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The prognostic role of circulating tumor cells (CTCs) has been clearly demonstrated in many types of cancer. However, their roles in diagnostic and treatment strategies remain to be defined. In this review, we present an overview of the current clinical validity of CTCs in nonmetastatic and metastatic cancer, and the main studies or concepts investigating the clinical utility of CTCs. In particular, we focus on breast, lung, colorectal, and prostate cancer. Two major topics concerning the clinical utility of CTC are discussed: treatment based on CTC count or CTC variations, and treatment based on the molecular characteristics of CTCs. Although some of these studies are inconclusive, many are still ongoing, and their results could help to define the role of CTCs in the management of cancers. A summary of published or ongoing phase II‐III trials is also presented. This review article discusses the clinical utility of circulating tumor cells (CTCs) in various settings of screening and treatment of non‐metastatic and metastatic tumors. Two major aspects are considered, namely i) treatment based on CTC count or CTC variations, and ii) treatment based on the molecular characteristics of CTCs. A summary of published or ongoing phase II‐III trials with CTCs is also presented.

Circulating tumor cells (CTCs) are rare tumor cells and have been investigated as diagnostic, prognostic and predictive biomarkers in many types of cancer. Although CTCs are not currently used in clinical practice, CTC studies have accumulated a high level of clinical validity, especially in breast, lung, prostate and colorectal cancers. In this review, we present an overview of the current clinical validity of CTCs in metastatic and non-metastatic disease, and the main concepts and studies investigating the clinical utility of CTCs. In particular, this review will focus on breast, lung, colorectal and prostate cancer. Three major topics concerning the clinical utility of CTC are discussed--(1) treatment based on CTCs used as liquid biopsy, (2) treatment based on CTC count or CTC variations, and (3) treatment based on CTC biomarker expression. A summary of published or ongoing phase II and III trials is also presented.

Despite the rapid uptake of multigene panel testing (MGPT) for hereditary cancer predisposition, there is limited guidance surrounding indications for testing and genes to include. To inform the clinical approach to hereditary cancer MGPT, we comprehensively evaluated 32 cancer predisposition genes by assessing phenotype-specific pathogenic variant (PV) frequencies, cancer risk associations, and performance of genetic testing criteria in a cohort of 165,000 patients referred for MGPT. We identified extensive genetic heterogeneity surrounding predisposition to cancer types commonly referred for germline testing (breast, ovarian, colorectal, uterine/endometrial, pancreatic, and melanoma). PV frequencies were highest among patients with ovarian cancer (13.8%) and lowest among patients with melanoma (8.1%). Fewer than half of PVs identified in patients meeting testing criteria for only BRCA1/2 or only Lynch syndrome occurred in the respective genes (33.1% and 46.2%). In addition, 5.8% of patients with PVs in BRCA1/2 and 26.9% of patients with PVs in Lynch syndrome genes did not meet respective testing criteria. Opportunities to improve upon identification of patients at risk for hereditary cancer predisposition include revising BRCA1/2 and Lynch syndrome testing criteria to include additional clinically actionable genes with overlapping phenotypes and relaxing testing criteria for associated cancers.

BACKGROUND With the availability of disease‐modifying therapies for Alzheimer's disease (AD), it is important for clinicians to have tests to aid in AD diagnosis, especially when the presence of amyloid pathology is a criterion for receiving treatment. METHODS High‐throughput, mass spectrometry‐based assays were used to measure %p‐tau217 and amyloid beta (Aβ)42/40 ratio in blood samples from 583 individuals with suspected AD (53% positron emission tomography [PET] positive by Centiloid > 25). An algorithm (PrecivityAD2 test) was developed using these plasma biomarkers to identify brain amyloidosis by PET. RESULTS The area under the receiver operating characteristic curve (AUC‐ROC) for %p‐tau217 (0.94) was statistically significantly higher than that for p‐tau217 concentration (0.91). The AUC‐ROC for the PrecivityAD2 test output, the Amyloid Probability Score 2, was 0.94, yielding 88% agreement with amyloid PET. Diagnostic performance of the APS2 was similar by ethnicity, sex, age, and apoE4 status. DISCUSSION The PrecivityAD2 blood test showed strong clinical validity, with excellent agreement with brain amyloidosis by PET.

Background This scoping review systematically maps externally validated machine learning (ML)-based models in cancer patient care, quantifying their performance, and clinical utility, and examining relationships between models, cancer types, and clinical decisions. By synthesizing evidence, this study identifies, strengths, limitations, and areas requiring further research. Methods The review followed the Joanna Briggs Institute's methodology, Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, and the Population, Concept, and Context mnemonic. Searches were conducted across Embase, IEEE Xplore, PubMed, Scopus, and Web of Science (January 2014-September 2022), targeting English-language quantitative studies in Q1 journals (SciMago Journal and Country Ranking > 1) that used ML to evaluate clinical outcomes for human cancer patients with commonly available data. Eligible models required external validation, clinical utility assessment, and performance metric reporting. Studies involving genetics, synthetic patients, plants, or animals were excluded. Results were presented in tabular, graphical, and descriptive form. Results From 4023 deduplicated abstracts and 636 full-text reviews, 56 studies (2018–2022) met the inclusion criteria, covering diverse cancer types and applications. Convolutional neural networks were most prevalent, demonstrating high performance, followed by gradient- and decision tree-based algorithms. Other algorithms, though underrepresented, showed promise. Lung and digestive system cancers were most frequently studied, focusing on diagnosis and outcome predictions. Most studies were retrospective and multi-institutional, primarily using image-based data, followed by text-based and hybrid approaches. Clinical utility assessments involved 499 clinicians and 12 tools, indicating improved clinician performance with AI assistance and superior performance to standard clinical systems. Discussion Interest in ML-based clinical decision-making has grown in recent years alongside increased multi-institutional collaboration. However, small sample sizes likely impacted data quality and generalizability. Persistent challenges include limited international validation across ethnicities, inconsistent data sharing, disparities in validation metrics, and insufficient calibration reporting, hindering model comparison reliability. Conclusion Successful integration of ML in oncology decision-making requires standardized data and methodologies, larger sample sizes, greater transparency, and robust validation and clinical utility assessments. Other Financed by FCT—Fundação para a Ciência e a Tecnologia (Portugal, project LA/P/0063/2020, grant 2021.09040.BD) as part of CSS’s Ph.D. This work was not registered. Graphical Abstract A visual summary (graphical abstract) encapsulating the core findings and future directions of ML applications in oncology patient care.

Measures of stepping volume and rate are common outputs from wearable devices, such as accelerometers. It has been proposed that biomedical technologies, including accelerometers and their algorithms, should undergo rigorous verification as well as analytical and clinical validation to demonstrate that they are fit for purpose. The aim of this study was to use the V3 framework to assess the analytical and clinical validity of a wrist-worn measurement system of stepping volume and rate, formed by the GENEActiv accelerometer and GENEAcount step counting algorithm. The analytical validity was assessed by measuring the level of agreement between the wrist-worn system and a thigh-worn system (activPAL), the reference measure. The clinical validity was assessed by establishing the prospective association between the changes in stepping volume and rate with changes in physical function (SPPB score). The agreement of the thigh-worn reference system and the wrist-worn system was excellent for total daily steps (CCC = 0.88, 95% CI 0.83–0.91) and moderate for walking steps and faster-paced walking steps (CCC = 0.61, 95% CI 0.53–0.68 and 0.55, 95% CI 0.46–0.64, respectively). A higher number of total steps and faster paced-walking steps was consistently associated with better physical function. After 24 months, an increase of 1000 daily faster-paced walking steps was associated with a clinically meaningful increase in physical function (0.53 SPPB score, 95% CI 0.32–0.74). We have validated a digital susceptibility/risk biomarker—pfSTEP—that identifies an associated risk of low physical function in community-dwelling older adults using a wrist-worn accelerometer and its accompanying open-source step counting algorithm.

The 1983 discovery of a mouse monoclonal antibody—the Ki-67 antibody—that recognized a nuclear antigen present only in proliferating cells represented a seminal discovery for the pathologic assessment of cellular proliferation in breast cancer and other solid tumors. Cellular proliferation is a central determinant of prognosis and response to cytotoxic chemotherapy in patients with breast cancer, and since the discovery of the Ki-67 antibody, Ki-67 has evolved as an important biomarker with both prognostic and predictive potential in breast cancer. Although there is universal recognition among the international guideline recommendations of the value of Ki-67 in breast cancer, recommendations for the actual use of Ki-67 assays in the prognostic and predictive evaluation of breast cancer remain mixed, primarily due to the lack of assay standardization and inconsistent inter-observer and inter-laboratory reproducibility. The treatment of high-risk ER-positive/human epidermal growth factor receptor-2 (HER2) negative breast cancer with the recently FDA-approved drug abemaciclib relies on a quantitative assessment of Ki-67 expression in the treatment decision algorithm. This further reinforces the urgent need for standardization of Ki-67 antibody selection and staining interpretation, which will hopefully lead to multidisciplinary consensus on the use of Ki-67 as a prognostic and predictive marker in breast cancer. The goals of this review are to highlight the historical evolution of Ki-67 in breast cancer, summarize the present literature on Ki-67 in breast cancer, and discuss the evolving literature on the use of Ki-67 as a companion diagnostic biomarker in breast cancer, with consideration for the necessary changes required across pathology practices to help increase the reliability and widespread adoption of Ki-67 as a prognostic and predictive marker for breast cancer in clinical practice.

INTRODUCTION Well‐chosen biomarkers have the potential to increase the efficiency of clinical trials and drug discovery and should show good precision as well as clinical validity. METHODS We suggest measures that operationalize these criteria and describe a general approach that can be used for inference‐based comparisons of biomarker performance. The methods are applied to measures obtained from structural magnetic resonance imaging (MRI) from individuals with mild dementia (n = 70) or mild cognitive impairment (MCI; n = 303) enrolled in the Alzheimer's Disease Neuroimaging Initiative. RESULTS Ventricular volume and hippocampal volume showed the best precision in detecting change over time in both individuals with MCI and with dementia. Differences in clinical validity varied by group. DISCUSSION The methodology presented provides a standardized framework for comparison of biomarkers across modalities and across different methods used to generate similar measures and will help in the search for the most promising biomarkers. Highlights A framework for comparison of biomarkers on pre‐defined criteria is presented. Criteria for comparison include precision in capturing change and clinical validity. Ventricular volume has high precision in change for both dementia and mild cognitive impairment (MCI) trials. Imaging measures’ performance in clinical validity varies more for dementia than for MCI.

The Quality of Life (QoL) Utility measure, QLU-C10D, is derived of the European Organisation for Research and Treatment of Cancer (EORTC) QoL Questionnaire, QLQ-C30. Based on the cancer-specific nature, the QLU-C10D is expected to be sensitive and responsive in lung cancer patients.This retrospective analysis used data from four international lung cancer multi-center trials (NCT00656136, NCT00949650, NCT01085136, NCT01523587). Clinical validity was assessed in comparison to a generic standard utility instrument, the EuroQoL Group´s EQ-5D-3L. Utilities of six country value sets (Australia, Canada, Italy, the Netherlands, Poland, UK) were calculated at baseline and end of treatment for both measures. Country value set pairs of both measures (k) were compared in terms of Relative Efficiency (RE) and difference in Effect Sizes (dES) in 1) sensitivity to detect differences between performance status groups and 2) responsiveness to changes at each trial sample. Analysis of the four trials (N1 = 496, N2 = 290, N3 = 202, N4 = 770) with the six country value sets of each utility measure showed ad 1) Sensitivity indices favored the QLU-C10D (k = 18, p ≤ 0.019; RE > 1.10; dES > 0.03), and ad 2) Responsiveness indices of changes within clinically known groups (k = 78), largely favored QLU-C10D (k = 74, p ≤ .024; RE > 1.01; dES > 0.02), in comparison with the generic utility instrument. In summary, 96% of the comparative indices favored the QLU-C10D. In summary, this study confirms the clinical validity of the QLU-C10D in lung cancer patients. The QLU-C10D produced homogenous results across six country value sets and detected differences/changes in alignment with clinical expectations. In most comparisons the QLU-C10D was more sensitive or responsive compared to the EQ-5D-3L.

Purpose: Genomic sequencing (GS) for newborns may enable detection of conditions for which early knowledge can improve health outcomes. One of the major challenges hindering its broader application is the time it takes to assess the clinical relevance of detected variants and the genes they impact so that disease risk is reported appropriately. Methods: To facilitate rapid interpretation of GS results in newborns, we curated a catalog of genes with putative pediatric relevance for their validity based on the ClinGen clinical validity classification framework criteria, age of onset, penetrance, and mode of inheritance through systematic evaluation of published evidence. Based on these attributes, we classified genes to guide the return of results in the BabySeq Project, a randomized, controlled trial exploring the use of newborn GS (nGS), and used our curated list for the first 15 newborns sequenced in this project. Results: Here, we present our curated list for 1,514 gene–disease associations. Overall, 954 genes met our criteria for return in nGS. This reference list eliminated manual assessment for 41% of rare variants identified in 15 newborns. Conclusion: Our list provides a resource that can assist in guiding the interpretive scope of clinical GS for newborns and potentially other populations. Genet Med advance online publication 12 January 2017