Staff Profile

@article{pmid37437883,

title = {Early Detection of Lung Cancer using small RNAs},

author = {Tobias Sikosek and Rastislav Horos and Franziska Trudzinski and Julia Jehn and Maurice Frank and Timothy Rajakumar and Laura V Klotz and Nathaniel Mercaldo and Mustafa Kahraman and Marco Heuvelman and Yasser Taha and Jennifer Gerwing and Jasmin Skottke and Alberto Daniel-Moreno and Marta Sanchez-Delgado and Sophie Bender and Christina Rudolf and Franziska Hinkfoth and Kaja Tikk and Judith Schenz and Markus A Weigand and Peter Feindt and Christian Schumann and Petros Christopoulos and Hauke Winter and Michael Kreuter and Marc A Schneider and Thomas Muley and Stephan Walterspacher and Martin Schuler and Kaid Darwiche and Christian Taube and Balazs Hegedus and Klaus F Rabe and Kimberly Rieger-Christ and Francine L Jacobsen and Clemens Aigner and Martin Reck and Alexander A Bankier and Amita Sharma and Bruno R Steinkraus},

doi = {10.1016/j.jtho.2023.07.005},

issn = {1556-1380},

year  = {2023},

date = {2023-07-01},

journal = {J Thorac Oncol},

abstract = {BACKGROUND: Lung cancer remains the deadliest cancer in the world and survival is heavily dependent on tumor stage at the time of detection. Low-dose computed tomography (LDCT) screening can significantly reduce mortality, however, annual screening is limited by low adherence in the USA and still not broadly implemented in Europe. As a result, <10% of lung cancers are detected through existing programs. Thus, there is great need for additional screening tests, such as a blood test that could be deployed in the primary care setting.nnMETHODS: We prospectively recruited 1,384 individuals meeting the NLST demographic eligibility criteria for lung cancer and collected stabilized whole blood to enable the pipetting-free collection of material, thus minimizing pre-analytical noise. Ultra-deep small RNA sequencing (20 million reads/sample) was performed with the addition of a method to remove highly abundant erythroid RNAs, and thus open bandwidth for the detection of less abundant species originating from plasma or the immune cellular compartment. We utilized 100 random data splits to train and evaluate an ensemble of logistic regression classifiers using small RNA expression of 943 individuals, discovered an 18-small RNA feature consensus signature (miLung), and validated this signature in an independent cohort (441 individuals). Blood cell sorting and tumor tissue sequencing were performed to deconvolve small RNAs into their source of origin.nnRESULTS: We generated diagnostic models and report a median ROC AUC of 0.86 (95% CI 0.84-0.86) in the discovery cohort, and generalized performance of 0.83 in the validation cohort. Diagnostic performance increased in a stage-dependent manner ranging from 0.73 (95% CI 0.71-0.76) for Stage I to 0.90 (95% CI 0.89-0.90) for Stage IV in the discovery cohort, and from 0.76 to 0.86 in the validation cohort. We identified a tumor-shed, plasma-bound ribosomal RNA fragment of the L1 stalk as a dominant predictor of lung cancer. The fragment is decreased following surgery with curative intent. In additional experiments, dried blood spot collection and sequencing demonstrated that small RNA analysis could potentially be conducted via home-sampling.nnCONCLUSION: These data suggest the potential of a small RNA-based blood test as a viable alternative to LDCT screening for early detection of smoking-associated lung cancer.},

keywords = {},

pubstate = {published},

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}

@article{pmid37142815,

title = {CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII},

author = {Burak Kocak and Bettina Baessler and Spyridon Bakas and Renato Cuocolo and Andrey Fedorov and Lena Maier-Hein and Nathaniel Mercaldo and Henning M\"{u}ller and Fanny Orlhac and Daniel Pinto Dos Santos and Arnaldo Stanzione and Lorenzo Ugga and Alex Zwanenburg},

doi = {10.1186/s13244-023-01415-8},

issn = {1869-4101},

year  = {2023},

date = {2023-05-01},

journal = {Insights Imaging},

volume = {14},

number = {1},

pages = {75},

abstract = {Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine clinical practice. The workflow of radiomics is complex due to several methodological steps and nuances, which often leads to inadequate reporting and evaluation, and poor reproducibility. Available reporting guidelines and checklists for artificial intelligence and predictive modeling include relevant good practices, but they are not tailored to radiomic research. There is a clear need for a complete radiomics checklist for study planning, manuscript writing, and evaluation during the review process to facilitate the repeatability and reproducibility of studies. We here present a documentation standard for radiomic research that can guide authors and reviewers. Our motivation is to improve the quality and reliability and, in turn, the reproducibility of radiomic research. We name the checklist CLEAR (CheckList for EvaluAtion of Radiomics research), to convey the idea of being more transparent. With its 58 items, the CLEAR checklist should be considered a standardization tool providing the minimum requirements for presenting clinical radiomics research. In addition to a dynamic online version of the checklist, a public repository has also been set up to allow the radiomics community to comment on the checklist items and adapt the checklist for future versions. Prepared and revised by an international group of experts using a modified Delphi method, we hope the CLEAR checklist will serve well as a single and complete scientific documentation tool for authors and reviewers to improve the radiomics literature.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid36913348,

title = {Incorporating algorithmic uncertainty into a clinical machine deep learning algorithm for urgent head CTs},

author = {Byung C Yoon and Stuart R Pomerantz and Nathaniel D Mercaldo and Swati Goyal and Eric M L'Italien and Michael H Lev and Karen A Buch and Bradley R Buchbinder and John W Chen and John Conklin and Rajiv Gupta and George J Hunter and Shahmir C Kamalian and Hillary R Kelly and Otto Rapalino and Sandra P Rincon and Javier M Romero and Julian He and Pamela W Schaefer and Synho Do and Ramon Gilberto Gonz\'{a}lez},

doi = {10.1371/journal.pone.0281900},

issn = {1932-6203},

year  = {2023},

date = {2023-03-13},

urldate = {2023-01-01},

journal = {PLoS One},

volume = {18},

number = {3},

pages = {e0281900},

abstract = {Machine learning (ML) algorithms to detect critical findings on head CTs may expedite patient management. Most ML algorithms for diagnostic imaging analysis utilize dichotomous classifications to determine whether a specific abnormality is present. However, imaging findings may be indeterminate, and algorithmic inferences may have substantial uncertainty. We incorporated awareness of uncertainty into an ML algorithm that detects intracranial hemorrhage or other urgent intracranial abnormalities and evaluated prospectively identified, 1000 consecutive noncontrast head CTs assigned to Emergency Department Neuroradiology for interpretation. The algorithm classified the scans into high (IC+) and low (IC-) probabilities for intracranial hemorrhage or other urgent abnormalities. All other cases were designated as No Prediction (NP) by the algorithm. The positive predictive value for IC+ cases (N = 103) was 0.91 (CI: 0.84-0.96), and the negative predictive value for IC- cases (N = 729) was 0.94 (0.91-0.96). Admission, neurosurgical intervention, and 30-day mortality rates for IC+ was 75% (63-84), 35% (24-47), and 10% (4-20), compared to 43% (40-47), 4% (3-6), and 3% (2-5) for IC-. There were 168 NP cases, of which 32% had intracranial hemorrhage or other urgent abnormalities, 31% had artifacts and postoperative changes, and 29% had no abnormalities. An ML algorithm incorporating uncertainty classified most head CTs into clinically relevant groups with high predictive values and may help accelerate the management of patients with intracranial hemorrhage or other urgent intracranial abnormalities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35999710,

title = {Assessing Bias and Limitations of Clinical Validation Studies of Molecular Diagnostic Tests for Indeterminate Thyroid Nodules: Systematic Review and Meta-Analysis},

author = {Catherine DiGennaro and Vahab Vahdat and Mohammad Jalali and Asmae Toumi and Tina Watson and G Scott Gazelle and Nathaniel Mercaldo and Carrie C Lubitz},

doi = {10.1089/thy.2022.0269},

issn = {1557-9077},

year  = {2022},

date = {2022-10-01},

urldate = {2022-09-26},

journal = {Thyroid},

volume = {32},

issue = {10},

pages = {1144-1157},

abstract = {BACKGROUND: Molecular tests for thyroid nodules with indeterminate fine needle aspiration results are increasingly used in clinical practice; however, true diagnostic summaries of these tests are unknown. A systematic review and meta-analysis were completed to (1) evaluate the accuracy of commercially available molecular tests for malignancy in indeterminate thyroid nodules and (2) quantify biases and limitations in studies which validate those tests.



METHODS: PubMed, EMBASE, and Web of Science were systematically searched through July 2021. English language articles that reported original clinical validation attempts of molecular tests for indeterminate thyroid nodules were included if they reported counts of true negative, true positive, false negative, and false positive results. We performed screening and full text review, followed by assessment of eight common biases and limitations, extraction of diagnostic and histopathological information, and meta-analysis of clinical validity using a bivariate linear mixed-effects model.



RESULTS: 49 studies were included. Meta-analysis of Afirma GEC (n=38 studies) revealed a sensitivity of 0.92 (95% CI: 0.90-0.94), specificity of 0.26 (0.20-0.32), negative likelihood ratio (LR-) of 0.32 (0.23-0.44), positive likelihood ratio (LR+) of 1.24 (1.15-1.35), and AUC of 0.83 (0.74-0.89). Afirma GSC (n=10) had a sensitivity of 0.94 (0.89-0.96), specificity of 0.38 (0.27-0.50), LR- of 0.18 (0.10-0.30), LR+ of 1.52 (1.28-1.87), and AUC of 0.91 (0.62-0.92). ThyroSeq v1 and v2 (n=10) had a sensitivity of 0.86 (0.82-0.90), specificity of 0.74 (0.59-0.85), LR- of 0.19 (0.13-0.26), LR+ of 3.52 (2.08-5.92), and AUC of 0.86 (0.81-0.90). ThyroSeq v3 (n=6) had a sensitivity of 0.92 (0.86-0.95), specificity of 0.41 (0.18-0.69), LR- of 0.24 (0.09-0.62), LR+ of 1.67 (1.09-2.98), and AUC of 0.90 (0.63-0.92). 14% of studies conducted blinded histopathologic review of excised thyroid nodules, and 8% made the decision to go to surgery blind to molecular test results.



CONCLUSIONS: Meta-analyses reveal high diagnostic accuracy of molecular tests for thyroid nodule assessment of malignancy risk; however, these studies are subject to several limitations. Limitations and their potential clinical impacts must be addressed and, when feasible, adjusted for using valid statistical methodologies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35880086,

title = {Brief Report: A Blood-Based MicroRNA Complementary Diagnostic Predicts Immunotherapy Efficacy in Advanced-Stage NSCLC With High Programmed Death-Ligand 1 Expression},

author = {Timothy Rajakumar and Rastislav Horos and Paul Kittner and Mustafa Kahraman and Tobias Sikosek and Franziska Hinkfoth and Kaja Tikk and Nathaniel D Mercaldo and Albrecht Stenzinger and Klaus F Rabe and Martin Reck and Michael Thomas and Petros Christopoulos and Bruno R Steinkraus},

doi = {10.1016/j.jtocrr.2022.100369},

issn = {2666-3643},

year  = {2022},

date = {2022-08-01},

urldate = {2022-08-01},

journal = {JTO Clin Res Rep},

volume = {3},

number = {8},

pages = {100369},

abstract = {Introduction: Patients with advanced, non-oncogene-driven NSCLC with high programmed death-ligand 1 (PD-L1) expression are eligible for treatment with immunotherapy. There is, however, an urgent medical need for biomarkers identifying cases that require additional combination with chemotherapy. We previously uncovered a myeloid-based 5-microRNA (5-miRNA) signature that identified responders to immunotherapy in PD-L1 unstratified patients; however, its potential utility in treatment guidance for patients with PD-L1 high tumors remained unclear.



Methods: We trained (n = 68) and validated (n = 56) a 5-miRNA multivariable Cox proportional hazards model predictive of overall survival on small RNA sequencing data of whole blood samples prospectively collected before the commencement of immunotherapy for stage IV NSCLC with PD-L1 tumor proportion score greater than or equal to 50%, treated with PD-1 inhibitor monotherapy (immunotherapy alone [IO]). Specificity was demonstrated in a control cohort treated with immunochemotherapy (ICT) (n = 31).



Results: The revised 5-miRNA risk score (miRisk) stratified IO-treated patients and identified a high-risk group with significantly shorter overall survival (hazard ratio = 5.24, 95% confidence interval: 2.17-12.66,  < 0.001). There was a significant interaction between the miRisk score and type of treatment (IO or ICT,  = 0.036), indicating that the miRisk score may serve as a predictive biomarker for immunotherapy response. Furthermore, the miRisk score could identify a group of high-risk patients who may benefit from treatment with ICT as opposed to IO (hazard ratio = 0.35, 95% confidence interval: 0.15-0.82,  = 0.018).



Conclusions: The miRisk score can distinguish a group of patients with PD-L1 high, stage IV NSCLC likely to benefit from adding chemotherapy to immunotherapy and may support treatment decisions as a blood-based complementary diagnostic.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35361874,

title = {A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy},

author = {Timothy Rajakumar and Rastislav Horos and Julia Jehn and Judith Schenz and Thomas Muley and Oana Pelea and Sarah Hofmann and Paul Kittner and Mustafa Kahraman and Marco Heuvelman and Tobias Sikosek and Jennifer Feufel and Jasmin Skottke and Dennis N\"{o}tzel and Franziska Hinkfoth and Kaja Tikk and Alberto Daniel-Moreno and Jessika Ceiler and Nathaniel Mercaldo and Florian Uhle and Sandra Uhle and Markus A Weigand and Mariam Elshiaty and Fabienne Lusky and Hannah Schindler and Quentin Ferry and Tatjana Sauka-Spengler and Qianxin Wu and Klaus F Rabe and Martin Reck and Michael Thomas and Petros Christopoulos and Bruno R Steinkraus},

doi = {10.1038/s41698-022-00262-y},

issn = {2397-768X},

year  = {2022},

date = {2022-03-01},

journal = {NPJ Precis Oncol},

volume = {6},

number = {1},

pages = {19},

abstract = {Immunotherapies have recently gained traction as highly effective therapies in a subset of late-stage cancers. Unfortunately, only a minority of patients experience the remarkable benefits of immunotherapies, whilst others fail to respond or even come to harm through immune-related adverse events. For immunotherapies within the PD-1/PD-L1 inhibitor class, patient stratification is currently performed using tumor (tissue-based) PD-L1 expression. However, PD-L1 is an accurate predictor of response in only ~30% of cases. There is pressing need for more accurate biomarkers for immunotherapy response prediction. We sought to identify peripheral blood biomarkers, predictive of response to immunotherapies against lung cancer, based on whole blood microRNA profiling. Using three well-characterized cohorts consisting of a total of 334 stage IV NSCLC patients, we have defined a 5 microRNA risk score (miRisk) that is predictive of overall survival following immunotherapy in training and independent validation (HR 2.40, 95% CI 1.37-4.19; P < 0.01) cohorts. We have traced the signature to a myeloid origin and performed miRNA target prediction to make a direct mechanistic link to the PD-L1 signaling pathway and PD-L1 itself. The miRisk score offers a potential blood-based companion diagnostic for immunotherapy that outperforms tissue-based PD-L1 staining.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Weinstein2021,

title = {Is Resident-Driven Inpatient Care More Expensive? Challenging a Long-Held Assumption.},

author = {Debra F Weinstein and Jin Choi and Nathaniel Mercaldo and Natalie Stump and Molly L Paras and Rhodes A Berube and Chin Hur},

url = {https://pubmed.ncbi.nlm.nih.gov/33496432/},

doi = {10.1097/ACM.0000000000003939},

issn = {1938-808X},

year  = {2021},

date = {2021-08-01},

urldate = {2021-01-01},

journal = {Academic medicine : journal of the Association of American Medical Colleges},

volume = {96},

number = {1205-1212},

issue = {8},

abstract = {The financial impact of graduate medical education (GME) on teaching hospitals remains poorly understood, while calls for increased federal support continue alongside legislative threats to reduce funding. Despite studies suggesting that residents are more "economical" than alternative providers, GME is widely believed to be an expensive investment. Assumptions that residents increase the cost of patient care have persisted in the absence of emerging evidence to the contrary. Thus, the authors sought to examine resident influence on patient care costs by comparing costs between a resident-driven service (RS) and a nonresident-covered service (NRS), with attention to clinical outcomes and how potential cost differences relate to the utilization of resources, length of stay (LOS), and other factors. This prospective study compared costs and clinical outcomes of internal medicine patients admitted to a RS versus an NRS at Massachusetts General Hospital (July 1, 2016-June 30, 2017). Total variable direct costs of inpatient admission was the primary outcome measure. LOS; 30-day readmission rate; utilization related to diagnostic radiology, pharmaceuticals, and clinical labs; and other outcome measures were also compared. Linear regression models quantified the relationship between log-transformed variable direct costs and service. Baseline characteristics of 5,448 patients on the 2 services (3,250 on a RS and 2,198 on an NRS) were similar. On a RS, patient care costs were slightly less and LOS was slightly shorter than on an NRS, with no significant differences in hospital mortality or 30-day readmission rate detected. Resource utilization was comparable between the services. These findings undermine long-held assumptions that residents increase the cost of patient care. Though not generalizable to ambulatory settings or other specialties, this study can help inform hospital decision-making around sponsorship of GME programs, especially if federal funding for GME remains capped or is subject to additional reductions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{EFrenk2021,

title = {Small-diameter TIPS combined with splenic artery embolization in the management of refractory ascites in cirrhotic patients.},

author = {Nathan E Frenk and Teodora Bochnakova and Suvranu Ganguli and Nathaniel Mercaldo and Andrew S Allegretti and Daniel S Pratt and Kei Yamada},

url = {https://pubmed.ncbi.nlm.nih.gov/33517259/},

doi = {10.5152/dir.2021.19530},

issn = {1305-3612},

year  = {2021},

date = {2021-03-01},

journal = {Diagnostic and interventional radiology (Ankara, Turkey)},

volume = {27},

number = {2},

pages = {232-237},

abstract = {Maximally decreasing portal pressures with transjugular intrahepatic portosystemic shunt (TIPS) is associated with improved ascites control but also increased encephalopathy incidence. Since splenic venous flow contributes to portal hypertension, we assessed if combining small-diameter TIPS with splenic artery embolization could improve ascites while minimizing encephalopathy. Fifty-five patients underwent TIPS creation for refractory ascites. Subjects underwent creation of 8 mm TIPS followed by proximal splenic artery embolization (group},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Duan2020,

title = {Differentiation of regenerative nodule, dysplastic nodule, and small hepatocellular carcinoma in cirrhotic patients: a contrast-enhanced ultrasound-based multivariable model analysis.},

author = {Yu Duan and Xiaoyan Xie and Qian Li and Nathaniel Mercaldo and Anthony E Samir and Ming Kuang and Manxia Lin},

url = {https://pubmed.ncbi.nlm.nih.gov/32307563/},

doi = {10.1007/s00330-020-06834-5},

issn = {1432-1084},

year  = {2020},

date = {2020-09-01},

journal = {European radiology},

volume = {30},

pages = {4741--4751},

abstract = {To develop a contrast-enhanced ultrasound (CEUS)-based model for differentiating cirrhotic liver lesions and for active surveillance of hepatocellular carcinoma (HCC). Patients with focal liver lesions (FLLs) with biopsy/resection-proven pathology and pre-procedure CEUS were enrolled from our institution between January 2011 and November 2014. Univariable and multivariable regression models were constructed using qualitative CEUS features and/or contrast arrival time ratio (CAT ). The optimism-adjusted Harrell's generalized concordance index (C ) was used to quantify the discriminatory ability of each CEUS feature and model. A total of 149 patients (113 men and 36 women) with 162 FLLs were enrolled with mean age 53.4 ± 12.7 years. A 0.1-unit reduction in CAT was associated with a 68% increase in the odds of having a higher nodule ranking (RN < DN < small HCC) (OR, 0.32; 95% CI, 0.20-0.50, p < .001). Arterial phase hypoenhancement and isoenhancement were inversely associated with a higher nodule ranking compared to hyperenhancement. Late-phase isoenhancement was associated with lower odds of a higher nodule ranking. The CEUS + CAT model (C 0.92, 0.89-0.95) provided greater discriminatory ability when compared to the CAT model (ΔC 0.09, 0.04-0.13, p < .001) and the CEUS model (ΔC 0.03, 0.01-0.0},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhou2020,

title = {Advanced MRI of Liver Fibrosis and Treatment Response in a Rat Model of Nonalcoholic Steatohepatitis.},

author = {Iris Y Zhou and Veronica Clavijo Jordan and Nicholas J Rotile and Eman Akam and Smitha Krishnan and Gunisha Arora and Hema Krishnan and Hannah Slattery and Noah Warner and Nathaniel Mercaldo and Christian T Farrar and Jeremy Wellen and Robert Martinez and Franklin Schlerman and Kenneth K Tanabe and Bryan C Fuchs and Peter Caravan},

url = {https://pubmed.ncbi.nlm.nih.gov/32343209/},

doi = {10.1148/radiol.2020192118},

issn = {1527-1315},

year  = {2020},

date = {2020-07-01},

journal = {Radiology},

volume = {296},

pages = {67--75},

abstract = {Background Liver biopsy is the reference standard to diagnose nonalcoholic steatohepatitis (NASH) but is invasive with potential complications. Purpose To evaluate molecular MRI with type 1 collagen-specific probe EP-3533 and allysine-targeted fibrogenesis probe Gd-Hyd, MR elastography, and native T1 to characterize fibrosis and to assess treatment response in a rat model of NASH. Materials and Methods MRI was performed prospectively (June-November 2018) in six groups of male Wistar rats age- and weight-matched animals received standard chow ( = 12 per group); received choline-deficient, l-amino acid-defined, high-fat diet (CDAHFD) for 6 weeks or 9 weeks ( = 8 per group); were fed 6 weeks of CDAHFD and switched to standard chow for 3 weeks ( = 12); were fed CDAHFD for 9 weeks with daily treatment of elafibranor beginning at week 6 ( = 14). Differences in imaging measurements and tissue analyses among groups were tested with one-way analysis of variance. The ability of each imaging measurement to stage fibrosis was quantified by using area under the receiver operating characteristic curve (AUC) with quantitative digital pathology (collagen proportionate area [CPA]) as reference standard. Optimal cutoff values for distinguishing advanced fibrosis were used to assess treatment response. Results AUC for distinguishing fibrotic (CPA >4.8%) from nonfibrotic (CPA ≤4.8%) livers was 0.95 (95% confidence interval [CI]: 0.91, 1.00) for EP-3533, followed by native T1, Gd-Hyd, and MR elastography with AUCs of 0.90 (95% CI: 0.83, 0.98), 0.84 (95% CI: 0.74, 0.95), and 0.65 (95% CI: 0.51, 0.79), respectively. AUCs for discriminating advanced fibrosis (CPA >10.3%) were 0.86 (95% CI: 0.76, 0.97), 0.96 (95% CI: 0.90, 1.01), 0.84 (95% CI: 0.70, 0.98), and 0.74 (95% CI: 0.63, 0.86) for EP-3533, Gd-Hyd, MR elastography, and native T1, respectively. Gd-Hyd MRI had the highest accuracy (24 of 26, 92%; 95% CI: 75%, 99%) in identifying responders and nonresponders in the treated groups compared with MR elastography (23 of 26, 88%; 95% CI: 70%, 98%), EP-3533 (20 of 26, 77%; 95% CI: 56%, 91%), and native T1 (14 of 26, 54%; 95% CI: 33%, 73%). Conclusion Collagen-targeted molecular MRI most accurately detected early onset of fibrosis, whereas the fibrogenesis probe Gd-Hyd proved most accurate for detecting treatment response. © RSNA, 2020},

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pubstate = {published},

tppubtype = {article}

}