Radiomics has been widely investigated for non-invasive acquisition of quantitative textural information from anatomic structures. While the vast majority of radiomic analysis is performed on images ...obtained from computed tomography, magnetic resonance imaging (MRI)-based radiomics has generated increased attention. In head and neck cancer (HNC), however, attempts to perform consistent investigations are sparse, and it is unclear whether the resulting textural features can be reproduced. To address this unmet need, we systematically reviewed the quality of existing MRI radiomics research in HNC.
Literature search was conducted in accordance with guidelines established by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Electronic databases were examined from January 1990 through November 2017 for common radiomic keywords. Eligible completed studies were then scored using a standardized checklist that we developed from Enhancing the Quality and Transparency of Health Research guidelines for reporting machine-learning predictive model specifications and results in biomedical research, defined by Luo et al. (1). Descriptive statistics of checklist scores were populated, and a subgroup analysis of methodology items alone was conducted in comparison to overall scores.
Sixteen completed studies and four ongoing trials were selected for inclusion. Of the completed studies, the nasopharynx was the most common site of study (37.5%). MRI modalities varied with only four of the completed studies (25%) extracting radiomic features from a single sequence. Study sample sizes ranged between 13 and 118 patients (median of 40), and final radiomic signatures ranged from 2 to 279 features. Analyzed endpoints included either segmentation or histopathological classification parameters (44%) or prognostic and predictive biomarkers (56%). Liu et al. (2) addressed the highest number of our checklist items (total score: 48), and a subgroup analysis of methodology checklist items alone did not demonstrate any difference in scoring trends between studies Spearman's ρ = 0.94 (
< 0.0001).
Although MRI radiomic applications demonstrate predictive potential in analyzing diverse HNC outcomes, methodological variances preclude accurate and collective interpretation of data.
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.
Objectives
Radiomic involves testing the associations of a large number of quantitative imaging features with clinical characteristics. Our aim was to extract a radiomic signature from axial ...T2-weighted (T2-W) magnetic resonance imaging (MRI) of the whole prostate able to predict oncological and radiological scores in prostate cancer (PCa).
Methods
This study included 65 patients with localized PCa treated with radiotherapy (RT) between 2014 and 2018. For each patient, the T2-W MRI images were normalized with the histogram intensity scale standardization method. Features were extracted with the IBEX software. The association of each radiomic feature with risk class, T-stage, Gleason score (GS), extracapsular extension (ECE) score, and Prostate Imaging Reporting and Data System (PI-RADS v2) score was assessed by univariate and multivariate analysis.
Results
Forty-nine out of 65 patients were eligible. Among the 1702 features extracted, 3 to 6 features with the highest predictive power were selected for each outcome. This analysis showed that texture features were the most predictive for GS, PI-RADS v2 score, and risk class; intensity features were highly associated with T-stage, ECE score, and risk class, with areas under the receiver operating characteristic curve (ROC AUC) ranging from 0.74 to 0.94.
Conclusions
MRI-based radiomics is a promising tool for prediction of PCa characteristics. Although a significant association was found between the selected features and all the mentioned clinical/radiological scores, further validations on larger cohorts are needed before these findings can be applied in the clinical practice.
Key Points
• A radiomic model was used to classify PCa aggressiveness.
• Radiomic analysis was performed on T2-W magnetic resonance images of the whole prostate gland.
• The most predictive features belong to the texture (57%) and intensity (43%) domains.
Paucity and low evidence-level data on proton therapy (PT) represent one of the main issues for the establishment of solid indications in the PT setting. Aim of the present registry, the POWER ...registry, is to provide a tool for systematic, prospective, harmonized, and multidimensional high-quality data collection to promote knowledge in the field of PT with a particular focus on the use of hypofractionation.
All patients with any type of oncologic disease (benign and malignant disease) eligible for PT at the European Institute of Oncology (IEO), Milan, Italy, will be included in the present registry. Three levels of data collection will be implemented: Level (1) clinical research (patients outcome and toxicity, quality of life, and cost/effectiveness analysis); Level (2) radiological and radiobiological research (radiomic and dosiomic analysis, as well as biological modeling); Level (3) biological and translational research (biological biomarkers and genomic data analysis). Endpoints and outcome measures of hypofractionation schedules will be evaluated in terms of either Treatment Efficacy (tumor response rate, time to progression/percentages of survivors/median survival, clinical, biological, and radiological biomarkers changes, identified as surrogate endpoints of cancer survival/response to treatment) and Toxicity. The study protocol has been approved by the IEO Ethical Committee (IEO 1885). Other than patients treated at IEO, additional PT facilities (equipped with Proteus®ONE or Proteus®PLUS technologies by IBA, Ion Beam Applications, Louvain-la-Neuve, Belgium) are planned to join the registry data collection. Moreover, the registry will be also fully integrated into international PT data collection networks.
Purpose
To critically review available literature on hypofractionated (≥ 3 Gy/fraction) proton therapy (PT) for breast cancer (BCa).
Methods
A systematic screening of the literature was performed in ...April 2021 in compliance with the preferred reporting items for systematic reviews and meta-analyses recommendations. All full-text publication written in English were considered eligible. Acute and late toxicities, oncological outcomes and dosimetric features were considered for the analysis.
Results
Twelve publications met the inclusion criteria; all studies but one focused on accelerated partial breast irradiation (APBI). Eleven works considered post-operative patients, one referred to ABPI as a curative-intent modality. The dosimetric profile of PT compared favorably with both photon-based 3D conformal and intensity-modulated techniques, while a more extended follow-up is warranted to fully assess both the long-term toxicities and the non-inferiority of oncological outcomes.
Conclusion
Our work shows that results on PT for BCa are currently only available for APBI applications, with dosimetric analyses demonstrating a clear advantage over both 3D conformal and intensity modulated X-rays techniques, especially when ≥ 2 treatment fields were used. However, further evidence is needed to define whether such theoretical benefit translates into clinical improvements, especially in the long-term.
Currently, main treatment strategies for early-stage non-small cell lung cancer (ES-NSCLC) disease are surgery or stereotactic body radiation therapy (SBRT), with successful local control rates for ...both approaches. However, regional and distant failure remain critical in SBRT, and it is paramount to identify predictive factors of response to identify high-risk patients who may benefit from more aggressive approaches. The main endpoint of the MONDRIAN trial is to identify multi-omic biomarkers of SBRT response integrating information from the individual fields of radiomics, genomics and proteomics.
MONDRIAN is a prospective observational explorative cohort clinical study, with a data-driven, bottom-up approach. It is expected to enroll 100 ES-NSCLC SBRT candidates treated at an Italian tertiary cancer center with well-recognized expertise in SBRT and thoracic surgery. To identify predictors specific to SBRT, MONDRIAN will include data from 200 patients treated with surgery, in a 1:2 ratio, with comparable clinical characteristics. The project will have an overall expected duration of 60 months, and will be structured into five main tasks: (i) Clinical Study; (ii) Imaging/ Radiomic Study, (iii) Gene Expression Study, (iv) Proteomic Study, (v) Integrative Model Building.
Thanks to its multi-disciplinary nature, MONDRIAN is expected to provide the opportunity to characterize ES-NSCLC from a multi-omic perspective, with a Radiation Oncology-oriented focus. Other than contributing to a mechanistic understanding of the disease, the study will assist the identification of high-risk patients in a largely unexplored clinical setting. Ultimately, this would orient further clinical research efforts on the combination of SBRT and systemic treatments, such as immunotherapy, with the perspective of improving oncological outcomes in this subset of patients.
The study was prospectively registered at clinicaltrials.gov (NCT05974475).
Management of patients with head and neck cancers (HNCs) is challenging for the Radiation Oncologist, especially in the COVID-19 era. The Italian Society of Radiotherapy and Clinical Oncology (AIRO) ...identified the need of practice recommendations on logistic issues, treatment delivery and healthcare personnel’s protection in a time of limited resources. A panel of 15 national experts on HNCs completed a modified Delphi process. A five-point Likert scale was used; the chosen cut-offs for strong agreement and agreement were 75% and 66%, respectively. Items were organized into two sections: (1) general recommendations (10 items) and (2) special recommendations (45 items), detailing a set of procedures to be applied to all specific phases of the Radiation Oncology workflow. The distribution of facilities across the country was as follows: 47% Northern, 33% Central and 20% Southern regions. There was agreement or strong agreement across the majority (93%) of proposed items including treatment strategies, use of personal protection devices, set-up modifications and follow-up re-scheduling. Guaranteeing treatment delivery for HNC patients is well-recognized in Radiation Oncology. Our recommendations provide a flexible tool for management both in the pandemic and post-pandemic phase of the COVID-19 outbreak.
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.
Introduction
Reirradiation (reRT) of locally recurrent/second primary tumors of the head and neck region is a potentially curative treatment for patients not candidate to salvage surgery. Aim of the ...present study is to summarize available literature on both prognostic factors and indications to curative reRT in this clinical setting.
Materials and methods
A narrative review of the literature was performed on two topics: (1) patients’ selection according to prognostic factors and (2) dosimetric feasibility of reRT. Postoperative reRT and palliative intent treatments were out of the scope of this work.
Results
Patient-tumor and treatment-related prognostic factors were analyzed, together with dosimetric parameters concerning target volume and organs at risk. Based on available evidence, a stepwise approach has been proposed aiming to provide a useful tool to identify suitable candidates for curative reRT in clinical practice. This was then applied to two clinical cases, proposed at the end of this work.
Conclusion
A second course of RT in head and neck recurrence/second primary tumors is a personalized approach that can be offered to selected patients only in centers with expertise and dedicated equipment following a multidisciplinary team discussion.
Purpose
To report the results involving post-operative interventional radiotherapy (POIRT) in a homogenous cohort of patients affected by keloid and treated at a single institution with the same ...fractionation schedule.
Patients and Methods
Inclusion criteria were: surgery with a histopathological diagnosis of keloid, subsequent high-dose rate interventional radiotherapy (HDR-IRT)—12 Gy in 4 fractions (3 Gy/fr) twice a day—and follow-up period ≥ 24 months.
Results
One-hundred and two patients and a total of 135 keloids were eligible for the analyses. Median follow-up was 64 IQR: 25–103 months. Thirty-six (26.7%) recurrences were observed, 12-months and 36-months cumulative incidence of recurrence were 20.7% (95% CI 12.2–28.5) and 23.8% (95% CI 14.9–31.7) respectively. History of spontaneous keloids (HR = 7.00, 95% CI 2.79–17.6,
p
< 0.001), spontaneous cheloid as keloid cause (HR = 6.97, 95% CI 2.05–23.7,
p
= 0.002) and sternal (HR = 10.6, 95% CI 3.08–36.8,
p
< 0.001), ear (HR = 6.03, 95% CI 1.71–21.3,
p
= 0.005) or limb (HR = 18.8, 95% CI 5.14–68.7,
p
< 0.001) keloid sites were significantly associated to a higher risk of recurrence.
Conclusions
The findings support the use of surgery and POIRT as an effective strategy for controlling keloid relapses. Further studies should focus on determining the optimal Biologically Effective Dose and on establishing a scoring system for patient selection.
PDF
