Systematic review and meta-analysis of the prognostic impact of lymph node micrometastasis and isolated tumour cells in patients with stage I-IIIA non-small cell lung cancer.

Lymph node micrometastases could be one of the reasons for the high recurrence rate after complete surgical resection in stage I-IIIA non-small cell lung cancer (NSCLC). The standard evaluation of a single haematoxylin and eosin (H&E) slide of a paraffin-embedded section of a lymph node is insufficient for the detection of micrometastases, and there is a need for additional histopathological evaluation. The association of lymph node micrometastases with survival remains as yet unresolved. The aim of this systematic review and meta-analysis is to investigate if lymph node micrometastases and isolated tumour cells in patients with stage I-IIIA NSCLC, detected with multiple sectioning and/or immunohistochemistry (IHC) and/or reverse transcriptase polymerase chain reaction (RT-PCR), are associated with overall survival (OS) and disease-free survival (DFS) after surgical resection. We performed a meta-analysis of time-to-event outcomes based on 15 articles using ancillary techniques to detect micrometastases. We extracted the OS and DFS every 3-6 months after surgery, for patients with and without occult lymph node micrometastasis, from the survival curves published in each article. These data were used to reconstruct OS and DFS for 'micrometastasis' and 'no micrometastasis' groups. Based on all included studies that used IHC, serial sectioning, or RT-PCR, we found a 5-year OS of 55% (micrometastasis) vs. 75% (no micrometastasis), and a 5-year DFS of 53% (micrometastasis) vs. 75% (no micrometastasis). Patients with stage I-IIIA NSCLC with lymph node micrometastases detected by ancillary histopathological and molecular techniques have a significantly poorer OS and DFS compared to patients without lymph node micrometastases.

Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer.

AIMS: Immunohistochemical programmed death-ligand 1 (PD-L1) staining to predict responsiveness to immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) has several drawbacks: a robust gold standard is lacking, and there is substantial interobserver and intraobserver variance, with up to 20% discordance around cutoff points. The aim of this study was to develop a new deep learning-based PD-L1 tumour proportion score (TPS) algorithm, trained and validated on a routine diagnostic dataset of digitised PD-L1 (22C3, laboratory-developed test)-stained samples. METHODS AND RESULTS: We designed a fully supervised deep learning algorithm for whole-slide PD-L1 assessment, consisting of four sequential convolutional neural networks (CNNs), using aiforia create software. We included 199 whole slide images (WSIs) of 'routine diagnostic' histology samples from stage IV NSCLC patients, and trained the algorithm by using a training set of 60 representative cases. We validated the algorithm by comparing the algorithm TPS with the reference score in a held-out validation set. The algorithm had similar concordance with the reference score (79%) as the pathologists had with one another (75%). The intraclass coefficient was 0.96 and Cohen's κ coefficient was 0.69 for the algorithm. Around the 1% and 50% cutoff points, concordance was also similar between pathologists and the algorithm. CONCLUSIONS: We designed a new, deep learning-based PD-L1 TPS algorithm that is similarly able to assess PD-L1 expression in daily routine diagnostic cases as pathologists. Successful validation on routine diagnostic WSIs and detailed visual feedback show that this algorithm meets the requirements for functioning as a 'scoring assistant'.

Predicting inadequate bowel preparation for colonoscopy in participants receiving split-dose bowel preparation: development and validation of a prediction score.

BACKGROUND: Adequate bowel preparation is important for optimal colonoscopy. It is important to identify patients at risk for inadequate bowel preparation because this allows taking precautions in this specific group. OBJECTIVE: To develop a prediction score to identify patients at risk for inadequate bowel preparation who may benefit from an intensified bowel cleansing regimen. DESIGN: Patient and colonoscopy data were prospectively collected, whereas clinical data were retrospectively collected for a total of 1996 colonoscopies in participants who received split-dose bowel preparation. Multivariate logistic regression analyses were conducted in a random two-thirds of the cohort to develop a prediction model. Validation and evaluation of the discriminative power of the prediction model were performed within the remaining one-third of the cohort. SETTING: Four centers, including one academic and three medium-to-large size nonacademic centers. PATIENTS: Consecutive colonoscopies in November and December 2012. Mean age was 57.3 ± 15.9 years, 45.8% were male and indications for colonoscopy were screening and/or surveillance (27%), abdominal symptoms and/or blood loss and/or anemia (60%), inflammatory bowel disease (9%), and others (4%). INTERVENTIONS: Colonoscopy. MAIN OUTCOME MEASUREMENTS: Inadequate bowel preparation defined as Boston Bowel Preparation Scale score <6. RESULTS: A total of 1331 colonoscopies were included in the development cohort, of which 172 (12.9%) had an inadequate bowel preparation. Independent factors included in the prediction model were American Society of Anesthesiologists Physical Status Classification System score ≥3, use of tricyclic antidepressants, use of opioids, diabetes, chronic constipation, history of abdominal and/or pelvic surgery, history of inadequate bowel preparation, and current hospitalization. The discriminative ability of the scale was good, with an area under the curve of 0.77 in the validation cohort. LIMITATIONS: Study design partially retrospective, no data on patient compliance. CONCLUSION: We developed a validated, easy-to-use prediction scale that can be used to identify subjects with an increased risk of inadequate bowel preparation with good accuracy.