Real-time automatic tumor segmentation for ultrasound-guided breast-conserving surgery navigation.

PURPOSE: Ultrasound-based navigation is a promising method in breast-conserving surgery, but tumor contouring often requires a radiologist at the time of surgery. Our goal is to develop a real-time automatic neural network-based tumor contouring process for intraoperative guidance. Segmentation accuracy is evaluated by both pixel-based metrics and expert visual rating. METHODS: This retrospective study includes 7318 intraoperative ultrasound images acquired from 33 breast cancer patients, randomly split between 80:20 for training and testing. We implement a u-net architecture to label each pixel on ultrasound images as either tumor or healthy breast tissue. Quantitative metrics are calculated to evaluate the model's accuracy. Contour quality and usability are also assessed by fellowship-trained breast radiologists and surgical oncologists. Additionally, the viability of using our u-net model in an existing surgical navigation system is evaluated by measuring the segmentation frame rate. RESULTS: The mean dice similarity coefficient of our u-net model is 0.78, with an area under the receiver-operating characteristics curve of 0.94, sensitivity of 0.95, and specificity of 0.67. Expert visual ratings are positive, with 93% of responses rating tumor contour quality at or above 7/10, and 75% of responses rating contour quality at or above 8/10. Real-time tumor segmentation achieved a frame rate of 16 frames-per-second, sufficient for clinical use. CONCLUSION: Neural networks trained with intraoperative ultrasound images provide consistent tumor segmentations that are well received by clinicians. These findings suggest that neural networks are a promising adjunct to alleviate radiologist workload as well as improving efficiency in breast-conserving surgery navigation systems.

Lack of definitive presurgical pathological diagnosis is associated with inadequate surgical margins in breast-conserving surgery.

PURPOSE: To determine the impact of definitive presurgical diagnosis on surgical margins in breast-conserving surgery (BCS) for primary carcinomas; clinicopathological features were also analyzed. METHODS: This retrospective study included women who underwent BCS for primary carcinomas in 2016 and 2017. Definitive presurgical diagnosis was defined as having a presurgical core needle biopsy (CNB) and not being upstaged between biopsy and surgery. Biopsy data and imaging findings including breast density were retrieved. Inadequate surgical margins (IM) were defined per latest ASCO and ASTRO guidelines. Univariable and multivariable analyses were performed. RESULTS: 360 women (median age, 66) met inclusion criteria with 1 having 2 cancers. 82.5% (298/361) were invasive cancers while 17.5% (63/361) were ductal carcinoma in situ (DCIS). Most biopsies were US-guided (284/346, 82.0%), followed by mammographic (60/346, 17.3%), and MRI-guided (2/346, 0.6%). US and mammographic CNB yielded median samples of 2 and 4, respectively, with a 14G needle. 15 patients (4.2%) lacked presurgical CNB. The IM rate was 30.0%. In multivariable analysis, large invasive cancers (>20 mm), dense breasts, and DCIS were associated with IM (p = 0.029, p = 0.010, and p = 0.013, respectively). Most importantly, lack of definitive presurgical diagnosis was a risk factor for IM (OR, 2.35; 95% CI: 1.23-4.51, p = 0.010). In contrast, neither patient age (<50) nor aggressive features (e.g., LVI) were associated with IM. CONCLUSION: Lack of a definitive presurgical diagnosis was associated with a two-fold increase of IM in BCS; other risk factors were dense breasts, large invasive cancers, and DCIS.

Participant selection for lung cancer screening by risk modelling (the Pan-Canadian Early Detection of Lung Cancer [PanCan] study): a single-arm, prospective study.

BACKGROUND: Results from retrospective studies indicate that selecting individuals for low-dose CT lung cancer screening on the basis of a highly predictive risk model is superior to using criteria similar to those used in the National Lung Screening Trial (NLST; age, pack-year, and smoking quit-time). We designed the Pan-Canadian Early Detection of Lung Cancer (PanCan) study to assess the efficacy of a risk prediction model to select candidates for lung cancer screening, with the aim of determining whether this approach could better detect patients with early, potentially curable, lung cancer. METHODS: We did this single-arm, prospective study in eight centres across Canada. We recruited participants aged 50-75 years, who had smoked at some point in their life (ever-smokers), and who did not have a self-reported history of lung cancer. Participants had at least a 2% 6-year risk of lung cancer as estimated by the PanCan model, a precursor to the validated PLCOm2012 model. Risk variables in the model were age, smoking duration, pack-years, family history of lung cancer, education level, body-mass index, chest x-ray in the past 3 years, and history of chronic obstructive pulmonary disease. Individuals were screened with low-dose CT at baseline (T0), and at 1 (T1) and 4 (T4) years post-baseline. The primary outcome of the study was incidence of lung cancer. This study is registered with ClinicalTrials.gov, number NCT00751660. FINDINGS: 7059 queries came into the study coordinating centre and were screened for PanCan risk. 15 were duplicates, so 7044 participants were considered for enrolment. Between Sept 24, 2008, and Dec 17, 2010, we recruited and enrolled 2537 eligible ever-smokers. After a median follow-up of 5·5 years (IQR 3·2-6·1), 172 lung cancers were diagnosed in 164 individuals (cumulative incidence 0·065 [95% CI 0·055-0·075], incidence rate 138·1 per 10 000 person-years [117·8-160·9]). There were ten interval lung cancers (6% of lung cancers and 6% of individuals with cancer): one diagnosed between T0 and T1, and nine between T1 and T4. Cumulative incidence was significantly higher than that observed in NLST (4·0%; p<0·0001). Compared with 593 (57%) of 1040 lung cancers observed in NLST, 133 (77%) of 172 lung cancers in the PanCan Study were early stage (I or II; p<0·0001). INTERPRETATION: The PanCan model was effective in identifying individuals who were subsequently diagnosed with early, potentially curable, lung cancer. The incidence of cancers detected and the proportion of early stage cancers in the screened population was higher than observed in previous studies. This approach should be considered for adoption in lung cancer screening programmes. FUNDING: Terry Fox Research Institute and Canadian Partnership Against Cancer.

Airway wall thickening and emphysema show independent familial aggregation in chronic obstructive pulmonary disease.

RATIONALE: It is unclear whether airway wall thickening and emphysema make independent contributions to airflow limitation in chronic obstructive pulmonary disease (COPD) and whether these phenotypes cluster within families. OBJECTIVES: To determine whether airway wall thickening and emphysema (1) make independent contributions to the severity of COPD and (2) show independent aggregation in families of individuals with COPD. METHODS: Index cases with COPD and their smoking siblings underwent spirometry and were offered high-resolution computed tomography scans of the thorax to assess the severity of airway wall thickening and emphysema. MEASUREMENTS AND MAIN RESULTS: A total of 3,096 individuals were recruited to the study, of whom 1,159 (519 probands and 640 siblings) had technically adequate high-resolution computed tomography scans without significant non-COPD-related thoracic disease. Airway wall thickness correlated with pack-years smoked (P < or = 0.001) and symptoms of chronic bronchitis (P < 0.001). FEV(1) (expressed as % predicted) was independently associated with airway wall thickness at a lumen perimeter of 10 mm (P = 0.0001) and 20 mm (P = 0.0013) and emphysema at -950 Hounsfield units (P < 0.0001). There was independent familial aggregation of both the emphysema (adjusted odds ratio, 2.1; 95% confidence interval, 1.1-4.0; P < or = 0.02) and airway disease phenotypes (P < 0.0001) of COPD. CONCLUSIONS: Airway wall thickening and emphysema make independent contributions to airflow obstruction in COPD. These phenotypes show independent aggregation within families of individuals with COPD, suggesting that different genetic factors influence these disease processes.