Heat Modulation of Intrinsic MR Contrasts for Tumor Characterization

(1) Background: The longitudinal relaxation time (T1), transverse relaxation time (T2), water proton chemical shift (CS), and apparent diffusion coefficient (ADC) are MR quantities that change with temperature. In this work, we investigate heat-induced intrinsic MR contrast types to add salient information to conventional MR imaging to improve tumor characterization. (2) Methods: Imaging tests were performed in vivo using different rat tumor models. The rats were cooled/heated to steady-state temperatures from 26–36 °C and quantitative measurements of T1, T2, and ADC were obtained. Temperature maps were measured using the proton resonance frequency shift (PRFS) method during the heating and cooling cycles. (3) Results: All tissue samples show repeatable relaxation parameter measurement over a range of 26–36 °C. Most notably, we observed a more than 3.3% change in T1/°C in breast adenocarcinoma tumors compared to a 1% change in benign breast fibroadenoma lesions. In addition, we note distinct values of T2/°C change for rat prostate carcinoma cells compared to benign tissue. (4) Conclusion: These findings suggest the possibility of improving MR imaging visualization and characterization of tissue with heat-induced contrast types. Specifically, these results suggest that the temporal thermal responses of heat-sensitive MR imaging contrast mechanisms in different tissue types contain information for improved (i) characterization of tumor/tissue boundaries for diagnostic and therapy purposes, and (ii) characterization of salient behavior of tissues, e.g., malignant versus benign tumors.

Anorectal and Genital Mucosal Melanoma: Diagnostic Challenges, Current Knowledge and Therapeutic Opportunities of Rare Melanomas

Mucosal melanomas (MM) are rare tumors, being less than 2% of all diagnosed melanomas, comprising a variegated group of malignancies arising from melanocytes in virtually all mucosal epithelia, even if more frequently found in oral and sino-nasal cavities, ano-rectum and female genitalia (vulva and vagina). To date, there is no consensus about the optimal management strategy of MM. Furthermore, the clinical rationale of molecular tumor characterization regarding BRAF, KIT or NRAS, as well as the therapeutic value of immunotherapy, chemotherapy and targeted therapy, has not yet been deeply explored and clearly established in MM. In this overview, focused on anorectal and genital MM as models of rare melanomas deserving of a multidisciplinary approach, we highlight the need of referring these patients to centers with experts in melanoma, anorectal and uro-genital cancers treatments. Taking into account the rarity, the poor outcomes and the lack of effective treatment options for MM, tailored research needs to be promptly promoted.

Human Papillomavirus-Related Multiphenotypic Sinonasal Carcinoma—An Even Broader Tumor Entity?

Human papillomavirus (HPV)-related multiphenotypic sinonasal carcinoma (HMSC) is a recently defined tumor subtype with apparent favorable clinical outcome despite aggressive histomorphology. However, in recent years, additional numbers of cases, with more variable features and at locations outside the sinonasal region, have complicated the definition of HMSC. Here, we have performed a systematic review of all cases described so far in order to accumulate more knowledge. We identified 127 articles published between 2013 and 2021, of which 21 presented unique cases. In total, 79 unique patient cases were identified and their clinical and micromorphological nature are herein summarized. In our opinion, better clinical follow-up data and a more detailed tumor characterization are preferably needed before HMSC can finally be justified as its own tumor entity.

The Evolving Role of Radiotherapy for Pediatric Cancers With Advancements in Molecular Tumor Characterization and Targeted Therapies

Ongoing rapid advances in molecular diagnostics, precision imaging, and development of targeted therapies have resulted in a constantly evolving landscape for treatment of pediatric cancers. Radiotherapy remains a critical element of the therapeutic toolbox, and its role in the era of precision medicine continues to adapt and undergo re-evaluation. Here, we review emerging strategies for combining radiotherapy with novel targeted systemic therapies (for example, for pediatric gliomas or soft tissue sarcomas), modifying use or intensity of radiotherapy when appropriate via molecular diagnostics that allow better characterization and individualization of each patient’s treatments (for example, de-intensification of radiotherapy in WNT subgroup medulloblastoma), as well as exploring more effective targeted systemic therapies that may allow omission or delay of radiotherapy. Many of these strategies are still under investigation but highlight the importance of continued pre-clinical and clinical studies evaluating the role of radiotherapy in this era of precision oncology.

The Role of CT-Based Radiomics in Precise Imaging of Renal Cancer

The application of advanced computational analysis to medical imaging opens a plethora of opportunities in the field of radiology, allowing for more accurate tissue characterization and, eventually, advancing towards precision medicine through imaging biomarkers. In this review, we briefly introduce the methodology for radiomics analysis and the main challenges for implementation of radiomics-based tools in clinical practice. Based on systematic review of published studies, we also summarize here the main advances regarding CT-based radiomics applications in renal cancer with regards to tumor characterization (diagnosis, grading, prognosis), gene expression prediction (radiogenomics) and response evaluation.

Lung and Tumor Characterization in the Machine Learning Era

Danger characterization of tumors from radiology image container to be much precise and quicker with computer aided diagnosis (CAD) implements. Tumor portrayal via such devices can likewise empower non-intrusive prognosis, and foster personalized, and treatment arranging as a piece of accuracy medication. In this study , in cooperation machine learning algorithm strategies to better tumor characterization. Our methodological analysis depends on directed erudition for which we exhibit critical increases with machine learning algorithm, particularly by exploitation a 3D Convolutional Neural Network and Transfer Learning. Disturbed by the radiologists' understandings of the outputs, we at that point tell the best way to fuse task subordinate feature representations into a CAD framework by means of a diagram regularized inadequate MultiTask Learning (MTL) system with the help of feature fusion.

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Deep Learning is a growing field of artificial intelligence that has become an operative research topic in a wide range of disciplines. Today we are witnessing the tangible successes of Deep Learning in our daily lives in various applications, including education, manufacturing, transportation, healthcare, military, and automotive, etc.<strong> </strong>Deep Learning is a subfield of Machine Learning that stems from Artificial Neural Networks, where a cascade of layers is employed to progressively extract higher-level features from the raw input and make predictive guesses about new data. This paper will discuss the effect of attribute extraction profoundly inherent in training<strong> </strong>approaches such as Convolutional Neural Networks (CNN). Furthermore, the paper aims to offer a study on Deep Learning techniques and attribute extraction methods that have appeared in the last few years. As the demand increases, considerable research in the attribute extraction assignment has become even more instrumental. Brain tumor characterization and detection will be used as a case study to demonstrate Deep Learning CNN's ability to achieve effective representational learning and tumor characterization.

Radiomics- Quantitative Biomarker Analysis for Breast Cancer Diagnosis and Prediction: A Review

Background: Cancer of the breast has become a global problem for women's health. Though concerns regarding early detection and accurate diagnosis were raised, an effort is required for precision medicine as well as personalized treatment. In the past years, the area of medicinal imaging has seen an unprecedented growth that leads to an advancement of radiomics, which provides countless quantitative biomarkers extracted from modern diagnostic images, including a detailed tumor characterization of breast malignancy. Discussion: In this research, we presented the methodology and implementation of radiomics, together with its future trends and challenges by the basis of published papers. Radiomics could distinguish between malignant from benign tumors, predict prognostic factors, molecular subtypes of breast carcinoma, treatment response to neoadjuvant chemotherapy (NAC), and recurrence survival. The incorporation of quantitative knowledge with clinical, histopathological and genomic information will enable physicians to afford customized care of treatment for patients with breast cancer. Conclusion: Our research was intended to help physicians and radiologists learn fundamental knowledge about radiomics and also to work collaboratively with researchers to explore evidence for further usage in clinical practice.

From Neurosurgical Planning to Histopathological Brain Tumor Characterization: Potentialities of Arcuate Fasciculus Along-Tract Diffusion Tensor Imaging Tractography Measures

Background: Tractography has been widely adopted to improve brain gliomas' surgical planning and guide their resection. This study aimed to evaluate state-of-the-art of arcuate fasciculus (AF) tractography for surgical planning and explore the role of along-tract analyses in vivo for characterizing tumor histopathology.Methods: High angular resolution diffusion imaging (HARDI) images were acquired for nine patients with tumors located in or near language areas (age: 41 ± 14 years, mean ± standard deviation; five males) and 32 healthy volunteers (age: 39 ± 16 years; 16 males). Phonemic fluency task fMRI was acquired preoperatively for patients. AF tractography was performed using constrained spherical deconvolution diffusivity modeling and probabilistic fiber tracking. Along-tract analyses were performed, dividing the AF into 15 segments along the length of the tract defined using the Laplacian operator. For each AF segment, diffusion tensor imaging (DTI) measures were compared with those obtained in healthy controls (HCs). The hemispheric laterality index (LI) was calculated from language task fMRI activations in the frontal, parietal, and temporal lobe parcellations. Tumors were grouped into low/high grade (LG/HG).Results: Four tumors were LG gliomas (one dysembryoplastic neuroepithelial tumor and three glioma grade II) and five HG gliomas (two grade III and three grade IV). For LG tumors, gross total removal was achieved in all but one case, for HG in two patients. Tractography identified the AF trajectory in all cases. Four along-tract DTI measures potentially discriminated LG and HG tumor patients (false discovery rate &lt; 0.1): the number of abnormal MD and RD segments, median AD, and MD measures. Both a higher number of abnormal AF segments and a higher AD and MD measures were associated with HG tumor patients. Moreover, correlations (unadjusted p &lt; 0.05) were found between the parietal lobe LI and the DTI measures, which discriminated between LG and HG tumor patients. In particular, a more rightward parietal lobe activation (LI &lt; 0) correlated with a higher number of abnormal MD segments (R = −0.732) and RD segments (R = −0.724).Conclusions: AF tractography allows to detect the course of the tract, favoring the safer-as-possible tumor resection. Our preliminary study shows that along-tract DTI metrics can provide useful information for differentiating LG and HG tumors during pre-surgical tumor characterization.