Carcinoma of the bronchus is the most common malignancy in the Western world. It is also the leading cause of cancer-related death accounting for 32% of all cancer deaths in males and 25% in females [1]. In the USA it causes more deaths than cancers of the colon, breast and prostate combined [2]. Disappointingly, in a recent UK survey of improvements in cancer survival [3], carcinoma of the bronchus showed the smallest percentage reduction in the number of deaths avoided between 1981-1990 (0.2%). This compares badly with breast (11% reduction) and melanoma (32%). The overall 5-yr survival for lung cancer diagnosed between 1986-1990 was only 5.3% (against 66% for breast and 76% for melanoma). It is on this background that the radiologist remains actively employed in the detection, diagnosis, staging and review of this common malignancy.

CT helps to distinguish diseases that cause airflow obstruction. Thin-section CT is particularly accurate in the identification of obliterative bronchiolitis.

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Our objective was to characterise the CT features of the various species of non-tuberculous mycobacteria (NTM) and to identify differences, if any, between Mycobacterium avium intracellulare( MAI) and other species. Fifty-five patients, who were culture positive on at least two occasions for a single NTM species, were evaluated. All patients had CT scans performed within 6 months of NTM identification. The CT scans were assessed for the presence and severity of bronchiectasis, nodules, cavities, tree-in-bud pattern, consolidation and for evidence of pre-existing lung disease. Bronchiectasis was identified in most patients (52 of 55, 95%) and nodules were present in approximately half (29 of 55, 53%). Patients with MAI ( n=16) were found to have significantly higher bronchiectasis scores and higher prevalence of nodules than the other species (both p<0.01). Patients with M.kansasii ( n=9) and M.xenopi ( n=9) had cavities, tree-in-bud pattern, and pre-existing emphysema as the dominant CT features. Patients with M.chelonae and M.fortuitum were younger than the other groups and also had a high incidence of pre-existing lung disease. Patients with MAI infection have more severe bronchiectasis and more nodules on CT than the other NTM species. Morphological differences between the other species were identified but were less distinct.

Evacuation proctography has a substantial diagnostic and therapeutic effect and is of considerable benefit to referring clinicians.

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Objective To evaluate the accuracy of magnetic resonance imaging in assessment of adolescent patients with complex Mu Èllerian anomalies and its contribution towards operative management. Design A retrospective review of magnetic resonance imaging and operative ®ndings.Setting A London teaching hospital that is a tertiary referral centre for complex reproductive tract disorders.Sample All adolescents referred for assessment of complex Mu Èllerian anomalies, from 1996 to 1999, and undergoing both magnetic resonance imaging and surgical assessment. Method In the nine suitable patients magnetic resonance imaging and surgical ®ndings were compared and the role of magnetic resonance imaging in determining the route and type of surgery was evaluated. Main outcome measures Magnetic resonance imaging data on reproductive tract anatomy and surgical ®ndings detailing reproductive tract anatomy. Results There was good correlation of magnetic resonance imaging and operative ®ndings in all cases. The best correlation was with uterine structure. In four cases the magnetic resonance imaging ®ndings were essential for the appropriate choice of the surgical approach and type of procedure. Conclusions Magnetic resonance imaging is a valuable tool in the management of this particular complex group of patients.

Objectives: Small bowel obstruction is a common surgical emergency which can lead to bowel necrosis, perforation and death. Plain abdominal X-rays are frequently used as a first-line test but the availability of immediate expert radiological review is variable. The aim was to investigate the feasibility of using a deep learning model for automated identification of small bowel obstruction. Methods: A total of 990 plain abdominal radiographs were collected, 445 with normal findings and 445 demonstrating small bowel obstruction. The images were labelled using the radiology reports, subsequent CT scans, surgical operation notes and enhanced radiological review. The data were used to develop a predictive model comprising an ensemble of five convolutional neural networks trained using transfer learning. Results: The performance of the model was excellent with an area under the receiver operator curve (AUC) of 0.961, corresponding to sensitivity and specificity of 91 and 93% respectively. Conclusion: Deep learning can be used to identify small bowel obstruction on plain radiographs with a high degree of accuracy. A system such as this could be used to alert clinicians to the presence of urgent findings with the potential for expedited clinical review and improved patient outcomes. Advances in knowledge: This paper describes a novel labelling method using composite clinical follow-up and demonstrates that ensemble models can be used effectively in medical imaging tasks. It also provides evidence that deep learning methods can be used to identify small bowel obstruction with high accuracy.