Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [18F]FDG PET/CT predicts survival in multiple myeloma.

PURPOSE: Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [18F]FDG PET/CT can provide prognostic information in MM, but it is hampered by issues regarding standardization of scan interpretation. Our group has recently demonstrated the feasibility of automated, volumetric assessment of bone marrow (BM) metabolic activity on PET/CT using a novel artificial intelligence (AI)-based tool. Accordingly, the aim of the current study is to investigate the prognostic role of whole-body calculations of BM metabolism in patients with newly diagnosed MM using this AI tool. MATERIALS AND METHODS: Forty-four, previously untreated MM patients underwent whole-body [18F]FDG PET/CT. Automated PET/CT image segmentation and volumetric quantification of BM metabolism were based on an initial CT-based segmentation of the skeleton, its transfer to the standardized uptake value (SUV) PET images, subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, ten different uptake thresholds (AI approaches), based on reference organs or absolute SUV values, were applied for definition of pathological tracer uptake and subsequent calculation of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Correlation analysis was performed between the automated PET values and histopathological results of the BM as well as patients' progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic (ROC) curve analysis was used to investigate the discrimination performance of MTV and TLG for prediction of 2-year PFS. The prognostic performance of the new Italian Myeloma criteria for PET Use (IMPeTUs) was also investigated. RESULTS: Median follow-up [95% CI] of the patient cohort was 110 months [105-123 months]. AI-based BM segmentation and calculation of MTV and TLG were feasible in all patients. A significant, positive, moderate correlation was observed between the automated quantitative whole-body PET/CT parameters, MTV and TLG, and BM plasma cell infiltration for all ten [18F]FDG uptake thresholds. With regard to PFS, univariable analysis for both MTV and TLG predicted patient outcome reasonably well for all AI approaches. Adjusting for cytogenetic abnormalities and BM plasma cell infiltration rate, multivariable analysis also showed prognostic significance for high MTV, which defined pathological [18F]FDG uptake in the BM via the liver. In terms of OS, univariable and multivariable analysis showed that whole-body MTV, again mainly using liver uptake as reference, was significantly associated with shorter survival. In line with these findings, ROC curve analysis showed that MTV and TLG, assessed using liver-based cut-offs, could predict 2-year PFS rates. The application of IMPeTUs showed that the number of focal hypermetabolic BM lesions and extramedullary disease had an adverse effect on PFS. CONCLUSIONS: The AI-based, whole-body calculations of BM metabolism via the parameters MTV and TLG not only correlate with the degree of BM plasma cell infiltration, but also predict patient survival in MM. In particular, the parameter MTV, using the liver uptake as reference for BM segmentation, provides solid prognostic information for disease progression. In addition to highlighting the prognostic significance of automated, global volumetric estimation of metabolic tumor burden, these data open up new perspectives towards solving the complex problem of interpreting PET scans in MM with a simple, fast, and robust method that is not affected by operator-dependent interventions.

A diagnostic phase III/IV seamless design to investigate the diagnostic accuracy and clinical effectiveness using the example of HEDOS and HEDOS II.

The development process of medical devices can be streamlined by combining different study phases. Here, for a diagnostic medical device, we present the combination of confirmation of diagnostic accuracy (phase III) and evaluation of clinical effectiveness regarding patient-relevant endpoints (phase IV) using a seamless design. This approach is used in the Thyroid HEmorrhage DetectOr Study (HEDOS & HEDOS II) investigating a post-operative hemorrhage detector named ISAR-M THYRO® in patients after thyroid surgery. Data from the phase III trial are reused as external controls in the control group of the phase IV trial. An unblinded interim analysis is planned between the two study stages which includes a recalculation of the sample size for the phase IV part after completion of the first stage of the seamless design. The study concept presented here is the first seamless design proposed in the field of diagnostic studies. Hence, the aim of this work is to emphasize the statistical methodology as well as feasibility of the proposed design in relation to the planning and implementation of the seamless design. Seamless designs can accelerate the overall trial duration and increase its efficiency in terms of sample size and recruitment. However, careful planning addressing numerous methodological and procedural challenges is necessary for successful implementation as well as agreement with regulatory bodies.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Metrics reloaded: recommendations for image analysis validation.

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

Robust incorporation of historical information with known type I error rate inflation.

Bayesian clinical trials can benefit from available historical information through the specification of informative prior distributions. Concerns are however often raised about the potential for prior-data conflict and the impact of Bayes test decisions on frequentist operating characteristics, with particular attention being assigned to inflation of type I error (TIE) rates. This motivates the development of principled borrowing mechanisms, that strike a balance between frequentist and Bayesian decisions. Ideally, the trust assigned to historical information defines the degree of robustness to prior-data conflict one is willing to sacrifice. However, such relationship is often not directly available when explicitly considering inflation of TIE rates. We build on available literature relating frequentist and Bayesian test decisions, and investigate a rationale for inflation of TIE rate which explicitly and linearly relates the amount of borrowing and the amount of TIE rate inflation in one-arm studies. A novel dynamic borrowing mechanism tailored to hypothesis testing is additionally proposed. We show that, while dynamic borrowing prevents the possibility to obtain a simple closed-form TIE rate computation, an explicit upper bound can still be enforced. Connections with the robust mixture prior approach, particularly in relation to the choice of the mixture weight and robust component, are made. Simulations are performed to show the properties of the approach for normal and binomial outcomes, and an exemplary application is demonstrated in a case study.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.

Simulating and reporting frequentist operating characteristics of clinical trials that borrow external information: Towards a fair comparison in case of one-arm and hybrid control two-arm trials.

Borrowing information from historical or external data to inform inference in a current trial is an expanding field in the era of precision medicine, where trials are often performed in small patient cohorts for practical or ethical reasons. Even though methods proposed for borrowing from external data are mainly based on Bayesian approaches that incorporate external information into the prior for the current analysis, frequentist operating characteristics of the analysis strategy are often of interest. In particular, type I error rate and power at a prespecified point alternative are the focus. We propose a procedure to investigate and report the frequentist operating characteristics in this context. The approach evaluates type I error rate of the test with borrowing from external data and calibrates the test without borrowing to this type I error rate. On this basis, a fair comparison of power between the test with and without borrowing is achieved. We show that no power gains are possible in one-sided one-arm and two-arm hybrid control trials with normal endpoint, a finding proven in general before. We prove that in one-arm fixed-borrowing situations, unconditional power (i.e., when external data is random) is reduced. The Empirical Bayes power prior approach that dynamically borrows information according to the similarity of current and external data avoids the exorbitant type I error inflation occurring with fixed borrowing. In the hybrid control two-arm trial we observe power reductions as compared to the test calibrated to borrowing that increase when considering unconditional power.

Optimizing expert and patient input in pediatric trial design: Lessons learned and recommendations from a collaboration between conect4children and European Patient-CEntric ClinicAl TRial PLatforms.

Advice from multiple stakeholders is required to design the optimal pediatric clinical trial. We present recommendations for acquiring advice from trial experts and patients/caregivers, derived from advice meetings that were performed through a collaboration of the Collaborative Network for European Clinical Trials for Children (c4c) and the European Patient-CEntric ClinicAl TRial PLatforms (EU-PEARL). Three advice meetings were performed: (1) an advice meeting for clinical and methodology experts, (2) an advice meeting for patients/caregivers, and (3) a combined meeting with both experts and patients/caregivers. Trial experts were recruited from c4c database. Patients/caregivers were recruited through a patient organization. Participants were asked to provide input on a trial protocol, including endpoints, outcomes, and the assessment schedule. Ten experts, 10 patients, and 13 caregivers participated. The advice meetings resulted in modification of eligibility criteria and outcome measures. We have provided recommendations for the most effective meeting type per protocol topic. Topics with limited options for patient input were most efficiently discussed in expert advice meetings. Other topics benefit from patient/caregiver input, either through a combined meeting with experts or a patients/caregivers-only advice meeting. Some topics, such as endpoints and outcome measures, are suitable for all meeting types. Combined sessions profit from synergy between experts and patients/caregivers, balancing input on protocol scientific feasibility and acceptability. Both experts and patients/caregivers provided critical input on the presented protocol. The combined meeting was the most effective methodology for most protocol topics. The presented methodology can be used effectively to acquire expert and patient feedback.

Application of an artificial intelligence-based tool in [18F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma.

PURPOSE: [18F]FDG PET/CT is an imaging modality of high performance in multiple myeloma (MM). Nevertheless, the inter-observer reproducibility in PET/CT scan interpretation may be hampered by the different patterns of bone marrow (BM) infiltration in the disease. Although many approaches have been recently developed to address the issue of standardization, none can yet be considered a standard method in the interpretation of PET/CT. We herein aim to validate a novel three-dimensional deep learning-based tool on PET/CT images for automated assessment of the intensity of BM metabolism in MM patients. MATERIALS AND METHODS: Whole-body [18F]FDG PET/CT scans of 35 consecutive, previously untreated MM patients were studied. All patients were investigated in the context of an open-label, multicenter, randomized, active-controlled, phase 3 trial (GMMG-HD7). Qualitative (visual) analysis classified the PET/CT scans into three groups based on the presence and number of focal [18F]FDG-avid lesions as well as the degree of diffuse [18F]FDG uptake in the BM. The proposed automated method for BM metabolism assessment is based on an initial CT-based segmentation of the skeleton, its transfer to the SUV PET images, the subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, six different SUV thresholds (Approaches 1-6) were applied for the definition of pathological tracer uptake in the skeleton [Approach 1: liver SUVmedian × 1.1 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 2: liver SUVmedian × 1.5 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 3: liver SUVmedian × 2 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 4: ≥ 2.5. Approach 5: ≥ 2.5 (axial skeleton), ≥ 2.0 (extremities). Approach 6: SUVmax liver]. Using the resulting masks, subsequent calculations of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in each patient were performed. A correlation analysis was performed between the automated PET values and the results of the visual PET/CT analysis as well as the histopathological, cytogenetical, and clinical data of the patients. RESULTS: BM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [18F]FDG uptake thresholds. In addition, there were significant differences between the three patient groups with regard to their MTV and TLG values for all applied thresholds of pathological tracer uptake. Furthermore, we could demonstrate a significant, moderate, positive correlation of BM plasma cell infiltration and plasma levels of ß2-microglobulin with the automated quantitative PET/CT parameters MTV and TLG after utilization of Approaches 1, 2, 4, and 5. CONCLUSIONS: The automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts.

Competition for cysteine acylation by C16:0 and C18:0 derived lipids is a global phenomenon in the proteome.

S-acylation is a reversible posttranslational protein modification consisting of attachment of a fatty acid to a cysteine via a thioester bond. Research over the last few years has shown that a variety of different fatty acids, such as palmitic acid (C16:0), stearate (C18:0), or oleate (C18:1), are used in cells to S-acylate proteins. We recently showed that GNAI proteins can be acylated on a single residue, Cys3, with either C16:0 or C18:1, and that the relative proportion of acylation with these fatty acids depends on the level of the respective fatty acid in the cell's environment. This has functional consequences for GNAI proteins, with the identity of the acylating fatty acid affecting the subcellular localization of GNAIs. Unclear is whether this competitive acylation is specific to GNAI proteins or a more general phenomenon in the proteome. We perform here a proteome screen to identify proteins acylated with different fatty acids. We identify 218 proteins acylated with C16:0 and 308 proteins acylated with C18-lipids, thereby uncovering novel targets of acylation. We find that most proteins that can be acylated by C16:0 can also be acylated with C18-fatty acids. For proteins with more than one acylation site, we find that this competitive acylation occurs on each individual cysteine residue. This raises the possibility that the function of many different proteins can be regulated by the lipid environment via differential S-acylation.

Can physiologic colonic [18F]FDG uptake in PET/CT imaging predict response to immunotherapy in metastatic melanoma?

AIM: The development of biomarkers that can reliably and early predict response to immune checkpoint inhibitors (ICIs) is crucial in melanoma. In recent years, the gut microbiome has emerged as an important regulator of immunotherapy response, which may, moreover, serve as a surrogate marker and prognosticator in oncological patients under immunotherapy. Aim of the present study is to investigate if physiologic colonic [18F]FDG uptake in PET/CT before start of ICIs correlates with clinical outcome of metastatic melanoma patients. The relation between [18F]FDG uptake in lymphoid cell-rich organs and long-term patient outcome is also assessed. METHODOLOGY: One hundred nineteen stage IV melanoma patients scheduled for immunotherapy with ipilimumab, applied either as monotherapy or in combination with nivolumab, underwent baseline [18F]FDG PET/CT. PET/CT data analysis consisted of standardized uptake value (SUV), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) calculations in the colon as well as measurements of the colon-to-liver SUV ratios (CLRmean, CLRmax). Visual grading of colon uptake based on a four-point scale was also performed. Moreover, the spleen-to-liver SUV ratios (SLRmean, SLRmax) and the bone marrow-to-liver SUV ratios (BLRmean, BLRmax) were calculated. We also measured serum lipopolysaccharide (LPS) levels as a marker for bacterial translocation and surrogate for mucosal defense homeostasis. The results were correlated with patients' best clinical response, progression-free survival (PFS), and overall survival (OS) as well as clinical signs of colitis. RESULTS: Median follow-up [95%CI] from the beginning of immunotherapy was 64.6 months [61.0-68.6 months]. Best response to treatment was progressive disease (PD) for 60 patients, stable disease (SD) for 37 patients, partial response (PR) for 18 patients, and complete response (CR) for 4 patients. Kaplan-Meier curves demonstrated a trend for longer PFS and OS in patients with lower colonic SUV and CLR values; however, no statistical significance for these parameters as prognostic factors was demonstrated. On the other hand, patients showing disease control as best response to treatment (SD, PR, CR) had significantly lower colonic MTV and TLG than those showing PD. With regard to lymphoid cell-rich organs, significantly lower baseline SLRmax and BLRmax were observed in patients responding with disease control than progression to treatment. Furthermore, patients with lower SLRmax and BLRmax values had a significantly longer OS when dichotomized at their median. In multivariate analysis, PET parameters that were found to significantly adversely correlate with patient survival were colonic MTV for PFS, colonic TLG for PFS, and BLRmax for PFS and OS. CONCLUSIONS: Physiologic colonic [18F]FDG uptake in PET/CT, as assessed by means of SUV, before start of ipilimumab-based treatment does not seem to independently predict patient survival of metastatic melanoma. On the other hand, volumetric PET parameters, such as MTV and TLG, derived from the normal gut may identify patients showing disease control to immunotherapy and significantly correlate with PFS. Moreover, the investigation of glucose metabolism in the spleen and the bone marrow may offer prognostic information.

Outcome of Children and Adolescents With Relapsed/Refractory/Progressive Malignancies Treated With Molecularly Informed Targeted Drugs in the Pediatric Precision Oncology Registry INFORM.

PURPOSE: INFORM is an international pediatric precision oncology registry, prospectively collecting molecular and clinical data of children with recurrent, progressive, or very high-risk malignancies. We have previously identified a subgroup of patients with improved outcomes on the basis of molecular profiling. The present analysis systematically investigates progression-free survival (PFS) and overall survival (OS) of patients receiving matching targeted treatment (MTT) with the most frequently applied drug classes and its correlation with underlying molecular alterations. METHODS: A cohort of 519 patients with relapsed or refractory high-risk malignancies who had completed a follow-up of at least 2 years or shorter in the case of death or loss to follow-up was analyzed. Survival times were compared using the log-rank test. RESULTS: MTT with anaplastic lymphoma kinase (ALK), neurotrophic tyrosine receptor kinase (NTRK), and B-RAF kinase (BRAF) inhibitors showed significantly improved PFS (P = .012) and OS (P = .036) in comparison with conventional treatment or no treatment. However, analysis of the four most commonly applied MTT groups, mitogen-activated protein kinase (MEK- n = 19), cyclin-dependent kinase (CDK- n = 23), other kinase (n = 62), and mammalian-target of rapamycin (mTOR- n = 20) inhibitors, did not reveal differences in PFS or OS compared with conventional treatment or no treatment in patients with similar molecular pathway alterations. We did not observe differences in the type of pathway alterations (eg, copy number alterations, single-nucleotide variants, InDels, gene fusions) addressed by MTT. CONCLUSION: Patients with respective molecular alterations benefit from treatment with ALK, NTRK, and BRAF inhibitors as previously described. No survival benefit was observed with MTT for mutations in the MEK, CDK, other kinase, or mTOR signaling pathways. The noninterventional character of a registry has to be taken into account when interpreting these data and underlines the need for innovative interventional biomarker-driven clinical trials in pediatric oncology.

Pulmonary magnetic resonance-guided online adaptive radiotherapy of locally advanced: the PUMA trial.

BACKGROUND: Patients with locally-advanced non-small-cell lung cancer (LA-NSCLC) are often ineligible for surgery, so that definitive chemoradiotherapy (CRT) represents the treatment of choice. Nevertheless, long-term tumor control is often not achieved. Intensification of radiotherapy (RT) to improve locoregional tumor control is limited by the detrimental effect of higher radiation exposure of thoracic organs-at-risk (OAR). This narrow therapeutic ratio may be expanded by exploiting the advantages of magnetic resonance (MR) linear accelerators, mainly the online adaptation of the treatment plan to the current anatomy based on daily acquired MR images. However, MR-guidance is both labor-intensive and increases treatment times, which raises the question of its clinical feasibility to treat LA-NSCLC. Therefore, the PUMA trial was designed as a prospective, multicenter phase I trial to demonstrate the clinical feasibility of MR-guided online adaptive RT in LA-NSCLC. METHODS: Thirty patients with LA-NSCLC in stage III A-C will be accrued at three German university hospitals to receive MR-guided online adaptive RT at two different MR-linac systems (MRIdian Linac®, View Ray Inc. and Elekta Unity®, Elekta AB) with concurrent chemotherapy. Conventionally fractioned RT with isotoxic dose escalation up to 70 Gy is applied. Online plan adaptation is performed once weekly or in case of major anatomical changes. Patients are followed-up by thoracic CT- and MR-imaging for 24 months after treatment. The primary endpoint is twofold: (1) successfully completed online adapted fractions, (2) on-table time. Main secondary endpoints include adaptation frequency, toxicity, local tumor control, progression-free and overall survival. DISCUSSION: PUMA aims to demonstrate the clinical feasibility of MR-guided online adaptive RT of LA-NSCLC. If successful, PUMA will be followed by a clinical phase II trial that further investigates the clinical benefits of this approach. Moreover, PUMA is part of a large multidisciplinary project to develop MR-guidance techniques. TRIAL REGISTRATION: ClinicalTrials.gov: NCT05237453 .

The prognostic value of [18F]FDG PET/CT based response monitoring in metastatic melanoma patients undergoing immunotherapy: comparison of different metabolic criteria.

PURPOSE: To investigate the prognostic value of [18F]FDG PET/CT as part of response monitoring in metastatic melanoma patients treated with immune checkpoint inhibitors (ICIs). METHODS: Sixty-seven patients underwent [18F]FDG PET/CT before start of treatment (baseline PET/CT), after two cycles (interim PET/CT) and after four cycles of ICIs administration (late PET/CT). Metabolic response evaluation was based on the conventional EORTC and PERCIST criteria, as well as the newly introduced, immunotherapy-modified PERCIMT, imPERCIST5 and iPERCIST criteria. Metabolic response to immunotherapy was classified according to four response groups (complete metabolic response [CMR], partial metabolic response [PMR], stable metabolic disease [SMD], progressive metabolic disease [PMD]), and further dichotomized by response rate (responders = [CMR] + [PMR] vs. non-responders = [PMD] + [SMD]), and disease control rate (disease control = [CMR] + [PMR] + [SMD] vs. [PMD]). The spleen-to-liver SUV ratios (SLRmean, SLRmax) and bone marrow-to-liver SUV ratios (BLRmean, BLRmax) were also calculated. The results of PET/CT were correlated with patients' overall survival (OS). RESULTS: Median patient follow up [95% CI] was 61.5 months [45.3 - 66.7 months]. On interim PET/CT, the application of the novel PERCIMT demonstrated significantly longer survival for metabolic responders, while the rest criteria revealed no significant survival differences between the different response groups. Respectively on late PET/CT, both a trend for longer OS and significantly longer OS were observed in patients responding to ICIs with metabolic response and disease control after application of various criteria, both conventional and immunotherapy-modified. Moreover, patients with lower SLRmean values demonstrated significantly longer OS. CONCLUSION: In patients with metastatic melanoma PET/CT-based response assessment after four ICIs cycles is significantly associated with OS after application of different metabolic criteria. The prognostic performance of the modality is also high after the first two ICIs cycles, especially with employment of novel criteria. In addition, investigation of spleen glucose metabolism may provide further prognostic information.

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark.

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery.

Beyond rankings: Learning (more) from algorithm validation.

Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is often restricted to pure ranking tables, leaving relevant questions unanswered. Specifically, little effort has been put into the systematic investigation on what characterizes images in which state-of-the-art algorithms fail. To address this gap in the literature, we (1) present a statistical framework for learning from challenges and (2) instantiate it for the specific task of instrument instance segmentation in laparoscopic videos. Our framework relies on the semantic meta data annotation of images, which serves as foundation for a General Linear Mixed Models (GLMM) analysis. Based on 51,542 meta data annotations performed on 2,728 images, we applied our approach to the results of the Robust Medical Instrument Segmentation Challenge (ROBUST-MIS) challenge 2019 and revealed underexposure, motion and occlusion of instruments as well as the presence of smoke or other objects in the background as major sources of algorithm failure. Our subsequent method development, tailored to the specific remaining issues, yielded a deep learning model with state-of-the-art overall performance and specific strengths in the processing of images in which previous methods tended to fail. Due to the objectivity and generic applicability of our approach, it could become a valuable tool for validation in the field of medical image analysis and beyond.

Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery.

Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy. While characterization of the tissue perfusion is crucial in various procedures, such as partial nephrectomy, doing so by means of visual inspection remains highly challenging. We developed a laparoscopic real-time multispectral imaging system featuring a compact and lightweight multispectral camera and the possibility to complement the conventional surgical view of the patient with functional information at a video rate of 25 Hz. To enable contrast agent-free ischemia monitoring during laparoscopic partial nephrectomy, we phrase the problem of ischemia detection as an out-of-distribution detection problem that does not rely on data from any other patient and uses an ensemble of invertible neural networks at its core. An in-human trial demonstrates the feasibility of our approach and highlights the potential of spectral imaging combined with advanced deep learning-based analysis tools for fast, efficient, reliable, and safe functional laparoscopic imaging.

Low level of antioxidant capacity biomarkers but not target overexpression predicts vulnerability to ROS-inducing drugs.

Despite a strong rationale for why cancer cells are susceptible to redox-targeting drugs, such drugs often face tumor resistance or dose-limiting toxicity in preclinical and clinical studies. An important reason is the lack of specific biomarkers to better select susceptible cancer entities and stratify patients. Using a large panel of lung cancer cell lines, we identified a set of "antioxidant-capacity" biomarkers (ACB), which were tightly repressed, partly by STAT3 and STAT5A/B in sensitive cells, rendering them susceptible to multiple redox-targeting and ferroptosis-inducing drugs. Contrary to expectation, constitutively low ACB expression was not associated with an increased steady state level of reactive oxygen species (ROS) but a high level of nitric oxide, which is required to sustain high replication rates. Using ACBs, we identified cancer entities with a high percentage of patients with favorable ACB expression pattern, making it likely that more responders to ROS-inducing drugs could be stratified for clinical trials.

Bovine meat and milk factor protein expression in tumor-free mucosa of colorectal cancer patients coincides with macrophages and might interfere with patient survival.

Bovine milk and meat factors (BMMFs) are plasmid-like DNA molecules isolated from bovine milk and serum, as well as the peritumor of colorectal cancer (CRC) patients. BMMFs have been proposed as zoonotic infectious agents and drivers of indirect carcinogenesis of CRC, inducing chronic tissue inflammation, radical formation and increased levels of DNA damage. Data on expression of BMMFs in large clinical cohorts to test an association with co-markers and clinical parameters were not previously available and were therefore assessed in this study. Tissue sections with paired tumor-adjacent mucosa and tumor tissues of CRC patients [individual cohorts and tissue microarrays (TMAs) (n = 246)], low-/high-grade dysplasia (LGD/HGD) and mucosa of healthy donors were used for immunohistochemical quantification of the expression of BMMF replication protein (Rep) and CD68/CD163 (macrophages) by co-immunofluorescence microscopy and immunohistochemical scoring (TMA). Rep was expressed in the tumor-adjacent mucosa of 99% of CRC patients (TMA), was histologically associated with CD68+ /CD163+ macrophages and was increased in CRC patients when compared to healthy controls. Tumor tissues showed only low stromal Rep expression. Rep was expressed in LGD and less in HGD but was strongly expressed in LGD/HGD-adjacent tissues. Albeit not reaching statistical significance, incidence curves for CRC-specific death were increased for higher Rep expression (TMA), with high tumor-adjacent Rep expression being linked to the highest incidence of death. BMMF Rep expression might represent a marker and early risk factor for CRC. The correlation between Rep and CD68 expression supports a previous hypothesis that BMMF-specific inflammatory regulations, including macrophages, are involved in the pathogenesis of CRC.