survival (primary end point), patient's progression-free survival and overall response rate, as well as the correlation of efficacy with several tumor or blood biomarkers.

Unusual phylogenetic tree and circulating actionable ESR1 mutations in an aggressive luminal/HER2-low breast cancer: Case report.

Under therapeutic pressure aggressive tumors evolve rapidly. Herein, a luminal B/HER2-low breast cancer was tracked for >3 years during a total of 6 largely unsuccessful therapy lines, from adjuvant to advanced settings. Targeted next generation sequencing (NGS) of the primary lesion, two metastases and 14 blood drawings suggested a striking, unprecedented coexistence of three evolution modes: punctuated, branched and convergent.

Microenvironmental Factors in Oral Cavity Squamous Cell Carcinoma Undergoing Surgery: Correlation with Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced MRI.

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.

Malignant meningioma mTOR mutated and precision medicine.

Background: WHO grade II and III meningiomas are more invasive than grade I malignancies and determine patients' shorter overall survival. Their tendency to recur after treatment has represented an important therapeutic challenge because of the limited treatment strategies at recurrence. Angiogenesis and mechanistic target of rapamycin (mTOR) activation are two of the main features of higher grade meningiomas, determining invasiveness and tendency to relapse.

Clinical indications and acquisition protocol for the use of dynamic contrast-enhanced MRI in head and neck cancer squamous cell carcinoma: recommendations from an expert panel.

Background: The clinical role of perfusion-weighted MRI (PWI) in head and neck squamous cell carcinoma (HNSCC) remains to be defined. The aim of this study was to provide evidence-based recommendations for the use of PWI sequence in HNSCC with regard to clinical indications and acquisition parameters.

Methods: Public databases were searched, and selected papers evaluated applying the Oxford criteria 2011.

How Dual-Energy Contrast-Enhanced Spectral Mammography Can Provide Useful Clinical Information About Prognostic Factors in Breast Cancer Patients: A Systematic Review of Literature.

Introduction: In the past decade, a new technique derived from full-field digital mammography has been developed, named contrast-enhanced spectral mammography (CESM). The aim of this study was to define the association between CESM findings and usual prognostic factors, such as estrogen receptors, progesterone receptors, HER2, and Ki67, in order to offer an updated overview of the state of the art for the early differential diagnosis of breast cancer and following personalized treatments.

Materials And Methods: According to the PRISMA guidelines, two electronic databases (PubMed and Scopus) were investigated, using the following keywords: breast cancer AND (CESM OR contrast enhanced spectral mammography OR contrast enhanced dual energy mammography) AND (receptors OR prognostic factors OR HER2 OR progesterone OR estrogen OR Ki67).

Predictors of Outcome after (Chemo)Radiotherapy for Node-Positive Oropharyngeal Cancer: The Role of Functional MRI.

The prognosis of a subset of patients with locally advanced oropharyngeal cancer (LA-OPC) is still poor despite improvements in patient selection and treatment. Identifying specific patient- and tumor-related factors can help to select those patients who need intensified treatment. We aimed to assess the role of historical risk factors and novel magnetic resonance imaging (MRI) biomarkers in predicting outcomes in these patients.

Diagnostic Accuracy of Contrast-Enhanced, Spectral Mammography (CESM) and 3T Magnetic Resonance Compared to Full-Field Digital Mammography plus Ultrasound in Breast Lesions: Results of a (Pilot) Open-Label, Single-Centre Prospective Study.

Introduction: To assess the diagnostic accuracy of CESM and 3T MRI compared to full-field digital mammography (FFDM), plus US, in the evaluation of advanced breast lesions. Materials and Methods: Consenting women with suspicious findings underwent FFDM, US, CESM and 3T MRI. Breast lesions were histologically assessed, with histology being the gold standard.

An approach to evaluate the quality of radiological reports in Head and Neck cancer loco-regional staging: experience of two Academic Hospitals.

Objectives: To evaluate the quality of the reports of loco-regional staging computed tomography (CT) or magnetic resonance imaging (MRI) in head and neck (H&N) cancer.

Methods: Consecutive reports of staging CT and MRI of all H&N cancer cases from 2018 to 2020 were collected. We created lists of quality indicators for tumor (T) for each district and for node (N).

Radiological Findings in Laryngeal Anterior Commissure Invasion: CT Scan Highlights.

Objectives: Preoperative anterior commissure (AC) evaluation in glottic cancer is crucial for therapeutic decisions. Endoscopy is often inadequate to precisely detect the presence of cancer in the AC; thus, computed tomography (CT) scan could help. We investigated the relation between AC thickness on CT scan (in mm), AC involvement by cancer at histology, and radiologic signs of anterior paraglottic space (PGS) infiltration.

The Role of Patient- and Treatment-Related Factors and Early Functional Imaging in Late Radiation-Induced Xerostomia in Oropharyngeal Cancer Patients.

The advent of quantitative imaging in personalized radiotherapy (RT) has offered the opportunity for a better understanding of individual variations in intrinsic radiosensitivity. We aimed to assess the role of magnetic resonance imaging (MRI) biomarkers, patient-related factors, and treatment-related factors in predicting xerostomia 12 months after RT (XER) in patients affected by oropharyngeal squamous cell carcinoma (OSCC). Patients with locally advanced OSCC underwent diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI at baseline; DWI was repeated at the 10th fraction of RT.

AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well?

Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). However, lack of parameter standardization limits clinical applications. Many machine learning (ML) radiomic models employ single classifiers rather than ensemble learning, which is known to boost performance, and comparative analyses are lacking in the literature.