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As of April 2015, participants in the Danish Lung Cancer Screening Trial had been followed for at least 5 years since their last screening. Mortality, causes of death, and lung cancer findings are reported to explore the effect of computed tomography (CT) screening. A total of 4,104 participants aged 50-70 years at the time of inclusion and with a minimum 20 pack-years of smoking were randomized to have five annual low-dose CT scans (study group) or no screening (control group). Follow-up information regarding date and cause of death, lung cancer diagnosis, cancer stage, and histology was obtained from national registries. No differences between the two groups in lung cancer mortality (hazard ratio, 1.03; 95% confidence interval, 0.66-1.6; P = 0.888) or all-cause mortality (hazard ratio, 1.02; 95% confidence interval, 0.82-1.27; P = 0.867) were observed. More cancers were found in the screening group than in the no-screening group (100 vs. 53, respectively; P < 0.001), particularly adenocarcinomas (58 vs. 18, respectively; P < 0.001). More early-stage cancers (stages I and II, 54 vs. 10, respectively; P < 0.001) and stage IIIa cancers (15 vs. 3, respectively; P = 0.009) were found in the screening group than in the control group. Stage IV cancers were nonsignificantly more frequent in the control group than in the screening group (32 vs. 23, respectively; P = 0.278). For the highest-stage cancers (T4N3M1, 21 vs. 8, respectively; P = 0.025), this difference was statistically significant, indicating an absolute stage shift. Older participants, those with chronic obstructive pulmonary disease, and those with more than 35 pack-years of smoking had a significantly increased risk of death due to lung cancer, with nonsignificantly fewer deaths in the screening group. No statistically significant effects of CT screening on lung cancer mortality were found, but the results of post hoc high-risk subgroup analyses showed nonsignificant trends that seem to be in good agreement with the results of the National Lung Screening Trial. Clinical trial registered with www.clinicaltrials.gov (NCT00496977).

US guidelines now recommend lung cancer screening with low-dose CT for high-risk individuals. Reports of new nodules after baseline screening have been scarce and are inconsistent because of differences in definitions used. We aimed to identify the occurrence of new solid nodules and their probability of being lung cancer at incidence screening rounds in the Dutch-Belgian Randomized Lung Cancer Screening Trial (NELSON). In the ongoing, multicentre, randomised controlled NELSON trial, between Dec 23, 2003, and July 6, 2006, 15 822 participants who had smoked at least 15 cigarettes a day for more than 25 years or ten cigarettes a day for more than 30 years and were current smokers, or had quit smoking less than 10 years ago, were enrolled and randomly assigned to receive either screening with low-dose CT (n=7915) or no screening (n=7907). From Jan 28, 2004, to Dec 18, 2006, 7557 individuals underwent baseline screening with low-dose CT; 7295 participants underwent second and third screening rounds. We included all participants with solid non-calcified nodules, registered by the NELSON radiologists as new or smaller than 15 mm3 (study detection limit) at previous screens. Nodule volume was generated semiautomatically by software. We calculated the maximum volume doubling time for nodules with an estimated percentage volume change of 25% or more, representing the minimum growth rate for the time since the previous scan. Lung cancer diagnosis was based on histology, and benignity was based on histology or stable size for at least 2 years. The NELSON trial is registered at trialregister.nl, number ISRCTN63545820. We analysed data for participants with at least one solid non-calcified nodule at the second or third screening round. In the two incidence screening rounds, the NELSON radiologists registered 1222 new solid nodules in 787 (11%) participants. A new solid nodule was lung cancer in 49 (6%) participants with new solid nodules and, in total, 50 lung cancers were found, representing 4% of all new solid nodules. 34 (68%) lung cancers were diagnosed at stage I. Nodule volume had a high discriminatory power (area under the receiver operating curve 0·795 [95% CI 0·728–0·862]; p<0·0001). Nodules smaller than 27 mm3 had a low probability of lung cancer (two [0·5%] of 417 nodules; lung cancer probability 0·5% [95% CI 0·0–1·9]), nodules with a volume of 27 mm3 up to 206 mm3 had an intermediate probability (17 [3·1%] of 542 nodules; lung cancer probability 3·1% [1·9–5·0]), and nodules of 206 mm3 or greater had a high probability (29 [16·9%] of 172 nodules; lung cancer probability 16·9% [12·0–23·2]). A volume cutoff value of 27 mm3 or greater had more than 95% sensitivity for lung cancer. Our study shows that new solid nodules are detected at each screening round in 5–7% of individuals who undergo screening for lung cancer with low-dose CT. These new nodules have a high probability of malignancy even at a small size. These findings should be considered in future screening guidelines, and new solid nodules should be followed up more aggressively than nodules detected at baseline screening. Zorgonderzoek Nederland Medische Wetenschappen and Koningin Wilhelmina Fonds Kankerbestrijding.

Noninvasive differentiation between the squamous cell carcinoma (SCC) and adenocarcinoma (ADC) subtypes of non-small cell lung cancer (NSCLC) could benefit patients who are unsuitable for invasive diagnostic procedures. Therefore, this study evaluates the predictive performance of a PET/CT-based radiomics model. It aims to distinguish between the histological subtypes of lung adenocarcinoma and squamous cell carcinoma, employing four different machine learning techniques. A total of 255 Non-Small Cell Lung Cancer (NSCLC) patients were retrospectively analyzed and randomly divided into the training (n = 177) and validation (n = 78) sets, respectively. Radiomics features were extracted, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was employed for feature selection. Subsequently, models were constructed using four distinct machine learning techniques, with the top-performing algorithm determined by evaluating metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC). The efficacy of the various models was appraised and compared using the DeLong test. A nomogram was developed based on the model with the best predictive efficiency and clinical utility, and it was validated using calibration curves. Results indicated that the logistic regression classifier had better predictive power in the validation cohort of the radiomic model. The combined model (AUC 0.870) exhibited superior predictive power compared to the clinical model (AUC 0.848) and the radiomics model (AUC 0.774). In this study, we discovered that the combined model, refined by the logistic regression classifier, exhibited the most effective performance in classifying the histological subtypes of NSCLC.

BackgroundIn the USA annual lung cancer screening is recommended. However, the optimal screening strategy (eg, screening interval, screening rounds) is unknown. This study provides results of the fourth screening round after a 2.5-year interval in the Dutch-Belgian Lung Cancer Screening trial (NELSON).MethodsEurope's largest, sufficiently powered randomised lung cancer screening trial was designed to determine whether low-dose CT screening reduces lung cancer mortality by ≥25% compared with no screening after 10 years of follow-up. The screening arm (n=7915) received screening at baseline, after 1 year, 2 years and 2.5 years. Performance of the NELSON screening strategy in the final fourth round was evaluated. Comparisons were made between lung cancers detected in the first three rounds, in the final round and during the 2.5-year interval.ResultsIn round 4, 46 cancers were screen-detected and there were 28 interval cancers between the third and fourth screenings. Compared with the second round screening (1-year interval), in round 4 a higher proportion of stage IIIb/IV cancers (17.3% vs 6.8%, p=0.02) and higher proportions of squamous-cell, bronchoalveolar and small-cell carcinomas (p=0.001) were detected. Compared with a 2-year interval, the 2.5-year interval showed a higher non-significant stage distribution (stage IIIb/IV 17.3% vs 5.2%, p=0.10). Additionally, more interval cancers manifested in the 2.5-year interval than in the intervals of previous rounds (28 vs 5 and 28 vs 19).ConclusionsA 2.5-year interval reduced the effect of screening: the interval cancer rate was higher compared with the 1-year and 2-year intervals, and proportion of advanced disease stage in the final round was higher compared with the previous rounds.Trial registration numberISRCTN63545820.

Sentinel-lymph-node mapping has been advocated as an alternative staging technique for endometrial cancer. The aim of this study was to measure the sensitivity and negative predictive value of sentinel-lymph-node mapping compared with the gold standard of complete lymphadenectomy in detecting metastatic disease for endometrial cancer. In the FIRES multicentre, prospective, cohort study patients with clinical stage 1 endometrial cancer of all histologies and grades undergoing robotic staging were eligible for study inclusion. Patients received a standardised cervical injection of indocyanine green and sentinel-lymph-node mapping followed by pelvic lymphadenectomy with or without para-aortic lymphadenectomy. 18 surgeons from ten centres (tertiary academic and community non-academic) in the USA participated in the trial. Negative sentinel lymph nodes (by haematoxylin and eosin staining on sections) were ultra-staged with immunohistochemistry for cytokeratin. The primary endpoint, sensitivity of the sentinel-lymph-node-based detection of metastatic disease, was defined as the proportion of patients with node-positive disease with successful sentinel-lymph-node mapping who had metastatic disease correctly identified in the sentinel lymph node. Patients who had mapping of at least one sentinel lymph node were included in the primary analysis (per protocol). All patients who received study intervention (injection of dye), regardless of mapping result, were included as part of the assessment of mapping and in the safety analysis in an intention-to-treat manner. The trial was registered with ClinicalTrials.gov, number NCT01673022 and is completed and closed. Between Aug 1, 2012, and Oct 20, 2015, 385 patients were enrolled. Sentinel-lymph-node mapping with complete pelvic lymphadenectomy was done in 340 patients and para-aortic lymphadenectomy was done in 196 (58%) of these patients. 293 (86%) patients had successful mapping of at least one sentinel lymph node. 41 (12%) patients had positive nodes, 36 of whom had at least one mapped sentinel lymph node. Nodal metastases were identified in the sentinel lymph nodes of 35 (97%) of these 36 patients, yielding a sensitivity to detect node-positive disease of 97·2% (95% CI 85·0–100), and a negative predictive value of 99·6% (97·9–100). The most common grade 3–4 adverse events or serious adverse events were postoperative neurological disorders (4 patients) and postoperative respiratory distress or failure (4 patients). 22 patients had serious adverse events, with one related to the study intervention: a ureteral injury incurred during sentinel-lymph-node dissection. Sentinel lymph nodes identified with indocyanine green have a high degree of diagnostic accuracy in detecting endometrial cancer metastases and can safely replace lymphadenectomy in the staging of endometrial cancer. Sentinel lymph node biopsy will not identify metastases in 3% of patients with node-positive disease, but has the potential to expose fewer patients to the morbidity of a complete lymphadenectomy. Indiana University Health, Indiana University Health Simon Cancer Center, and the Indiana University Department of Obstetrics and Gynecology.

Rapid antimicrobial susceptibility testing (AST) is urgently needed for treating infections with appropriate antibiotics and slowing down the emergence of antibiotic‐resistant bacteria. Here, a phenotypic platform that rapidly produces AST results by femtosecond stimulated Raman scattering imaging of deuterium oxide (D2O) metabolism is reported. Metabolic incorporation of D2O into biomass in a single bacterium and the metabolic response to antibiotics are probed in as short as 10 min after culture in 70% D2O medium, the fastest among current technologies. Single‐cell metabolism inactivation concentration (SC‐MIC) is obtained in less than 2.5 h from colony to results. The SC‐MIC results of 37 sets of bacterial isolate samples, which include 8 major bacterial species and 14 different antibiotics often encountered in clinic, are validated by standard minimal inhibitory concentration blindly measured via broth microdilution. Toward clinical translation, stimulated Raman scattering imaging of D2O metabolic incorporation and SC‐MIC determination after 1 h antibiotic treatment and 30 min mixture of D2O and antibiotics incubation of bacteria in urine or whole blood is demonstrated. A phenotypic platform that rapidly produces antimicrobial susceptibility testing results by femtosecond stimulated Raman scattering imaging of D2O metabolism is reported. Metabolic incorporation of D2O into biomass in a single bacterium and the metabolic response to antibiotics are probed. Single‐cell metabolism inactivation concentration is obtained in less than 2.5 h from colony to results.

The advent of single‐cell methods is paving the way for an in‐depth understanding of the cell cycle with unprecedented detail. Due to its ramifications in nearly all biological processes, the evaluation of cell cycle progression is critical for an exhaustive cellular characterization. Here, we present DeepCycle, a deep learning method for estimating a cell cycle trajectory from unsegmented single‐cell microscopy images, relying exclusively on the brightfield and nuclei‐specific fluorescent signals. DeepCycle was evaluated on 2.6 million single‐cell microscopy images of MDCKII cells with the fluorescent FUCCI2 system. DeepCycle provided a latent representation of cell images revealing a continuous and closed trajectory of the cell cycle. Further, we validated the DeepCycle trajectories by showing its nearly perfect correlation with real time measured from live‐cell imaging of cells undergoing an entire cell cycle. This is the first model able to resolve the closed cell cycle trajectory, including cell division, solely based on unsegmented microscopy data from adherent cell cultures. Synopsis DeepCycle is a deep neural network able to reconstruct a cyclic cell cycle trajectory from unsegmented cell images. The model is validated on cells undergoing a full cell cycle by comparing the progression of the inferred trajectory to real time. The deep learning model DeepCycle reconstructs a cyclic cell cycle trajectory solely from unsegmented images in the Hoescht and Brightfield channels. The model was trained using fluorescently labelled cell cycle markers from the FUCCI2 system. The reconstructed DeepCycle pseudotime was validated by comparing its progression to the measured real cell cycle time of cells undergoing an entire cell cycle. Graphical Abstract DeepCycle is a deep neural network able to reconstruct a cyclic cell cycle trajectory from unsegmented cell images. The model is validated on cells undergoing a full cell cycle by comparing the progression of the inferred trajectory to real time.

Many biological networks respond to various inputs through a common signaling molecule that triggers distinct cellular outcomes. One potential mechanism for achieving specific input–output relationships is to trigger distinct dynamical patterns in response to different stimuli. Here we focused on the dynamics of p53, a tumor suppressor activated in response to cellular stress. We quantified the dynamics of p53 in individual cells in response to UV and observed a single pulse that increases in amplitude and duration in proportion to the UV dose. This graded response contrasts with the previously described series of fixed pulses in response to γ‐radiation. We further found that while γ‐triggered p53 pulses are excitable, the p53 response to UV is not excitable and depends on continuous signaling from the input‐sensing kinases. Using mathematical modeling and experiments, we identified feedback loops that contribute to specific features of the stimulus‐dependent dynamics of p53, including excitability and input‐duration dependency. Our study shows that different stresses elicit different temporal profiles of p53, suggesting that modulation of p53 dynamics might be used to achieve specificity in this network.

The soilborne ascomycete fungus Verticillium dahliae causes destructive vascular wilt disease in hundreds of dicotyledonous plant species. However, our understanding of the early invasion from the epidermis to the vasculature and the prompt proliferation and colonization in the xylem tissues remains poor. To elaborate the detailed infection strategy of V. dahliae in host plants, we traced the whole infection process of V. dahliae by live‐cell imaging combined with high‐resolution scanning electron microscopy. The 4D image series demonstrated that the apex of invading hyphae becomes tapered and directly invades the intercellular space of root epidermal cells at the initial infection. Following successful epidermal invasion, the invading hyphae extend in the intercellular space of the root cortex toward the vascular tissues. Importantly, the high‐resolution microscopic and live‐cell images demonstrated (a) that conidia are formed via budding at the apex of the hyphae in the xylem vessels to promote systemic propagation vertically, and (b) that the hyphae freely cross adjacent xylem vessels through the intertracheary pits to achieve horizontal colonization. Our findings provide a solid cellular basis for future studies on both intracellular invasion and vascular colonization/proliferation during V. dahliae infection and pathogenesis in host plants. High‐resolution scanning electron microscopy and live‐cell images show that Verticillium dahliae hyphae invade the intercellular space of the host root epidermis and differentiate into simple conidiophores to promote systemic propagation in xylem vessels.

Imaging of microscopic objects is of fundamental importance, especially in life sciences. Recent fast progress in electronic detection and control, numerical computation, and digital image processing, has been crucial in advancing modern microscopy. Digital holography is a new field in three-dimensional imaging. Digital reconstruction of a hologram offers the remarkable capability to refocus at different depths inside a transparent or semi-transparent object. Thus, this technique is very suitable for biological cell studies in vivo and could have many biomedical and biological applications. A comprehensive review of the research carried out in the area of digital holographic microscopy (DHM) for live-cell imaging is presented. The novel microscopic technique is non-destructive and label-free and offers unmatched imaging capabilities for biological and bio-medical applications. It is also suitable for imaging and modelling of key metabolic processes in living cells, microbial communities or multicellular plant tissues. Live-cell imaging by DHM allows investigation of the dynamic processes underlying the function and morphology of cells. Future applications of DHM can include real-time cell monitoring in response to clinically relevant compounds. The effect of drugs on migration, proliferation, and apoptosis of abnormal cells is an emerging field of this novel microscopic technique.