Stereotactic body radiotherapy for moderately central and ultra-central oligometastatic disease: initial outcomes

Background: Delivery of SBRT to central thoracic tumours within 2 cm of the proximal bronchial tree (PBT), and especially ultra-central tumours which directly abut the PBT, has been controversial due to concerns about high risk of toxicity and treatment-related death when delivering high doses close to critical mediastinal structures. We present dosimetric and clinical outcomes from a group of oligometastatic patients treated with a risk-adapted SBRT approach. Methods: Between September 2015 and October 2018, 27 patients with 28 central thoracic oligometastases (6 moderately central, 22 ultra-central) were treated with 60 Gy in 8 fractions under online CBCT guidance. PTV dose was compromised where necessary to meet mandatory OAR constraints. Patients were followed up for toxicity and disease status. Results: Mandatory OAR constraints were met in all cases; this required PTV coverage compromise in 23 cases, with V100% reduced to <70% in 11 cases. No acute or late toxicities of Grade ≥ 3 were reported. One and 2 year in-field control rates were 95.2% and 85.7% respectively, progression-free survival rates were 42.8% and 23.4% respectively, and overall survival rates were 82.7% and 69.5% respectively. No significant differences were seen in control or survival rates by extent of PTV underdosage or between moderately and ultra-central cases. Conclusion: It appears that compromising PTV coverage to meet OAR constraints allows safe and effective delivery of SBRT to moderately and ultra-central tumours, with low toxicity rates and high in-field control rates. This treatment can be delivered on standard linear accelerators with widely available imaging technology

Comparison of investigator-delineated gross tumour volumes and quality assurance in pancreatic cancer: analysis of the on-trial cases for the SCALOP trial

Background and purpose We performed a retrospective central review of tumour outlines in patients undergoing radiotherapy in the SCALOP trial. Materials and methods The planning CT scans were reviewed retrospectively by a central review team, and the accuracy of investigators’ GTV (iGTV) and PTV (iPTV) was compared to the trials team-defined gold standard (gsGTV and gsPTV) using the Jaccard Conformity Index (JCI) and Geographical Miss Index (GMI). The prognostic value of JCI and GMI was also assessed. The RT plans were also reviewed against protocol-defined constraints. Results 60 patients with diagnostic-quality planning scans were included. The median whole volume JCI for GTV was 0.64 (IQR: 0.43–0.82), and the median GMI was 0.11 (IQR: 0.05–0.22). For PTVs, the median JCI and GMI were 0.80 (IQR: 0.71–0.88) and 0.04 (IQR: 0.02–0.12) respectively. Tumour was completely missed in 1 patient, and ⩾ 50% of the tumour was missed in 3. Patients with JCI for GTV ⩾ 0.7 had 7.12 (95% CIs: 1.83–27.67, p = 0.005) higher odds of progressing by 9 months in multivariate analysis. Major deviations in RT planning were noted in 4.5% of cases. Conclusions Radiotherapy workshops and real-time central review of contours are required in RT trials of pancreatic cancer

Beauty is in the AI of the beholder: Are we ready for the clinical integration of Artificial Intelligence in radiography? An exploratory analysis of perceived AI knowledge, skills, confidence, and education perspectives of UK radiographers

The use of artificial intelligence (AI) in medical imaging and radiotherapy has been met with both scepticism and excitement. However, clinical integration of AI is already well-underway. Many authors have recently reported on the AI knowledge and perceptions of radiologists/medical staff and students however there is a paucity of information regarding radiographers. Published literature agrees that AI is likely to have significant impact on radiology practice. As radiographers are at the forefront of radiology service delivery, an awareness of the current level of their perceived knowledge, skills, and confidence in AI is essential to identify any educational needs necessary for successful adoption into practice. The aim of this survey was to determine the perceived knowledge, skills, and confidence in AI amongst UK radiographers and highlight priorities for educational provisions to support a digital healthcare ecosystem. A survey was created on Qualtrics® and promoted via social media (Twitter®/LinkedIn®). This survey was open to all UK radiographers, including students and retired radiographers. Participants were recruited by convenience, snowball sampling. Demographic information was gathered as well as data on the perceived, self-reported, knowledge, skills, and confidence in AI of respondents. Insight into what the participants understand by the term "AI" was gained by means of a free text response. Quantitative analysis was performed using SPSS® and qualitative thematic analysis was performed on NVivo®. Four hundred and eleven responses were collected (80% from diagnostic radiography and 20% from a radiotherapy background), broadly representative of the workforce distribution in the UK. Although many respondents stated that they understood the concept of AI in general (78.7% for diagnostic and 52.1% for therapeutic radiography respondents, respectively) there was a notable lack of sufficient knowledge of AI principles, understanding of AI terminology, skills, and confidence in the use of AI technology. Many participants, 57% of diagnostic and 49% radiotherapy respondents, do not feel adequately trained to implement AI in the clinical setting. Furthermore 52% and 64%, respectively, said they have not developed any skill in AI whilst 62% and 55%, respectively, stated that there is not enough AI training for radiographers. The majority of the respondents indicate that there is an urgent need for further education (77.4% of diagnostic and 73.9% of therapeutic radiographers feeling they have not had adequate training in AI), with many respondents stating that they had to educate themselves to gain some basic AI skills. Notable correlations between confidence in working with AI and gender, age, and highest qualification were reported. Knowledge of AI terminology, principles, and applications by healthcare practitioners is necessary for adoption and integration of AI applications. The results of this survey highlight the perceived lack of knowledge, skills, and confidence for radiographers in applying AI solutions but also underline the need for formalised education on AI to prepare the current and prospective workforce for the upcoming clinical integration of AI in healthcare, to safely and efficiently navigate a digital future. Focus should be given on different needs of learners depending on age, gender, and highest qualification to ensure optimal integration. [Abstract copyright: Copyright © 2021 Rainey, O'Regan, Matthew, Skelton, Woznitza, Chu, Goodman, McConnell, Hughes, Bond, McFadden and Malamateniou.

The impact of AI on radiographic image reporting – perspectives of the UK reporting radiographer population

Background: It is predicted that medical imaging services will be greatly impacted by AI in the future. Developments in computer vision have allowed AI to be used for assisted reporting. Studies have investigated radiologists' opinions of AI for image interpretation (Huisman et al., 2019 a/b) but there remains a paucity of information in reporting radiographers' opinions on this topic.Method: A survey was developed by AI expert radiographers and promoted via LinkedIn/Twitter and professional networks for radiographers from all specialities in the UK. A sub analysis was performed for reporting radiographers only.Results: 411 responses were gathered to the full survey (Rainey et al., 2021) with 86 responses from reporting radiographers included in the data analysis. 10.5% of respondents were using AI tools? as part of their reporting role. 59.3% and 57% would not be confident in explaining an AI decision to other healthcare practitioners and 'patients and carers' respectively. 57% felt that an affirmation from AI would increase confidence in their diagnosis. Only 3.5% would not seek second opinion following disagreement from AI. A moderate level of trust in AI was reported: mean score = 5.28 (0 = no trust; 10 = absolute trust). 'Overall performance/accuracy of the system', 'visual explanation (heatmap/ROI)', 'Indication of the confidence of the system in its diagnosis' were suggested as measures to increase trust.Conclusion: AI may impact reporting professionals' confidence in their diagnoses. Respondents are not confident in explaining an AI decision to key stakeholders. UK radiographers do not yet fully trust AI. Improvements are suggested

Oxygen-enhanced MRI and radiotherapy in patients with oropharyngeal squamous cell carcinoma

Background and purpose: This study aimed to assess the role of T1 mapping and oxygen-enhanced MRI in patients undergoing radical dose radiotherapy for HPV positive oropharyngeal cancer, which has not yet been examined in an OE-MRI study. Materials and methods: Variable Flip Angle T1 maps were acquired on a 3T MRI scanner while patients (n = 12) breathed air and/or 100 % oxygen, before and after fraction 10 of the planned 30 fractions of chemoradiotherapy (‘visit 1’ and ‘visit 2’, respectively). The analysis aimed to assess to what extent (1) native R1 relates to patient outcome; (2) OE-MRI response relates to patient outcome; (3) changes in mean R1 before and after radiotherapy related to clinical outcome in patients with oropharyngeal squamous cell carcinoma. Results: Due to the radiotherapy being largely successful, the sample sizes of non-responder groups were small, and therefore it was not possible to properly assess the predictive nature of OE-MRI. The tumour R1 increased in some patients while decreasing in others, in a pattern that was overall consistent with the underlying OE-MRI theory and previously reported tumour OE-MRI responses. In addition, we discuss some practical challenges faced when integrating this technique into a clinical trial, with the aim that sharing this is helpful to researchers planning to use OE-MRI in future clinical studies. Conclusion: Altogether, these results suggest that further clinical OE-MRI studies to assess hypoxia and radiotherapy response are worth pursuing, and that there is important work to be done to improve the robustness of the OE-MRI technique in human applications in order for it to be useful as a widespread clinical technique

Clinical Trial of Oral Nelfinavir before and during Radiation Therapy for Advanced Rectal Cancer

Purpose Nelfinavir, a PI3-kinase pathway inhibitor, is a radiosensitizer which increases tumor blood flow in preclinical models. We conducted an early-phase study to demonstrate the safety of nelfinavir combined with hypofractionated radiotherapy (RT) and to develop biomarkers of tumor perfusion and radiosensitization for this combinatorial approach. Patients and Methods Ten patients with T3-4 N0-2 M1 rectal cancer received 7 days of oral nelfinavir (1250 mg bd) and a further 7 days of nelfinavir during pelvic RT (25 Gy/5 fractions/7 days). Perfusion CT (p-CT) and DCE-MRI scans were performed pre-treatment, after 7 days of nelfinavir and prior to last fraction of RT. Biopsies taken pre-treatment and 7 days after the last fraction of RT were analysed for tumor cell density (TCD). Results There were 3 drug-related grade 3 adverse events: diarrhea, rash, lymphopenia. On DCE-MRI, there was a mean 42% increase in median Ktrans, and a corresponding median 30% increase in mean blood flow on p-CT during RT in combination with nelfinavir. Median TCD decreased from 24.3% at baseline to 9.2% in biopsies taken 7 days after RT (P=0.01). Overall, 5/9 evaluable patients exhibited good tumor regression on MRI assessed by Tumor Regression Grade (mrTRG). Conclusions This is the first study to evaluate nelfinavir in combination with RT without concurrent chemotherapy. It has shown that nelfinavir-RT is well tolerated and is associated with increased blood flow to rectal tumors. The efficacy of nelfinavir-RT versus RT alone merits clinical evaluation, including measurement of tumor blood flow

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin