107 research outputs found
Transarterial (chemo)embolization and radiofrequency ablation for patients with hepatic metastases (mts) of head and neck carcinoma (HNC) resistant to systemic treatment
No full textDistinguishing lymph nodes in head and neck cancer patients using MRI-based radiomics
No full textThe identification of the primary tumor location in patients with head and neck carcinoma of unknown primary involves an invasive and complex diagnostic protocol, thus fostering the development of non-invasive methods. Herein, a radiomic-based approach was proposed to distinguish between oropharynx and nasopharynx primary tumor location from the lymph nodes segmented in magnetic resonance images of head and neck cancer (HNC) patients. A total of 200 HNC patients (100 oropharynx and 100 nasopharynx) were considered. 10-fold cross-validation with class proportion was applied. Five different feature selection methods and five machine learning classification algorithms were tested. Overall, a high classification performance was achieved by all the combined feature selections/machine learning algorithms, with the best results obtained from the support vector machine and the neural networks algorithms with neighborhood component analysis (acc 100%)
Gynecologic tumors : How to communicate imaging results to the surgeon
No full textGynecologic cancers are a leading cause of morbidity and mortality for female patients, with an estimated 88,750 new cancer cases and 29,520 deaths in the United States in 2012. To offer the best treatment options to patients it is important that the radiologist, surgeon, radiation oncologist, and gynecologic oncologist work together with a multidisciplinary approach. Using the available diagnostic imaging modalities, the radiologist must give appropriate information to the surgeon in order to plan the best surgical approach and its timing
Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images
No full textThe objectives of the study are to develop a new way to assess stability and discrimination capacity of radiomic features without the need of test-retest or multiple delineations and to use information obtained to perform a preliminary feature selection. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of two groups of patients: 18 with soft tissue sarcomas (STS) and 18 with oropharyngeal cancers (OPC). Sixty-nine radiomic features were computed, using three different histogram discretizations (16, 32, and 64 bins). Geometrical transformations (translations) of increasing entity were applied to the regions of interest (ROIs), and the intra-class correlation coefficient (ICC) was used to compare the features computed on the original and modified ROIs. The distribution of ICC values for minimal and maximal entity translations (ICC10 and ICC100, respectively) was used to adjust thresholds of ICC (ICCmin and ICCmax) used to discriminate between good, unstable (ICC10 < ICCmin), and non-discriminative features (ICC100 > ICCmax). Fifty-four and 59 radiomic features passed the stability-based selection for all the three histogram discretizations for the OPC and STS datasets, respectively. The excluded features were similar across the different histogram discretizations (Jaccard’s index 0.77 ± 0.13 and 0.9 ± 0.1 for OPC and STS, respectively) but different between datasets (Jaccard’s index 0.19 ± 0.02). The results suggest that the observed radiomic features are mainly stable and discriminative, but the stability depends on the region of the body under observation. The method provides a way to assess stability without the need of test-retest or multiple delineations
Does Dose Modification Affect Efficacy of First-Line Pazopanib in Metastatic Renal Cell Carcinoma?
No full textBACKGROUND:
Pazopanib is a standard treatment for metastatic renal cell carcinoma (mRCC), and 800 mg/daily is considered the optimal dose. However, some patients require dose modification because of toxicity. Whether a reduced dose of pazopanib is as effective as the standard dose in achieving clinical benefit remains unclear.
OBJECTIVES:
Our objective was to conduct a retrospective analysis to investigate the clinical effect of different therapeutic doses of first-line pazopanib in patients with mRCC.
METHODS:
Consecutive patients with mRCC treated with first-line pazopanib between 2011 and 2016 at the Istituto Nazionale Tumori of Milan were retrospectively analysed for demographics, response, outcomes, and toxicity. Three patient groups were compared: group 1 received the standard dose of 800 mg/day; group 2 started with 800 mg/day and then reduced the dose to 400 or 600 mg/day because of toxicity; and group 3 received a reduced starting dose of 400 or 600 mg/day because they had an Eastern Cooperative Oncology Group (ECOG) performance status (PS) of 2 and/or comorbidities.
RESULTS:
In total, 69 patients were evaluated: 34 in group 1, 19 in group 2, and 16 in group 3. After a median follow-up of 13.9 months (range 0.3-43.8), 27 (39.1%) patients had progressive disease (PD) and three (4.3%) patients had died. The incidence rate of PD or death per 100 person-months was 2.5 [95% confidence interval (CI) 0.6-4.4; hazard ratio (HR) 1] in group 1 and 3.9 (95% CI 0-14.3; HR 1.43) in the combined group (2 + 3). The discontinuation rate due to PD was 28% in group 1, 42% in group 2, and 44% in group 3. The objective response rate was 44, 11, and 19% in groups 1, 2, and 3, respectively.
CONCLUSIONS:
Our results may suggest that patients with mRCC receiving a lower dose of first-line pazopanib might not have a meaningful progression-free survival advantage compared with those receiving a standard dose. These data highlight that proper management of treatment-related side effects may lead to optimal drug exposure
Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer
No full textIn this paper, several radiomics-based predictive models of response to induction chemotherapy (IC) in sinonasal cancers (SNCs) are built and tested. Models were built as a combination of radiomic features extracted from three types of MRI images: T1-weighted images, T2-weighted images and apparent diffusion coefficient (ADC) maps. Fifty patients (aged 54 ± 12 years, 41 men) were included in this study. Patients were classified according to their response to IC (25 responders and 25 nonresponders). Not all types of images were acquired for all of the patients: 49 had T1-weighted images, 50 had T2-weighted images and 34 had ADC maps. Only in a subset of 33 patients were all three types of image acquired. Eighty-nine radiomic features were extracted from the MRI images. Dimensionality reduction was performed by using principal component analysis (PCA) and by selecting only the three main components. Different algorithms (trees ensemble, K-nearest neighbors, support vector machine, naïve Bayes) were used to classify the patients as either responders or nonresponders. Several radiomic models (either monomodality or multimodality obtained by a combination of T1-weighted, T2-weighted and ADC images) were developed and the performance was assessed through 100 iterations of train and test split. The area under the curve (AUC) of the models ranged from 0.56 to 0.78. Trees ensemble, support vector machine and naïve Bayes performed similarly, but in all cases ADC-based models performed better. Trees ensemble gave the highest AUC (0.78 for the T1-weighted+T2-weighted+ADC model) and was used for further analyses. For trees ensemble, the models based on ADC features performed better than those models that did not use those features (P < 0.02 for one-tail Hanley test, AUC range 0.68–0.78 vs 0.56–0.69) except the T1-weighted+ADC model (AUC 0.71 vs 0.69, nonsignificant differences). The results suggest the relevance of ADC-based radiomics for prediction of response to IC in SNCs
NUT carcinoma of the submandibular gland: A case report
No full textBackground: NUT carcinoma (NUTc) is a rare and aggressive malignant epithelial tumor characterized by rearrangement of the NUT gene on chromosome 15q14. Methods: In this article, we present the fifth case worldwide of a young woman affected by a NUTc arising from a submandibular gland, presenting as a rapidly evolving mass. She underwent a right scialoadenectomy and received the initial diagnosis of high-grade mucoepidermoid carcinoma. Due to evidence of local recurrence at magnetic resonance imaging 1 month later, a subsequent right radical neck dissection was performed. The patient then sought a second opinion at our cancer center and finally received the correct diagnosis of NUT carcinoma. Given the well-known aggressive behavior of this neoplasm, as well as clinical and radiological features, she underwent adjuvant chemo-radiation (intensity-modulated radiotherapy + concurrent chemotherapy with cisplatin). Results: After a disease-free interval of 2.6 months, a widespread metastatic disease led to rapid deterioration of performance status and patient death in a few weeks after metastatic onset. Conclusions: We presented a case of NUTc arising from salivary gland aiming to improve the knowledge of this rare malignancy. First, we pointed out that in the setting of rare tumors like salivary gland cancers, the diagnosis should be obtained by expert pathologists, and patients should be referred to tertiary cancer centers for their clinical management. Second, molecular profiling may help to identify possible druggable targets that may be exploited to treat patients suffering from this aggressive malignancy. Sharing the molecular data provided in this case will be useful for further research
Use of apparent diffusion coefficient images to predict response to induction chemotherapy in sinonasal cancer
No full textThe purpose of this study is to identify a set of radiomic features extracted from apparent diffusion coefficient (ADC) maps, obtained using baseline diffusion weighted magnetic resonance imaging (DW-MRI), which are able to predict the outcome of induction chemotherapy (IC) in sinonasal cancers. Such prediction could help the clinician defining the better treatment for a particular patient. Eighty-eight radiomic features were extracted from the ADC maps of 15 patients that underwent IC. A preliminary filtering of the features was made by assessing their stability to geometrical transformations of the region of interest (ROI). Mann-Whitney tests corrected for control of false discoveries were performed to identify the features that could discriminate between responsive and nonresponsive patients (4 and 11 respectively). Twenty features were found to be able to discriminate the two groups and they can potentially be used for prediction of response to treatment
Head and neck cancers
No full textThis textbook combines essential information on clinical cancer medicine with a guide to the latest advances in molecular oncology and tumor biology. Providing a systematic overview of all types of solid tumors, including epidemiology and cancer prevention, genetic aspects of hereditary cancers, differential diagnosis, typical signs and symptoms, diagnostic strategies and staging, and treatment modalities, it also discusses new and innovative cancer treatments, particularly targeted therapy and immunotherapy. Expert commentaries at the end of each chapter highlight key points, offer insights, suggest further reading and discuss clinical application using case descriptions. This textbook is an invaluable, practice-oriented tool for medical students just beginning their clinical oncology studies, as well as for medical oncology residents and young professionals
Big Data in Head and Neck Cancer
No full textHead and neck cancers can be used as a paradigm for exploring “big data” applications in oncology. Computational strategies derived from big data science hold the promise of shedding new light on the molecular mechanisms driving head and neck cancer pathogenesis, identifying new prognostic and predictive factors, and discovering potential therapeutics against this highly complex disease. Big data strategies integrate robust data input, from radiomics, genomics, and clinical-epidemiological data to deeply describe head and neck cancer characteristics. Thus, big data may advance research generating new knowledge and improve head and neck cancer prognosis supporting clinical decision-making and development of treatment recommendations
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