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.

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

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies