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. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

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. Here, we evaluated next generation sequencing (NGS) and digital PCR (dPCR) as tools to profile TP53 mutational status and circulating microRNA expression aiming at scoring the locoregional risk of recurrence by means of molecular analyses. Serial monitoring of these biomarkers allowed identifying patients at high risk, laying the ground for accurate tracking of disease evolution and potential intensification of post-operative treatments. Additionally, our pipeline demonstrated its applicability into the clinical routine, being cost-effective and feasible in terms of patient sampling, holding promise to accurately (re)-stage RMs in the era of precision medicine.

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. RESULTS: The best accuracy for discriminating HER2 + versus HER2 - breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR - breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set). CONCLUSIONS: The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome.

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. A comprehensive search of three different databases: MEDLINE, Embase, and Cochrane Library databases. A total of 998 patients were evaluated from the 13 studies. OPSSC p16+ were 729, while controls p16- were 269. The meta-analytic study estimated the diagnostic performance of ctHPVDNA as follows: pooled sensitivity and specificity of 0.90 (95% CI: 0.82-0.94) and 0.94 (95% CI: 0.85-0.98), respectively; positive and negative likelihood ratios of 12.6 (95% CI: 4.9-32.1) and 0.05 (95% CI: 0.02-0.13), respectively. ddPCR for ctHPVDNA has good accuracy, sensitivity, and specificity for diagnosis of HPV + OPSCC. ctHPVDNA kinetic represents a great reliable opportunity to improve diagnostic and therapeutic management of cancer patients and could open new perspectives for understanding tumor biology.

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. 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.

Pretreated EGFRdel19/BRAFV600E 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 EGFRdel19/BRAFV600E 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. Moreover, the impact of different adverse features on prognosis will be investigated. Data was collected on oncological FU of 132 patients. A retrospective review was performed to create a dataset of 23 variables for each patient. RESULTS: The decision-tree algorithm is highly effective in predicting the prognosis, with a 95% accuracy in predicting the 1-year survival and 82.5% in 3-year survival; The measured AUC area is 0.886 at 1-year Test and 0.871 at 3-years Test. The measured AUC area is 0.917 at 1-year Training set and 0.964 at 3-years Training set. Factors that affected 1yOS are: LNR, type of surgery, and subsite. The most significant variables at 3yOS are: number of metastasis, perineural invasion and Grading. CONCLUSIONS: The integration of ML in medical practices could revolutionize our approach on cancer pathology.

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. Lesions were delineated by two expert radiologists. Malignity was always confirmed by histopathological data. The data were randomly divided into training and validation set with an 80:20 ratio. A total of 58 radiomic features were extracted from each lesion using the LIFEx Software. Three different key methods of feature selection were implemented in Python: (1) K best (KB), (2) sequential (S), and (3) Random Forrest (RF). A model was therefore produced for each subset of seven variables using a machine-learning algorithm, which exploits the RF classification based on the Gini index. Results: All three clinical-radiomic models show significant differences (p < 0.05) between malignant and benign tumors. The area under the curve (AUC) values of the models obtained with three different feature selection methods were 0.72 [0.64,0.80], 0.72 [0.64,0.80] and 0.74 [0.66,0.82] for KB, SFS, and RF, respectively. Conclusion: The clinical-radiomic models developed by using radiomic features from DBT images showed a good discriminating power and hence may help radiologists in breast cancer tumor diagnoses already at the first screening.

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. While these options prove promising, available clinical data on mTOR inhibitors' efficacy are somewhat limited. CASE STUDY: We report a case of a 25-year-old female patient diagnosed with a right parasagittal occipital anaplastic meningioma (grade III WHO) in 2013. The patient underwent multiple treatments and, in 2019, a further recurrence occurred. The patient reported an mTOR mutation, and it is for this reason that the MTB approved treatment with everolimus and bevacizumab. Therapy was administered in May 2019, and partial response and prolonged disease control was obtained in November 2021, when progression took place. The patient's death occurred in March 2022. CONCLUSIONS: This case report provides evidence on the efficacy of mTOR inhibitors as a treatment option in recurrent meningiomas. Furthermore, it highlights the importance of performing a molecular analysis as a preliminary step towards targeting the mTOR pathway.

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. A questionnaire was prepared including statements on clinical indications of PWI as well as its acquisition technique and submitted to selected panelists who worked in anonymity using a modified Delphi approach. Each panelist was asked to rate each statement using a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). Statements with scores equal or inferior to 5 assigned by at least two panelists were revised and re-submitted for the subsequent Delphi round to reach a final consensus. RESULTS: Two Delphi rounds were conducted. The final questionnaire consisted of 6 statements on clinical indications of PWI and 9 statements on the acquisition technique of PWI. Four of 19 (21%) statements obtained scores equal or inferior to 5 by two panelists, all dealing with clinical indications. The Delphi process was considered concluded as reasons entered by panelists for lower scores were mainly related to the lack of robust evidence, so that no further modifications were suggested. CONCLUSIONS: Evidence-based recommendations on the use of PWI have been provided by an independent panel of experts worldwide, encouraging a standardized use of PWI across university and research centers to produce more robust evidence.

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). The search was concluded in August 2021. No restriction was applied to publication dates. Results: We obtained 28 articles from the research in PubMed and 114 articles from Scopus. After the removal of six replicas that were counted only once, out of 136 articles, 37 articles were reviews. Eight articles alone have tackled the relation between CESM imaging and ER, PR, HER2, and Ki67. When comparing radiological characterization of the lesions obtained by either CESM or contrast-enhanced MRI, they have a similar association with the proliferation of tumoral cells, as expressed by Ki-67. In CESM-enhanced lesions, the expression was found to be 100% for ER and 77.4% for PR, while moderate or high HER2 positivity was found in lesions with non-mass enhancement and with mass closely associated with a non-mass enhancement component. Conversely, the non-enhancing breast cancer lesions were not associated with any prognostic factor, such as ER, PR, HER2, and Ki67, which may be associated with the probability of showing enhancement. Radiomics on CESM images has the potential for non-invasive characterization of potentially heterogeneous tumors with different hormone receptor status. Conclusions: CESM enhancement is associated with the proliferation of tumoral cells, as well as to the expression of estrogen and progesterone receptors. As CESM is a relatively young imaging technique, a few related works were found; this may be due to the "off-label" modality. In the next few years, the role of CESM in breast cancer diagnostics will be more thoroughly investigated.

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. Patients diagnosed with LA-OPC were studied with diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI at baseline and at the 10th radiotherapy (RT) fraction. Clinical information was collected as well. The endpoint of the study was the development of disease progression, locally or distantly. Of the 97 patients enrolled, 68 were eligible for analysis. Disease progression was recorded in 21 patients (11 had loco-regional progression, 10 developed distant metastases). We found a correlation between N diameter and disease control (p = 0.02); features such as p16 status and extranodal extension only showed a trend towards statistical significance. Among perfusion MRI features, higher median values of Kep both in primary tumor (T, p = 0.016) and lymph node (N, p = 0.003) and lower median values of ve (p = 0.018 in T, p = 0.004 in N) correlated with better disease control. Kep P90 and N diameter were identified by MRMR algorithm as the best predictors of outcome. In conclusion, the association of non-invasive MRI biomarkers and patients and tumor characteristics may help in predicting disease behavior and patient outcomes in order to ensure a more customized treatment.

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. Two experienced breast radiologists, blinded to cancer status, read the images. Diagnostic accuracy of (1) CESM as an adjunct to FFDM and US, and (2) 3T MRI as an adjunct to CESM compared to FFDM and US, was assessed. Measures of accuracy were sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV). Results: There were 118 patients included along with 142 histologically characterized lesions. K agreement values were 0.69, 0.68, 0.63 and 0.56 for concordance between the gold standard and FFDM, FFDM + US, CESM and MRI, respectively (p < 0.001, for all). K concordance for CESM was 0.81 with FFDM + US and 0.73 with MRI (p value < 0.001 for all). Conclusions: CESM may represent a valuable alternative and/or an integrating technique to MRI in the evaluation of breast cancer patients.

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). We marked these as 0 or 1 in the report calculating a report score (RS) and a maximum sum (MS) of each list. Two radiologists and two otolaryngologists in consensus classified reports as low quality (LQ) if the RS fell in the percentage range 0-59% of MS and as high quality (HQ) if it fell in the range 60-100%, annotating technique and district. We evaluated the distribution of reports in these categories. RESULTS: Two hundred thirty-seven reports (97 CT and 140 MRI) of 95 oral cavity, 52 laryngeal, 47 oropharyngeal, 19 hypo-pharyngeal, 14 parotid, and 10 nasopharyngeal cancers were included. Sixty-six percent of all the reports were LQ for T, 66% out of all the MRI reports, and 65% out of all CT reports were LQ. Eight-five percent of reports were HQ for N, 85% out of all the MRI reports, and 82% out of all CT reports were HQ. Reports of oral cavity, oro-nasopharynx, and parotid were LQ, respectively, in 76%, 73%, 100% and 92 out of cases. CONCLUSION: Reports of staging CT/MRI in H&N cancer were LQ for T description and HQ for N description.

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. STUDY DESIGN: Retrospective observational study. METHODS: An experienced radiologist retrospectively measured AC thickness and identified signs of anterior PGS infiltration on pretreatment contrast-enhanced CT scans of 80 patients with primary glottic cancer. The gold standard to define the presence of cancer in the AC was histology. The receiver operating characteristic (ROC) curves were used to determine the potential cut-off values of AC thickness (Youden index method) able to maximize both sensitivity and specificity in identifying the presence of cancer in the AC at histology and PGS infiltration on CT scan. RESULTS: AC was significantly thicker in patients with cancer in the AC at histology (P < .001) and in patients with PGS infiltration on CT scan (P < .001). The cut-off values to discriminate the presence of cancer at histology and PGS infiltration on CT scan were 3.62 and 2.6 mm, respectively. We found a substantial agreement between anterior PGS infiltration on CT scan and the presence of cancer in the AC at histology (Cohen Kappa: P = .70). CONCLUSION: AC thickness and radiologic signs of PGS infiltration on pretreatment CT scan could represent a method to predict the presence of cancer in the AC at histology. LEVEL OF EVIDENCE: 4 Laryngoscope, 132:2427-2433, 2022.

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. The results will hopefully provide more information on Tedopi combinational treatment compared with current standard of care in NSCLC patients who fail first-line chemoimmunotherapy. Clinical Trial Registration: NCT04884282 (ClinicalTrials.gov).

Patients with lung cancer that has spread to other parts of the body are usually treated with a combination of chemotherapy and drugs that stimulate the immune system to kill cancer cells, which is referred to as immunotherapy. If after receiving these drugs the cancer still gets worse, patients have only a few treatment options left and are usually treated with chemotherapy only. Researchers will study if a new medicine called Tedopi^®, a vaccine that specifically attacks cancer cells, used together with chemotherapy or immunotherapy, will work better then chemotherapy alone for these patients. The study will monitor how long patients will live after treatment, for how long they will live without their disease getting worse and how many patients will improve after treatment. Moreover, researchers will study if patients present specific features, such as certain molecules in their tumor cells or blood cells, that may indicate that they respond better to certain treatments.

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. Punctuated evolution of the trunk was supported by en bloc inheritance of a large set (19 distinct genes) of copy number alterations. Branched evolution was supported by the distribution of site-specific SNVs. Convergent evolution was characterized by a unique asynchronous expansion of three actionable (OncoKB level 3A) mutations at two consecutive ESR1 codons. Low or undetectable in all the sampled tumor tissues, ESR1 mutations expanded rapidly in blood during HER2/hormone double-blockade, and predicted life-threatening local progression at lung and liver metastatic foci. Dramatic clinical response to Fulvestrant (assigned off-label exclusively based on liquid biopsy) was associated with clearance of all 3 subclones and was in stark contrast to the poor therapeutic efficacy reported in large liquid biopsy-informed interventional trials. Altogether, deconvolution of the tumor phylogenetic tree, as shown herein, may help to customize treatment in breast cancers that rapidly develop refractoriness to multiple drugs.

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. The diffusion coefficient (D), the kurtosis parameter (K), the transfer constants Ktrans and Kep and the volume of extravascular extracellular space ve 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.

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 (XER12) 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. The Radiation Therapy Oncology Group (RTOG) toxicity scale was used to evaluate salivary gland toxicity. Xerostomia-related questionnaires (XQs) were administered weekly during and after RT. RTOG toxicity ≥ grade 2 at XER12 was considered as endpoint to build prediction models. A Decision Tree classification learner was applied to build the prediction models following a five-fold cross-validation. Of the 89 patients enrolled, 63 were eligible for analysis. Thirty-six (57.1%) and 21 (33.3%) patients developed grade 1 and grade 2 XER12, respectively. Including only baseline variables, the model based on DCE-MRI and V65 (%) (volume of both glands receiving doses ≥ 65 Gy) had a fair accuracy (77%, 95% CI: 66.5-85.4%). The model based on V65 (%) and XQ-Intmid (integral of acute XQ scores from the start to the middle of RT) reached the best accuracy (81%, 95% CI: 71-88.7%). In conclusion, non-invasive biomarkers from DCE-MRI, in combination with dosimetric variables and self-assessed acute XQ scores during treatment may help predict grade 2 XER12 with a fair to good accuracy.