In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and ...machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians.
Periodontal disease (PD) is one of the most common oral conditions affecting both youths and adults. There are some research works suggesting a high incidence of PD in pregnant women. As an ...inflammatory disease of bacterial origin, PD may result in the activation of the pathways affecting the course and the pregnancy outcome. The authors, based on the literature review, try to answer the PICO question: Does maternal periodontitis (exposure) influence the incidence of complications rates in pregnancy and the development of systemic diseases in childhood and adult offspring (outcome) in the humans of any race (population) compared to the offspring of mothers with healthy periodontium (comparison)? The authors try to describe the molecular pathways and mechanisms of these interdependencies. There is some evidence that maternal periodontitis may affect the pregnancy course and outcome, resulting in preeclampsia, preterm delivery, vulvovaginitis and low birth weight. It can be suggested that maternal periodontitis may affect offspring epigenome and result in some health consequences in their adult life.
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Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive primary malignancy of the pancreas, with a dismal prognosis and limited treatment options. It possesses a unique tumor microenvironment ...(TME), generating dense stroma with complex elements cross-talking with each other to promote tumor growth and progression. Diversified neural components makes for not having a full understanding of their influence on its aggressive behavior. The aim of the study was to summarize and integrate the role of nerves in the pancreatic tumor microenvironment. The role of autonomic nerve fibers on PDAC development has been recently studied, which resulted in considering the targeting of sympathetic and parasympathetic pathways as a novel treatment opportunity. Perineural invasion (PNI) is commonly found in PDAC. As the severity of the PNI correlates with a poorer prognosis, new quantification of this phenomenon, distinguishing between perineural and endoneural invasion, could feature in routine pathological examination. The concepts of cancer-related neurogenesis and axonogenesis in PDAC are understudied; so, further research in this field may be warranted. A better understanding of the interdependence between the neural component and cancer cells in the PDAC microenvironment could bring new nerve-oriented treatment options into clinical practice and improve outcomes in patients with pancreatic cancer. In this review, we aim to summarize and integrate the current state of knowledge and future challenges concerning nerve–cancer interactions in PDAC.
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6.
Recent advances in radiation oncology Garibaldi, Cristina; Jereczek-Fossa, Barbara Alicja; Marvaso, Giulia ...
Ecancermedicalscience,
11/2017, Volume:
11
Journal Article
Peer reviewed
Open access
Radiotherapy (RT) is very much a technology-driven treatment modality in the management of cancer. RT techniques have changed significantly over the past few decades, thanks to improvements in ...engineering and computing. We aim to highlight the recent developments in radiation oncology, focusing on the technological and biological advances. We will present state-of-the-art treatment techniques, employing photon beams, such as intensity-modulated RT, volumetric-modulated arc therapy, stereotactic body RT and adaptive RT, which make possible a highly tailored dose distribution with maximum normal tissue sparing. We will analyse all the steps involved in the treatment: imaging, delineation of the tumour and organs at risk, treatment planning and finally image-guidance for accurate tumour localisation before and during treatment delivery. Particular attention will be given to the crucial role that imaging plays throughout the entire process. In the case of adaptive RT, the precise identification of target volumes as well as the monitoring of tumour response/modification during the course of treatment is mainly based on multimodality imaging that integrates morphological, functional and metabolic information. Moreover, real-time imaging of the tumour is essential in breathing adaptive techniques to compensate for tumour motion due to respiration. Brief reference will be made to the recent spread of particle beam therapy, in particular to the use of protons, but also to the yet limited experience of using heavy particles such as carbon ions. Finally, we will analyse the latest biological advances in tumour targeting. Indeed, the effectiveness of RT has been improved not only by technological developments but also through the integration of radiobiological knowledge to produce more efficient and personalised treatment strategies.
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Surgical removal of impacted mandibular third molars constitutes one of the most frequently performed procedures within oral surgery. This surgery procedure is associated with many post-operative ...complications. Advanced platelet-rich fibrin (A-PRF) belongs to the second generation of platelet concentrates and is rich in numerous growth factors. The aim of this study was to assess the influence of A-PRF on selected clinical features following the surgical removal of impacted mandibular third molars. The research was conducted on 100 generally healthy patients, who underwent a lower third molar odontectomy in Department of Oral Surgery, Medical University of Gdańsk, Poland, between 2018 and 2019. The research group consisted of 50 patients (immediate A-PRF socket filling) and control group (50 patients without A-PRF socket filling). During the study, the following clinical features were assessed: pain (visual analog scale), analgesics intake, the presence of trismus, edema, hematomas within the surrounding tissues (e.g., cheek), prevalence of pyrexia, dry socket, secondary bleeding, presence of hematomas, skin warmth in the post-operative area, and bleeding time observed by the patient were analyzed on the 3rd, 7th, and 14th day after the procedure. There was a significant association between A-PRF socket filling and pain intensity, the analgesics intake, trismus, and edema on the 3rd and the 7th day (
< 0.05). The presence of hematomas and skin warmth on the 3rd day after the surgery (
< 0.05) were also statistically associated with A-PRF use. The study showed that in reducing the incidence of postoperative complications, A-PRF was more important than the position of the tooth or the duration of the procedure. The growth factors in A-PRF reduce postoperative complications, such as pain, trismus, edema, analgesics intake, presence of hematomas, and skin warmth, after mandibular wisdom teeth odontectomy.
Cancer is the second leading cause of death globally, exceeded only by cardiovascular disease. Despite the introduction of several survival-prolonging treatment modalities, including targeted therapy ...and immunotherapy, the overall prognosis for the metastatic disease remains challenging. Therefore, the identification of new molecular biomarkers and therapeutic targets related to cancer diagnosis and prognosis is of paramount importance. DNA polymerase delta 1 (POLD1), a catalytic and proofreading subunit of the DNA polymerase δ complex, performs a crucial role in DNA replication and repair processes. Recently, germline and somatic mutations of the
gene have been acknowledged in several malignancies. Moreover, diversified
expression profiles have been reported in association with clinicopathological features in a variety of tumor types. With this review, we aim to summarize the current knowledge on the role of POLD1 in cancers. In addition, we discuss the future prospects and clinical applications of the assessment of
mutation and expression patterns in tumors.
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ZNF-281 is a zinc finger factor which can lead to cancer progression and metastasis. Its up-regulation reported in many cancers was correlated with metastasis and worsened patients’ prognosis. This ...is the first study describing ZNF-281 in the context of OSCC. Oral tissue samples drawn from 66 OSCC patients and 36 control patients were collected to determine protein (using immunochemistry and the semi-quantitative H-score method) and mRNA expression levels (using the RT-qPCR reaction). Our aim was to assess the ZNF-281 expression level in OSCC and the control group. Moreover, we determined the impact of ZNF-281 on survival parameters and the association of diversified clinical parameters with ZNF-281 expression. Clinical factors such as grade, AJCC stage and radiotherapy have an impact on the ZNF-281 H-score level, whereas AJCC stage and grade have an influence on ZNF-281 mRNA expression. Our survival analysis indicated that the impact on overall survival is not statistically significant, and the prognostic potential of ZNF-281 is rather limited. Our findings show that both levels of the ZNF-281 H-score and mRNA are decreased in OSCC in comparison to normal tissue. Moreover, we estimated that the H-score can differentiate normal tissue from OSCC with a sensitivity of 97% and specificity of 93.7%.
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Machine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction ...of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT).
Electronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1.
Forty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation).
The range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young. Overall, if not safe yet, ML is most probably a bet worth making.
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