Convolutional Neural Network-Based Deep Learning Engine for Mastoidectomy Instrument Recognition and Movement Tracking.

OBJECTIVE: To develop a convolutional neural network-based computer vision model to recognize and track 2 mastoidectomy surgical instruments-the drill and the suction-irrigator-from intraoperative video recordings of mastoidectomies. STUDY DESIGN: Technological development and model validation. SETTING: Academic center. METHODS: Ten 1-minute videos of mastoidectomies done for cochlear implantation by varying levels of resident surgeons were collected. For each video, containing 900 frames, an open-access computer vision annotation tool was used to annotate the drill and suction-irrigator class images with bounding boxes. A mastoidectomy instrument tracking module, which extracts the center coordinates of bounding boxes, was developed using a feature pyramid network and layered with DETECTRON, an open-access faster-region-based convolutional neural network. Eight videos were used to train the model, and 2 videos were used for testing. Outcome measures included Intersection over Union (IoU) ratio, accuracy, and average precision. RESULTS: For an IoU of 0.5, the mean average precision for the drill was 99% and 86% for the suction-irrigator. The model proved capable of generating maps of drill and suction-irrigator stroke direction and distance for the entirety of each video. CONCLUSIONS: This computer vision model can identify and track the drill and suction-irrigator from videos of intraoperative mastoidectomies performed by residents with excellent precision. It can now be employed to retrospectively study objective mastoidectomy measures of expert and resident surgeons, such as drill and suction-irrigator stroke concentration, economy of motion, speed, and coordination, setting the stage for characterization of objective expectations for safe and efficient mastoidectomies.

Latent heterogeneity of muscle-invasive bladder cancer in patient characteristics and survival: A population-based nation-wide study in the Bladder Cancer Data Base Sweden (BladderBaSe).

BACKGROUND: Patients with muscle-invasive bladder cancer (MIBC) constitute a heterogenous group in terms of patient and tumour characteristics ('case-mix') and prognosis. The aim of the current study was to investigate whether differences in survival could be used to separate MIBC patients into separate classes using a recently developed latent class regression method for survival analysis with competing risks. METHODS: We selected all participants diagnosed with MIBC in the Bladder Cancer Data Base Sweden (BladderBase) and analysed inter-patient heterogeneity in risk of death from bladder cancer and other causes. RESULTS: Using data from 9653 MIBC patients, we detected heterogeneity with six distinct latent classes in the studied population. The largest, and most frail class included 50% of the study population and was characterised by a somewhat larger proportion of women, higher age at diagnosis, more advanced disease and lower probability of curative treatment. Despite this, patients in this class treated with curative intent by radical cystectomy or radiotherapy had a lower association to risk of death. The second largest class included 23% and was substantially less frail as compared to the largest class. The third and fourth class included each around 9%-10%, whereas the fifth and sixth class included each 3%-4% of the population. CONCLUSIONS: Results from the current study are compatible with previous research and the method can be used to adjust comparisons in prognosis between MIBC populations for influential differences in the distribution of sub-classes.

Treatment Preference Among People With Cystic Fibrosis: The Importance of Reducing Treatment Burden.

BACKGROUND: There is a growing consensus that the perspective of the patient should be considered in the evaluation of novel interventions. RESEARCH QUESTION: What treatment outcomes matter to people with cystic fibrosis (CF), and what trade-offs would they make to realize these outcomes? STUDY DESIGN AND METHODS: Adults attending a specialist CF center were invited to complete an online discrete choice experiment (DCE). The DCE required participants to evaluate hypothetical CF treatment profiles, defined by impact on lung function, pulmonary exacerbations, abdominal symptoms, life expectancy, quality of life, inhaled medicine usage, and physiotherapy requirement. Choice data were analyzed, using multinomial logit and latent class models. RESULTS: One hundred and three people with CF completed the survey (median age, 35 years; range, 18-76 years); 52% were female; mean FEV1 % predicted, 69% [SD, 22%]). On average, an improvement in life expectancy by 10 years or more had the greatest impact on treatment preference, followed by a 15% increase in lung function. However, it was shown that people would trade substantial reductions in these key outcomes to reduce treatment time or burden. Preference profiles were not uniform across the sample: three distinct subgroups were identified, each placing markedly different importance on the relative importance of both life expectancy and lung function compared with other attributes. INTERPRETATION: The relative importance of treatment burden to people with CF, compared with life expectancy and lung function, suggests it should be routinely captured in clinical trials as an important secondary outcome measure. When considering the patient perspective, it is important that decision-makers recognize that the values of people with CF are not homogeneous.

Acute Immune Signatures and Their Legacies in Severe Acute Respiratory Syndrome Coronavirus-2 Infected Cancer Patients.

Given the immune system's importance for cancer surveillance and treatment, we have investigated how it may be affected by SARS-CoV-2 infection of cancer patients. Across some heterogeneity in tumor type, stage, and treatment, virus-exposed solid cancer patients display a dominant impact of SARS-CoV-2, apparent from the resemblance of their immune signatures to those for COVID-19+ non-cancer patients. This is not the case for hematological malignancies, with virus-exposed patients collectively displaying heterogeneous humoral responses, an exhausted T cell phenotype and a high prevalence of prolonged virus shedding. Furthermore, while recovered solid cancer patients' immunophenotypes resemble those of non-virus-exposed cancer patients, recovered hematological cancer patients display distinct, lingering immunological legacies. Thus, while solid cancer patients, including those with advanced disease, seem no more at risk of SARS-CoV-2-associated immune dysregulation than the general population, hematological cancer patients show complex immunological consequences of SARS-CoV-2 exposure that might usefully inform their care.

HER2-HER3 Heterodimer Quantification by FRET-FLIM and Patient Subclass Analysis of the COIN Colorectal Trial.

BACKGROUND: The phase III MRC COIN trial showed no statistically significant benefit from adding the EGFR-target cetuximab to oxaliplatin-based chemotherapy in first-line treatment of advanced colorectal cancer. This study exploits additional information on HER2-HER3 dimerization to achieve patient stratification and reveal previously hidden subgroups of patients who had differing disease progression and treatment response. METHODS: HER2-HER3 dimerization was quantified by fluorescence lifetime imaging microscopy in primary tumor samples from 550 COIN trial patients receiving oxaliplatin and fluoropyrimidine chemotherapy with or without cetuximab. Bayesian latent class analysis and covariate reduction was performed to analyze the effects of HER2-HER3 dimer, RAS mutation, and cetuximab on progression-free survival and overall survival (OS). All statistical tests were two-sided. RESULTS: Latent class analysis on a cohort of 398 patients revealed two patient subclasses with differing prognoses (median OS = 1624 days [95% confidence interval [CI] = 1466 to 1816 days] vs 461 days [95% CI = 431 to 504 days]): Class 1 (15.6%) showed a benefit from cetuximab in OS (hazard ratio = 0.43, 95% CI = 0.25 to 0.76, P = .004). Class 2 showed an association of increased HER2-HER3 with better OS (hazard ratio = 0.64, 95% CI = 0.44 to 0.94, P = .02). A class prediction signature was formed and tested on an independent validation cohort (n = 152) validating the prognostic utility of the dimer assay. Similar subclasses were also discovered in full trial dataset (n = 1630) based on 10 baseline clinicopathological and genetic covariates. CONCLUSIONS: Our work suggests that the combined use of HER dimer imaging and conventional mutation analyses will be able to identify a small subclass of patients (>10%) who will have better prognosis following chemotherapy. A larger prospective cohort will be required to confirm its utility in predicting the outcome of anti-EGFR treatment.

Heterogeneity in risk of prostate cancer: A Swedish population-based cohort study of competing risks and Type 2 diabetes mellitus.

Most previous studies of prostate cancer have not taken into account that men in the studied populations are also at risk of competing event, and that these men may have different susceptibility to prostate cancer risk. The aim of our study was to investigate heterogeneity in risk of prostate cancer, using a recently developed latent class regression method for competing risks. We further aimed to elucidate the association between Type 2 diabetes mellitus (T2DM) and prostate cancer risk, and to compare the results with conventional methods for survival analysis. We analysed the risk of prostate cancer in 126,482 men from the comparison cohort of the Prostate Cancer Data base Sweden (PCBaSe) 3.0. During a mean follow-up of 6 years 6,036 men were diagnosed with prostate cancer and 22,393 men died. We detected heterogeneity in risk of prostate cancer with two distinct latent classes in the study population. The smaller class included 9% of the study population in which men had a higher risk of prostate cancer and the risk was stronger associated with class membership than any of the covariates included in the study. Moreover, we found no association between T2DM and risk of prostate cancer after removal of the effect of informative censoring due to competing risks. The recently developed latent class for competing risks method could be used to provide new insights in precision medicine with the target to classify individuals regarding different susceptibility to a particular disease, reaction to a risk factor or response to treatment.

HER2-HER3 dimer quantification by FLIM-FRET predicts breast cancer metastatic relapse independently of HER2 IHC status.

Overexpression of HER2 is an important prognostic marker, and the only predictive biomarker of response to HER2-targeted therapies in invasive breast cancer. HER2-HER3 dimer has been shown to drive proliferation and tumor progression, and targeting of this dimer with pertuzumab alongside chemotherapy and trastuzumab, has shown significant clinical utility. The purpose of this study was to accurately quantify HER2-HER3 dimerisation in formalin fixed paraffin embedded (FFPE) breast cancer tissue as a novel prognostic biomarker.FFPE tissues were obtained from patients included in the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study. HER2-HER3 dimerisation was quantified using an improved fluorescence lifetime imaging microscopy (FLIM) histology-based analysis. Analysis of 131 tissue microarray cores demonstrated that the extent of HER2-HER3 dimer formation as measured by Förster Resonance Energy Transfer (FRET) determined through FLIM predicts the likelihood of metastatic relapse up to 10 years after surgery (hazard ratio 3.91 (1.61-9.5), p = 0.003) independently of HER2 expression, in a multivariate model. Interestingly there was no correlation between the level of HER2 protein expressed and HER2-HER3 heterodimer formation. We used a mathematical model that takes into account the complex interactions in a network of all four HER proteins to explain this counterintuitive finding.Future utility of this technique may highlight a group of patients who do not overexpress HER2 protein but are nevertheless dependent on the HER2-HER3 heterodimer as driver of proliferation. This assay could, if validated in a group of patients treated with, for instance pertuzumab, be used as a predictive biomarker to predict for response to such targeted therapies.

Correction: Robust Bayesian Fluorescence Lifetime Estimation, Decay Model Selection and Instrument Response Determination for Low-Intensity FLIM Imaging.

[This corrects the article DOI: 10.1371/journal.pone.0158404.].

Robust Bayesian Fluorescence Lifetime Estimation, Decay Model Selection and Instrument Response Determination for Low-Intensity FLIM Imaging.

We present novel Bayesian methods for the analysis of exponential decay data that exploit the evidence carried by every detected decay event and enables robust extension to advanced processing. Our algorithms are presented in the context of fluorescence lifetime imaging microscopy (FLIM) and particular attention has been paid to model the time-domain system (based on time-correlated single photon counting) with unprecedented accuracy. We present estimates of decay parameters for mono- and bi-exponential systems, offering up to a factor of two improvement in accuracy compared to previous popular techniques. Results of the analysis of synthetic and experimental data are presented, and areas where the superior precision of our techniques can be exploited in Förster Resonance Energy Transfer (FRET) experiments are described. Furthermore, we demonstrate two advanced processing methods: decay model selection to choose between differing models such as mono- and bi-exponential, and the simultaneous estimation of instrument and decay parameters.

Prediagnostic serum glucose and lipids in relation to survival in breast cancer patients: a competing risk analysis.

BACKGROUND: Abnormal glucose and lipids levels may impact survival after breast cancer (BC) diagnosis, but their association to other causes of mortality such as cardiovascular (CV) disease may result in a competing risk problem. METHODS: We assessed serum glucose, triglycerides (TG) and total cholesterol (TC) measured prospectively 3 months to 3 years before diagnosis in 1798 Swedish women diagnosed with any type of BC between 1985 and 1999. In addition to using Cox regression, we employed latent class proportional hazards models to capture any heterogeneity of associations between these markers and BC death. The latter method was extended to include the primary outcome (BC death) and competing outcomes (CV death and death from other causes), allowing latent class-specific hazard estimation for cause-specific deaths. RESULTS: A lack of association between prediagnostic glucose, TG or TC with BC death was observed with Cox regression. With latent class proportional hazards model, two latent classes (Class I and II) were suggested. Class I, comprising the majority (81.5 %) of BC patients, had an increased risk of BC death following higher TG levels (HR: 1.87, 95 % CI: 1.01-3.45 for every log TG increase). Lower overall survival was observed in Class II, but no association for BC death was found. On the other hand, TC positively corresponded to CV death in Class II, and similarly, glucose to death from other causes. CONCLUSION: Addressing cohort heterogeneity in relation to BC survival is important in understanding the relationship between metabolic markers and cause-specific death in presence of competing outcomes.