Background Surgery is the main modality of cure for solid cancers and was prioritised to continue during COVID-19 outbreaks. This study aimed to identify immediate areas for system strengthening by comparing the delivery of elective cancer surgery during the COVID-19 pandemic in periods of lockdown versus light restriction. Methods This international, prospective, cohort study enrolled 20 006 adult (≥18 years) patients from 466 hospitals in 61 countries with 15 cancer types, who had a decision for curative surgery during the COVID-19 pandemic and were followed up until the point of surgery or cessation of follow-up (Aug 31, 2020). Average national Oxford COVID-19 Stringency Index scores were calculated to define the government response to COVID-19 for each patient for the period they awaited surgery, and classified into light restrictions (index <20), moderate lockdowns (20–60), and full lockdowns (>60). The primary outcome was the non-operation rate (defined as the proportion of patients who did not undergo planned surgery). Cox proportional-hazards regression models were used to explore the associations between lockdowns and non-operation. Intervals from diagnosis to surgery were compared across COVID-19 government response index groups. This study was registered at ClinicalTrials.gov , NCT04384926 . Findings Of eligible patients awaiting surgery, 2003 (10·0%) of 20 006 did not receive surgery after a median follow-up of 23 weeks (IQR 16–30), all of whom had a COVID-19-related reason given for non-operation. Light restrictions were associated with a 0·6% non-operation rate (26 of 4521), moderate lockdowns with a 5·5% rate (201 of 3646; adjusted hazard ratio [HR] 0·81, 95% CI 0·77–0·84; p<0·0001), and full lockdowns with a 15·0% rate (1775 of 11 827; HR 0·51, 0·50–0·53; p<0·0001). In sensitivity analyses, including adjustment for SARS-CoV-2 case notification rates, moderate lockdowns (HR 0·84, 95% CI 0·80–0·88; p<0·001), and full lockdowns (0·57, 0·54–0·60; p<0·001), remained independently associated with non-operation. Surgery beyond 12 weeks from diagnosis in patients without neoadjuvant therapy increased during lockdowns (374 [9·1%] of 4521 in light restrictions, 317 [10·4%] of 3646 in moderate lockdowns, 2001 [23·8%] of 11 827 in full lockdowns), although there were no differences in resectability rates observed with longer delays. Interpretation Cancer surgery systems worldwide were fragile to lockdowns, with one in seven patients who were in regions with full lockdowns not undergoing planned surgery and experiencing longer preoperative delays. Although short-term oncological outcomes were not compromised in those selected for surgery, delays and non-operations might lead to long-term reductions in survival. During current and future periods of societal restriction, the resilience of elective surgery systems requires strengthening, which might include...
Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed. Objectives: To develop and validate a deep learning method to improve the management of IPNs. Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions. Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4–90.7%) and 91.9% (95% CI, 88.7–94.7%), compared with 78.1% (95% CI, 68.7–86.4%) and 81.9 (95% CI, 76.1–87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts. Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis.
Purpose We propose a systematic methodology to quantify incidentally identified pulmonary nodules based on observed radiological traits (semantics) quantified on a point scale and a machine learning method using these data to predict cancer status. Materials and Methods We investigated 172 patients who had low-dose computed tomography (LDCT) images, with 102 and 70 patients grouped into training and validation cohorts, respectively. On the images, 24 radiological traits were systematically scored and a linear classifier was built to relate the traits to malignant status. The model was formed both with and without size descriptors to remove bias due to nodule size. The multivariate pairs formed on the training set was tested on an independent validation data set to evaluate its performance. Results The best four feature set that included a size measurement (Set 1), was short axis, contour, concavity, and texture, which had an area under the receiver operator characteristic curve (AUROC) of 0.88 (Accuracy= 81%, Sensitivity= 76.2%, Specificity= 91.7%). If size measures were excluded, the four best features (Set 2) were: location, fissure attachment, lobulation, and spiculation which had an AUROC of 0.83 (Accuracy= 73.2%, Sensitivity= 73.8%, Specificity= 81.7%) in predicting malignancy in primary nodules. The validation test AUROC was 0.8 (Accuracy=74.3%, Sensitivity =66.7%, Specificity= 75.6%) and 0.74 (Accuracy=71.4%, Sensitivity = 61.9%, Specificity = 75.5%) for Sets 1 and 2, respectively. Conclusions Radiological image traits are useful in predicting malignancy in lung nodules. These semantic traits can be used in combination with size-based measures to enhance prediction accuracy and reducing false positives.
BACKGROUND:The aims of this study were to provide data on the safety of head and neck cancer surgery currently being undertaken during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: This international, observational cohort study comprised 1137 consecutive patients with head and neck cancer undergoing primary surgery with curative intent in 26 countries. Factors associated with severe pulmonary complications in COVID-19-positive patients and infections in the surgical team were determined by univariate analysis. RESULTS: Among the 1137 patients, the commonest sites were the oral cavity (38%) and the thyroid (21%). For oropharynx and larynx tumors, nonsurgical therapy was favored in most cases. There was evidence of surgical de-escalation of neck management and reconstruction. Overall 30-day mortality was 1.2%. Twenty-nine patients (3%) tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within 30 days of surgery; 13 of these patients (44.8%) developed severe respiratory complications, and 3.51 (10.3%) died. There were significant correlations with an advanced tumor stage and admission to critical care. Members of the surgical team tested positive within 30 days of surgery in 40 cases (3%). There were significant associations with operations in which the patients also tested positive for SARS-CoV-2 within 30 days, with a high community incidence of SARS-CoV-2, with screened patients, with oral tumor sites, and with tracheostomy. CONCLUSIONS: Head and neck cancer surgery in the COVID-19 era appears safe even when surgery is prolonged and complex. The overlap in COVID-19 between patients and members of the surgical team raises the suspicion of failures in cross-infection measures or the use of personal protective equipment. Cancer 2020;0:1-13.
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