Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer

Simple Summary Colorectal cancer is the second most malignant tumor per number of deaths after lung cancer and the third per number of new cases after breast and lung cancer. The correct and rapid identification (i.e., segmentation of the cancer regions) is a fundamental task for correct patient diagnosis. In this study, we propose a novel automated pipeline for the segmentation of MRI scans of patients with LARC in order to predict the response to nCRT using radiomic features. This study involved the retrospective analysis of T-2-weighted MRI scans of 43 patients affected by LARC. The segmentation of tumor areas was on par or better than the state-of-the-art results, but required smaller sample sizes. The analysis of radiomic features allowed us to predict the TRG score, which agreed with the state-of-the-art results. Background: Rectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of the rectal tract. Predicting the pathologic response of neoadjuvant chemoradiotherapy at an MRI primary staging scan in patients affected by locally advanced rectal cancer (LARC) could lead to significant improvement in the survival and quality of life of the patients. In this study, the possibility of automatizing this estimation from a primary staging MRI scan, using a fully automated artificial intelligence-based model for the segmentation and consequent characterization of the tumor areas using radiomic features was evaluated. The TRG score was used to evaluate the clinical outcome. Methods: Forty-three patients under treatment in the IRCCS Sant'Orsola-Malpighi Polyclinic were retrospectively selected for the study; a U-Net model was trained for the automated segmentation of the tumor areas; the radiomic features were collected and used to predict the tumor regression grade (TRG) score. Results: The segmentation of tumor areas outperformed the state-of-the-art results in terms of the Dice score coefficient or was comparable to them but with the advantage of considering mucinous cases. Analysis of the radiomic features extracted from the lesion areas allowed us to predict the TRG score, with the results agreeing with the state-of-the-art results. Conclusions: The results obtained regarding TRG prediction using the proposed fully automated pipeline prove its possible usage as a viable decision support system for radiologists in clinical practice

Adherence issues related to sublingual immunotherapy as perceived by allergists

Objectives: Sublingual immunotherapy (SLIT) is a viable alternative to subcutaneous immunotherapy to treat allergic rhinitis and asthma, and is widely used in clinical practice in many European countries. The clinical efficacy of SLIT has been established in a number of clinical trials and meta-analyses. However, because SLIT is self-administered by patients without medical supervision, the degree of patient adherence with treatment is still a concern. The objective of this study was to evaluate the perception by allergists of issues related to SLIT adherence. Methods: We performed a questionnaire-based survey of 296 Italian allergists, based on the adherence issues known from previous studies. The perception of importance of each item was assessed by a VAS scale ranging from 0 to 10. Results: Patient perception of clinical efficacy was considered the most important factor (ranked 1 by 54% of allergists), followed by the possibility of reimbursement (ranked 1 by 34%), and by the absence of side effects (ranked 1 by 21%). Patient education, regular follow-up, and ease of use of SLIT were ranked first by less than 20% of allergists. Conclusion: These findings indicate that clinical efficacy, cost, and side effects are perceived as the major issues influencing patient adherence to SLIT, and that further improvement of adherence is likely to be achieved by improving the patient information provided by prescribers. © 2010 Scurati et al, publisher and licensee Dove Medical Press Ltd

Mutational burden of resectable pancreatic cancer, as determined by whole transcriptome and whole exome sequencing, predicts a poor prognosis

Despite the genomic characterization of pancreatic cancer (PC), marked advances in the development of prognosis classification and novel therapeutic strategies have yet to come. The present study aimed to better understand the genomic alterations associated with the invasive phenotype of PC, in order to improve patient selection for treatment options. A total of 30 PC samples were analysed by either whole transcriptome (9 samples) or exome sequencing (21 samples) on an Illumina platform (75X2 or 100X2 bp), and the results were matched with normal DNA to identify somatic events. Single nucleotide variants and insertions and deletions were annotated using public databases, and the pathogenicity of the identified variants was defined according to prior knowledge and mutation-prediction tools. A total of 43 recurrently altered genes were identified, which were involved in numerous pathways, including chromatin remodelling and DNA damage repair. In addition, an analysis limited to a subgroup of early stage patients (50% of samples) demonstrated that poor prognosis was significantly associated with a higher number of known PC mutations (P=0.047). Samples from patients with a better overall survival (>25 months) harboured an average of 24 events, whereas samples from patients with an overall survival of <25 months presented an average of 40 mutations. These findings indicated that a complex genetic profile in the early stage of disease may be associated with increased aggressiveness, thus suggesting an urgent requirement for an innovative approach to classify this disease

Mutational burden of resectable pancreatic cancer, as determined by whole transcriptome and whole exome sequencing, predicts a poor prognosis.

Despite the genomic characterization of pancreatic cancer\ua0(PC), marked advances in the development of prognosis classification and novel therapeutic strategies have yet to come. The present study aimed to better understand the genomic alterations associated with the invasive phenotype of PC, in order to improve patient selection for treatment options. A total of 30\ua0PC samples were analysed by either whole transcriptome (9\ua0samples) or exome sequencing (21\ua0samples) on an Illumina platform (75X2 or 100X2\ua0bp), and the results were matched with normal DNA to identify somatic events. Single nucleotide variants and insertions and deletions were annotated using public databases, and the pathogenicity of the identified variants was defined according to prior knowledge and mutation-prediction tools. A total of 43\ua0recurrently altered genes were identified, which were involved in numerous pathways, including chromatin remodelling and DNA damage repair. In addition, an analysis limited to a subgroup of early stage patients (50% of samples) demonstrated that poor prognosis was significantly associated with a higher number of known PC mutations (P=0.047). Samples from patients with a better overall survival (>25\ua0months) harboured an average of 24\ua0events, whereas samples from patients with an overall survival of <25\ua0months presented an average of 40\ua0mutations. These findings indicated that a complex genetic profile in the early stage of disease may be associated with increased aggressiveness, thus suggesting an urgent requirement for an innovative approach to classify this disease