Radiomic analysis has recently demonstrated versatile uses in improving diagnostic and prognostic prediction accuracy for lung cancer. However, since lung tumors are subject to substantial motion due to respiration, the stability of radiomic features over the respiratory cycle of the patient needs to be investigated to better evaluate the robustness of the inter-patient feature variability for clinical applications, and its impact in such applications needs to be assessed. A full panel of 841 radiomic features, including tumor intensity, shape, texture, and wavelet features, were extracted from individual phases of a four-dimensional (4D) computed tomography on 20 early-stage non-small-cell lung cancer (NSCLC) patients. The stability of each radiomic feature was assessed across different phase images of the same patient using the coefficient of variation (COV). The relationship between individual COVs and tumor motion magnitude was inspected. Population COVs, the mean COVs of all 20 patients, were used to evaluate feature motion stability and categorize the radiomic features into 4 different groups. The two extremes, the Very Small group (COV≤5%) and the Large group (COV>20%), each accounted for about a quarter of the features. Shape features were the most stable, with COV≤10% for all features. A clinical study was subsequently conducted using 140 early-stage NSCLC patients. Radiomic features were employed to predict the overall survival with a 500-round bootstrapping. Identical multiple regression model development process was applied, and the model performance was compared between models with and without a feature pre-selection step based on 4D COV to pre-exclude unstable features. Among the systematically tested cutoff values, feature pre-selection with 4D COV≤5% achieved the optimal model performance. The resulting 3-feature radiomic model significantly outperformed its counterpart with no 4D COV pre-selection, with P = 2.16x10 -27 in the one-tailed t-test comparing the prediction performances of the two models.

Pretreatment serum levels of neurone specific enolase (NSE) were measured in patients with small cell lung cancer (SCLC). Median values were significantly higher in patients with extensive compared with limited stage disease (48 ng ml-1 v. 17 ng ml-1: P less than 0.001). Serial NSE levels paralleled the clinical response to treatment. In 37 patients with limited SCLC, receiving identical chemotherapy, the pretreatment NSE level was of prognostic significance: with an approximate reduction in median survival of 10% for each 5 ng ml-1 incremental rise in NSE (P = 0.004).

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Aim: To investigate the association between receiving treatment at academic centers and overall survival in pancreatic ductal adenocarcinoma patients who do not receive definitive surgery of the pancreatic tumor. Methods: Using the National Cancer Database, patients who were diagnosed with pancreatic ductal adenocarcinoma between 2004 to 2016 were identified. Results: Of 262,209 patients, 101,003 (38.5%) received treatment at academic centers. In the multivariable Cox regression analysis, patients who received treatment at a nonacademic facility had significantly worse overall survival compared with patients who were treated at an academic center (hazard ratio: 1.279; 95% CI: 1.268–1.290; p = 0.001). Conclusion: Compared with treatment at academic centers, treatment at nonacademic centers was associated with significantly worse overall survival in patients with nonsurgically managed pancreatic ductal adenocarcinoma.

Introduction: This multicentre randomised controlled trial investigated whether a computed tomography (CT) scan of the axilla could more accurately assess whether the axillary lymph nodes were involved with malignancy in patients with newly diagnosed breast cancer and therefore influence surgical decision-making with regard to axillary surgery. Methods: Patients with newly diagnosed breast cancer (via screening and symptomatic routes) at two NHS Trusts in the North East of England were recruited and randomised in equal numbers. Both groups received routine diagnostic and surgical care (usual care). In addition, one group received a CT scan of their axilla on the same side as the breast cancer. Results: The study recruited 297 patients, of whom 291 contributed to findings. CT scan-guided care did not result in a change in the need for a second operation, with about 20% of both groups needing further surgery. Patients within the two groups were similar before treatment, had similar types and grade of cancer, experienced similar pattern complications and reported similar experiences of care. Conclusion: New diagnostic imaging technologies regularly enter NHS centres of excellence as research tools. It is important these are evaluated rigorously before becoming routine care. In patients newly diagnosed with breast cancer, CT-augmented diagnosis of cancer in the axilla was not found to improve surgical outcomes or patient experience. O2Preoperative assessment of breast volume to aid surgical planning: comparison of software-based mammographic measurements with subsequent mastectomy volumes. Introduction: The proportion of breast volume excised during conservation surgery for breast cancer is crucial to cosmetic outcomes. Validated, expedient methods for accurate preoperative quantification of breast volume are lacking. This study evaluated breast volume measurements calculated by Volpara® breast density software, by comparing them with actual mastectomy volumes. Methods: From a prospective clinical database, 31 patients were identified for whom Volpara® (Matakina Technology Limited, New Zealand) volume measurements and mastectomy volumes were available. All patients had undergone skin-sparing mastectomy (SSM), bilateral in one case. Specimen volumes had been measured using a water-displacement technique. Volpara® volumes for the corresponding CC and MLO view of each of the 32 breasts were averaged and compared with the mastectomy volumes. Correlation was assessed using the Pearson correlation coefficient. Results: Volpara® breast volumes were, as expected, consistently higher than SSM volumes but with a very strong correlation (Pearson correlation coefficient for average Volpara® volumes and mastectomy volumes = 0.82 (P < 0.01)). Conclusion:The excellent correlation between Volpara® and SSM volumes suggests that this readily available and convenient preoperative measure of breast volume could be used as a tool to aid surgical planning in women with breast cancer, which might be particularly useful in those women not ...