JNCI Cancer Spectrum

Population-based mammography screening improves breast cancer outcomes, but its impact on real-world treatment pathways and quality indicators (QIs) remains incompletely described. We conducted a retrospective nationwide cohort study using linked data from the Czech National Cancer Registry and the National Registry of Reimbursed Health Services. Women aged [≥]18 years with a first breast cancer diagnosis between 2017 and 2024 were classified as screen-detected (SCR) or diagnostically-detected (DIG) according to the imaging modality preceding histological verification. Outcomes included stage distribution, untreated cases, first-line treatment, main treatment modality, time to treatment, multidisciplinary team discussion (MDT), centralization to Comprehensive Cancer Centres (COCs), and survival patterns. The verified cohort included 47,648 women: 26,817 SCR cases (56.3 %) and 20,831 DIG cases (43.7 %). In this nationwide analysis, SCR breast cancer was associated with earlier stage at diagnosis and better survival patterns, but also with longer time to treatment and longer time to MDT discussion than DIG-detected disease. Although treatment rates were high and centralization improved over time, substantial regional variation persisted in care pathways, MDT use, and access to COCs. These findings support continued strengthening of screening participation, monitoring of care intervals, and quality assurance of MDT reporting and regional oncology care delivery.

ABSTRACT Background: Despite its high prevalence and established impact on women's health, the long-term biological effects of Intimate Partner Violence (IPV) remain poorly understood. In particular, its potential role in increasing cancer risk has received limited attention. This review examines whether IPV may be associated with elevated cancer risk in women. Methods: We conducted a systematic review and meta-analysis in accordance with PRISMA and MOOSE guidelines to evaluate whether IPV may be associated with cancer risk. Eligible studies included adult women ([≥]18 years) with documented IPV exposure and cancer or precancerous outcomes. We searched PubMed, Web of Science, Scopus, and Google Scholar for articles published from 2000 to 2025. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). A random-effects meta-analysis was performed on longitudinal studies reporting adjusted risk estimates. Results: Thirteen studies were included in the qualitative synthesis, but only two met criteria for meta-analysis, both reporting on cervical cancer. The pooled odds ratio was 3.00 (95% CI: 2.05 - 4.38; I2 = 0%). A separate pooled prevalence analysis of six retrospective studies showed that 32.2% of women with cancer reported a lifetime history of IPV. Study quality ranged from low to high. Conclusions: This review underscores the limited and heterogeneous nature of the existing evidence on IPV as a potential cancer risk factor. While preliminary findings suggest a possible association, particularly with cervical cancer, the scarcity of high-quality longitudinal studies and the methodological variability in the studies reviewed prevent definitive conclusions regarding causal linkage. Further research, particularly prospective and mechanistic studies, is needed to clarify the relationship between IPV and oncogenesis across different cancer types and to identify underlying biological pathways.

This review evaluates the biological pathways linking soft drink consumption with the risk of several cancers within the framework of the Global Cancer Update Programme (CUP Global). Soft drink consumption has been associated with increased risk of multiple cancers, and glucose or insulin dysregulation has been proposed as a potential underlying mechanism. We applied a three-stage framework. In the first stage, we identified insulin sensitivity as the key biological process potentially linking soft drink consumption (sugar-sweetened or artificially sweetened) to cancer risk, with glucose-related and insulin-related biomarkers as potential intermediate phenotypes, using a combination of expert knowledge and a web-based text mining tool. In the second stage, we conducted targeted PubMed searches to identify studies examining associations between consumption of soft drinks and these intermediate phenotypes (IPs) and between these IPs and the risk of several cancers in adult humans. In the third stage, the evidence was evaluated by the Expert Committee on Cancer Mechanisms (MEC), who assessed the strength of the evidence for these associations. The MEC concluded that there was weak evidence supporting a role of glucose or insulin-related processes as a potential mechanistic pathway linking the consumption of sugar-sweetened or artificially sweetened beverages to the risk of various cancers evaluated.

Tumor-infiltrating lymphocytes (TILs) are widely used to assess antitumor immunity in breast cancer but may not reflect the functional competence of adaptive immune responses. We show that immune organization, reflected by tertiary lymphoid structures (TLS) and coordinated humoral and cellular immune programs, represents a distinct dimension of tumor immunity beyond lymphocyte abundance. By integrating histologic, transcriptomic, spatial, and immune receptor profiling analyses across multiple breast cancer cohorts, we show that immune organization is associated with greater immune repertoire diversity, evidence of therapy-induced clonal selection, and improved clinical outcomes, independent of immune infiltration. Transcriptomic measures of immune organization retained independent prognostic value across external cohorts, whereas measures of immune infiltration did not. Furthermore, treatment-induced increases in immune organization, but not immune infiltration, were associated with therapeutic response. These findings identify immune organization as a dynamic and clinically measurable state of adaptive antitumor immunity with implications for prognosis, treatment monitoring, and therapeutic development in breast cancer.

Purpose: Nonresponse to routinely collected patient-reported outcome measures (PROMs) threatens the representativeness of aggregated data. We characterized patient-, provider-, and clinic-level factors associated with PROMIS Global-10 nonresponse in routine radiation oncology care. Methods: In this retrospective cohort study, all adults seen at five Mass General Brigham radiation oncology clinics over one year were included. The primary outcome was patient-level nonresponse, defined as never completing the portal-administered Global-10 versus completing it at least once. Using iterative mixed-effects logistic regression, we modeled patient-, provider-, and clinic-level factors. Results: Among 12,214 patients, 71 providers, and five clinics, patient- and appointment-level response rates were 35.4% and 10.9%, with patient-level response ranging nearly fivefold across clinics (12.8% to 66.2%). In Model 1, male sex, lower education, not working, and recent surgery had higher odds of nonresponse, and longer time since diagnosis lower odds. After provider- and clinic-level factors were added, patient sex, education, and employment became nonsignificant, whereas recent surgery (adjusted odds ratio [aOR] 1.97) and longer time since diagnosis (aOR 0.46 for >12 months) persisted. A provider's historical collection rate was protective but attenuated at the clinic level. There, a later program launch (aOR 0.29) and higher historical collection rate (aOR 0.79) correlated with lower nonresponse, whereas academic versus community setting did not. Conclusions: Nonresponse to routinely collected PROMs is a multilevel phenomenon driven substantially by clinic-level implementation factors, not patient characteristics alone. Because response rate is only a proxy for representativeness, PROMs programs and PRO-based performance measures should prioritize representative collection over volume.

Aim: To assess temporal trends in incidence and mortality and project the future burden of five major gastrointestinal cancers in Jiangsu Province, China. Methods: Population-based cancer registry data from Jiangsu Province between 2010 and 2021 were used to analyze the burden of esophageal, gastric, colon, rectal, and liver cancers. Age-standardized incidence and mortality rates were calculated and compared by cancer type, sex, and urban-rural residence. Joinpoint regression was used to estimate annual percentage changes (APC) and average annual percentage changes (AAPC). The APC from the most recent Joinpoint segment was used to project incidence and mortality rates to 2030. Results: In 2021, gastric cancer had the highest age-standardized incidence and mortality among the five cancers. Incidence and mortality were consistently higher in males than in females and increased markedly after 50 years of age. From 2010 to 2021, age-standardized incidence and mortality declined for esophageal, gastric, and liver cancer, but increased for colon and rectal cancer. Colon cancer showed the steepest increase in both incidence and mortality. Rural areas experienced faster increases in colon and rectal cancer burden than urban areas. Projections to 2030 suggest continued declines in esophageal, gastric, and liver cancer, while colon cancer incidence and mortality are expected to rise further. Conclusion: Jiangsu Province is experiencing a transition in gastrointestinal cancer burden, with continued declines in esophageal, gastric, and liver cancers but an emerging and growing burden of colorectal cancer, especially colon cancer. Prevention strategies should focus on expanding colorectal cancer screening and early diagnosis, particularly in rural areas, while sustaining control of esophageal, gastric, and liver cancers.

We aimed to assess the burden and correlates of late diagnosis of cervical cancer among women attending care at a public health facility in Coastal Kenya. A retrospective cross-sectional design was used. Data extracted from medical records of women already diagnosed with cervical cancer at Kilifi County Referral Hospital (KCRH) were used. Logistic regression was used to assess independent predictors of late diagnosis of cervical cancer. A total of 126 patients were included in this analysis. Out of the 126 participants, 67.5% (n=85) were 50 years and older with a mean age of 54 years. Among the 126 participants whose records were included in analysis, 73.0% (n=92; [95% CI: 64.3 - 80.5]) were diagnosed with late-stage cervical cancer. In multivariable analysis, none of the variables included in the model showed an association with the outcome. In conclusion, we found a high burden of late diagnosis of cervical cancer in this setting. Targeted interventions are warranted to reverse this trend.

Cutaneous squamous cell carcinoma (cSCC) is the second most common form of cancer worldwide. While most cSCCs are not life-threatening, 2-5% of patients develop metastases. To better understand what causes some cSCCs to progress to metastatic disease, we assembled a nationwide cohort of 19,120 patients with clinico-pathologically annotated tumors linked to metastatic outcome. RNA-sequencing was performed on 378 tumors, and whole-exome sequencing on 147, with balanced numbers of tumors that progressed to metastatic disease (cases) and did not (controls). UV radiation was the dominant mutational signature with additional contributions from aging, APOBEC activity, and, in immunosuppressed patients, azathioprine exposure. We identified 38 genes under selection across a core set of signaling pathways. Gene expression clusters were primarily associated with the differentiation state of tumor cells and secondarily with the composition of the tumor microenvironment. Several mutational and transcriptional programs were associated with metastasis, including a dedifferentiated gene expression signature, activating mutations in the RAS signaling pathway, loss-of-function alterations in the SWI/SNF chromatin remodeling complex, and specific arm-level copy number alterations. A 23-gene expression signature was built to predict metastasis from primary cSCC tissue. The signature was validated in two independent cohorts (N=102 and 52), where it predicted metastasis independently of staging systems. Together, these findings provide the most detailed molecular portrait of cSCC to date and establish an assay for risk stratification suitable for clinical implementation.

Objective To develop and validate a risk model for predicting postoperative bleeding in patients with thyroid cancer. Methods A total of 2800 consecutive patients diagnosed with thyroid cancer in the Department of Thyroid and Breast Surgery of the Affiliated Hospital of Xuzhou Medical University between January 2020 and December 2023 were retrospectively analyzed. Patients were categorized into two groups based on postoperative bleeding occurrence: bleeding and non-bleeding groups. Univariate and multivariate logistic regression analyses were utilized to screen independent risk factors. Meanwhile, risk prediction models were developed and nomogram . Subgroup analysis was performed to identify independent risk factors. The predictive effects of the models were assessed using the Hosmer-Lemeshow test and receiver operating characteristic (ROC) curves. Results Of the 2800 recruited patients, 50 had postoperative bleeding, with an incidence rate of 1.7%. Multivariate logistic regression analysis showed that age, hypertension, total thyroidectomy, tumor size [&ge;]4 cm, and operation time [&ge;]90 min were the risk factors for postoperative bleeding in thyroid cancer patients (P<0.05). A risk prediction model was established based on the above factors, and the area under the ROC curve was 0.881, with a sensitivity of 94.0%, a specificity of 67.3%, and an accuracy of 74.0%. Decision curve analysis revealed that the model had good predictive ability. Conclusions The constructed risk prediction model has good predictive power and can provide a reference for healthcare professionals to predict the risk of bleeding in patients after thyroid cancer surgery.

Introduction: This study aimed to provide population level survival trends during the era in which new systemic therapies transformed treatment guidelines for metastatic cancer, as well as insights on the real world use of these treatments and associated survival. Methods: Adults diagnosed with synchronous metastatic solid cancer in 2008 until 2022 (22 cancer types) were identified from the Netherlands Cancer Registry. Median overall survival (OS) was assessed by five year diagnostic period. For 2018 until 2022, systemic therapy use in any treatment line was analyzed and survival percentiles within treatment and cancer types were estimated with Kaplan Meier survival analyses. Results: Median OS in the overall cohort (n=280,419 patients) improved from 6 to 8 months between the period 2008 until 2012 and 2018 until 2022. Among patients diagnosed in 2018 until 2022, 15% received immunotherapy, 15% targeted therapy, 29% chemotherapy and/or traditional hormone therapy only, and 39% no systemic therapy. In some cancer types, a relatively large proportion of treated patients had longterm survival (e.g., immunotherapy in melanoma: p50 = 67 months). Other cancer types had a smaller subset of treated patients (p10 and p25) with substantially better outcomes than the median (e.g., targeted therapy in NSCLC: p50 = 22 months, p10 = 96 months). Conclusion: Population level survival for patients with synchronous metastatic solid cancer has modestly improved over time. The marked survival heterogeneity within cancer and treatment types highlights both the potential and uncertainty associated with (novel) treatments. Improved prediction of treatment effects and clear communication regarding survival expectation remain critical. Presenting multiple survival scenarios over median survival alone can support decision making.

Background: Changes in the extracellular matrix (ECM) are a recognised feature of aggressive prostate cancer, but they are not exploited in clinical decision-making. We aimed to develop automated quantitative ECM parameters to facilitate risk stratification for localised prostate cancer. Methods: 378 quantitative ECM parameters were derived from picrosirius red-stained diagnostic prostate biopsies in a cohort of 422 patients, matched 1:1 for recurrence, recruited to the CHHiP (Conventional or Hypofractionated High Dose Intensity Modulated Radiotherapy in Prostate Cancer) trial of radiotherapy fractionation for localised prostate cancer. These ECM parameters comprehensively described fibre architecture, gaps and ECM texture. Machine learning models at the level of both individual image tiles and patients defined how ECM parameters related to tumour versus normal prostate, Gleason grade group and recurrence. Shapley analysis was used to interpret ECM feature importance and develop signatures associated with recurrence. Results: Specific ECM patterns identified tumour versus normal prostate, Gleason pattern 4 versus 3 and recurrence. ECM patterns associated with recurrence were enriched in Gleason 4+3 patients, versus Gleason 3+4 patients. Shapley analysis revealed that biopsies from patients with recurrence had smaller more elongated gaps between fibres, with finer grained ECM texture and lower ECM homogeneity than less recurrent regions. Interpretation: Quantitative automated analysis of ECM architecture can inform probability of prostate cancer recurrence after radiotherapy; Features relating to ECM gap size and texture are of particular relevance.

Background: Glioblastoma remains resistant to most immune-based therapies. Surgery may create a perioperative window in which systemic immune activation and tumor antigen release intersect. We evaluated whether COVID-19 vaccination shortly before first glioblastoma surgery was associated with survival. Methods: We performed a retrospective single-center cohort study of adults with newly diagnosed glioblastoma undergoing initial biopsy or resection from 2021 to 2025. The primary exposure was documented COVID-19 vaccination within 100 days before first tumor surgery. Overall survival was analyzed from surgery using Kaplan-Meier and Cox models, with 1:1 propensity matching and sensitivity analyses addressing treatment completion, calendar time, surgical selection, steroid exposure, immune-cell variables, COVID severity, and negative-control vaccination. Results: The cohort included 187 patients: 64 perioperatively vaccinated and 123 non-perioperative comparators. Among vaccinated patients, 59/64 (92.2%) received mRNA vaccines; median vaccination-to-surgery interval was 81 days (IQR 71-90). Median overall survival was 743 days in vaccinated patients versus 318 days in comparators (unmatched HR 0.48, 95% CI 0.30-0.76; p=0.002). After 1:1 matching, median survival was 743 versus 349 days (HR 0.52, 95% CI 0.34-0.80). Sensitivity analyses accounting for adjuvant therapy, surgery year, extent of resection, steroid exposure, immune-cell measures, and COVID hospitalization were directionally consistent. Influenza vaccination was not associated with survival. Conclusions: COVID-19 vaccination within 100 days before first glioblastoma surgery was associated with longer overall survival. These findings identify perioperative vaccination timing as a potentially relevant and modifiable variable in glioblastoma outcomes.

Background: Early-onset colorectal cancer (EOCRC) is increasing globally, particularly among Hispanic/Latino (H/L) populations, yet the contribution of tumor-colonizing microbiota to age-associated colorectal cancer (CRC) biology remains poorly understood. Most microbiome studies have focused on fecal communities or non-Hispanic populations, leaving the intratumoral microbial landscape of H/L patients largely unexplored. Methods: We performed an exploratory characterization of tumor-colonizing microbiota using whole-exome sequencing (WES) data from four primary colorectal tumors obtained from H/L patients treated at City of Hope, including two EOCRC (<50 years) and two late-onset colorectal cancer (LOCRC; [&ge;]50 years) cases. Following removal of host-derived sequences, microbial taxonomic profiling was conducted at the family, genus, and species levels, and microbial metabolic pathways were inferred. Clinical and pathological data were integrated to evaluate age-associated differences in microbial composition and predicted function. Results: Family-, genus-, and species-level analyses consistently demonstrated greater microbial diversity in LOCRC than EOCRC. LOCRC contained more than twice the number of unique bacterial families, nearly three times as many unique genera, and more than twice as many unique bacterial species. A conserved core microbiota, including Fusobacteriaceae, Prevotellaceae, Fusobacterium, and Prevotella, was identified across both age groups, whereas LOCRC was enriched in CRC-associated taxa including Fusobacterium nucleatum, Bacteroides fragilis, Parvimonas micra, Porphyromonas asaccharolytica, and Dialister pneumosintes. Species-level analyses revealed only a single shared bacterial species between EOCRC and LOCRC, indicating progressive microbial divergence with increasing taxonomic resolution. In contrast, functional profiling identified 11 predicted microbial metabolic pathways, of which nine were shared between age groups, two were unique to EOCRC, and none were exclusive to LOCRC. Core metabolic pathways involved in energy metabolism, amino acid biosynthesis, phospholipid metabolism, and central carbon metabolism exhibited comparable abundance across both groups, demonstrating substantial functional conservation despite pronounced taxonomic differences. Conclusions: Tumor-colonizing microbiota differ markedly between EOCRC and LOCRC in H/L patients, with late-onset tumors exhibiting substantially greater microbial richness and taxonomic complexity. Despite these compositional differences, microbial metabolic functions remain largely conserved, supporting the concept of functional redundancy within the colorectal tumor microenvironment (TME). Although exploratory, this proof-of-concept study provides one of the first characterizations of intratumoral microbiota in H/L EOCRC and establishes a foundation for larger multi-omics investigations aimed at identifying microbiome-based biomarkers and therapeutic targets for precision oncology.

The therapeutic landscape for hepatocellular carcinoma (HCC) is evolving rapidly, necessitating scalable approaches to synthesize the expanding scientific literature. We characterized thematic shifts in HCC treatment and prognosis research by conducting a retrospective bibliometric analysis of influential publications from 2023 and 2024. Using the OpenAlex database, we identified the 50 most highly cited papers from each year based on eighteen-month post-publication citation counts. Large language models were deployed to extract, normalize, and classify concepts from unstructured text into canonical topics and parent themes, enabling quantitative year-over-year frequency comparisons. Analysis of these 100 papers revealed a distinct maturation in research focus. Although broad categories like general immunotherapy remained prevalent, their relative frequency declined in favor of specific dual immune checkpoint regimens, notably CTLA-4 inhibition and the durvalumab plus tremelimumab combination. Concurrently, parent themes related to radiomics, imaging, and health systems exhibited significant growth in the 2024 cohort. These findings demonstrate a thematic transition in high-impact HCC research from foundational immuno-oncology toward optimized combination therapies and precision diagnostics. Furthermore, this study highlights the utility of artificial intelligence-driven bibliometrics for objectively tracking dynamic conceptual shifts in oncology. A web interface for exploring the data is available at https://pri.pepkio.com/.

Background The clinical significance of clonal hematopoiesis of indeterminate potential (CHIP) in melanoma remains incompletely defined, particularly with respect to CHIP genotype, clone size, and somatic mutations (e.g BRAF mutations). We integrated human cohort data and a syngeneic melanoma mouse model to evaluate whether CHIP is associated with melanoma risk, tumor growth, and differential clinical outcomes. Methods We analyzed CHIP prevalence and survival in a large treatment-unselected melanoma cohort (n=2,480), evaluated tumor growth in a syngeneic BRAF-mutant (BRAFmut) melanoma murine model of TET2-CHIP and DNMT3A-CHIP, and assessed survival outcomes in an immune checkpoint inhibitor (ICI)-treated advanced melanoma cohort (n=361). Associations with progression-free survival (PFS) and overall survival (OS) were evaluated using Kaplan-Meier analyses and multivariable Cox proportional hazards models. Results CHIP was enriched among patients with treatment-unselected melanoma compared with age/sex-matched healthy controls, and larger CHIP clone size showed an age-adjusted association with inferior OS. In a syngeneic BRAFmut melanoma murine model, TET2-CHIP, but not DNMT3A-CHIP, was associated with significantly increased primary melanoma tumor growth. Among patients with ICI-treated advanced melanoma, CHIP was associated with worse OS compared with patients without CHIP. TET2-CHIP had the strongest adverse association with survival, whereas DNMT3A-CHIP was not significantly associated with PFS or OS. Conclusions CHIP is enriched in melanoma and exploratory analyses demonstrate genotype-specific differences in melanoma tumor growth and clinical outcomes. These findings support further investigation of genotype-specific CHIP profiling as a potential biomarker for melanoma risk stratification and immunotherapy outcomes.

Background: Cases requiring 13 or more tissue sections in Mohs micrographic surgery (MMS) demand extended operative time, additional resources, and often specialised closure techniques. Pre-operative identification of such cases would improve surgical scheduling, resource allocation, and patient counselling. We aimed to develop and validate a machine learning prediction tool using pre-operative clinical features to identify cases likely to require13 sections. Objectives: To develop and validate machine learning models for predicting which Mohs procedures will require 13 sections, using pre-operative clinical features, and to identify key predictive factors. Methods: We analysed 408 consecutive Mohs procedures with 16 pre-operative clinical variables. Thirty machine learning algorithms were evaluated, including ensemble methods (Stacking, Voting), gradient boosting (XGBoost, LightGBM, CatBoost), neural networks (3-7 layers), support vector machines, and traditional classifiers. Model performance was assessed using 5-fold stratified cross-validation and independent test set evaluation. Feature importance was determined using SHAP (SHapley Additive exPlanations) analysis. Results: The stacking ensemble achieved the highest cross-validation AUC of 0.891 (95% CI: 0.849-0.934) and test AUC of 0.884. Tumour area (cm2), calculated using the ellipse formula to approximate clinical tumour morphology, emerged as the strongest predictor (SHAP importance: 0.141), followed by tumour size dimensions (0.086 and 0.068), aggressive histopathology (0.046), and recurrence status (0.035). Wide neural network architectures (5-layer) outperformed deeper configurations (7-layer). The model demonstrated 70.7% high-confidence predictions with uncertainty <15%. Conclusions: Machine learning models using pre-operative clinical features can accurately predict which Mohs procedures will require 13 or more sections. The stacking ensemble approach provides robust predictions suitable for clinical decision support. External validation in multi-centre cohorts with diverse patient populations and practice patterns is warranted to assess model generalisability.

Background: Early detection of pancreatic ductal adenocarcinoma (PDAC) is critical. While faecal elastase-1 (FE-1) is a standard clinical marker for pancreatic function, its diagnostic accuracy for malignancy is limited. We sought to identify plasma metabolites that enhance FE-1 performance in symptomatic "at-risk" patients. Methods: Using the DEPEND cohort (CRUK C45617/A29908), plasma metabolomics was performed on patients with resectable PDAC (n=23) and healthy volunteers (n=24). Predictive modelling included feature selection and cross-validation, with further validation in an independent external cohort. Results: Citrulline was identified as significantly depleted in PDAC patients across discovery and validation cohorts. In isolation, Citrulline achieved an AUC of 0.86 (internal) and 0.88 (external validation). Standalone FE-1 demonstrated an AUC of 0.67. However, combining Citrulline and FE-1 significantly improved diagnostic performance, achieving a combined AUC of 0.96. Stratification revealed distinct metabolomic signatures associated with poorly differentiated tumours, suggesting a link to histological grade. Conclusions: Integrating Citrulline with FE-1 testing substantially improves PDAC detection in symptomatic patients. This non-invasive panel offers high diagnostic potential, though prospective validation is required to establish clinical cut-offs for routine practice.

Background with objective: Detecting EGFR mutations is critical for treating lung adenocarcinoma with highly effective targeted therapies. However, standard genetic testing is expensive, complex, and often unavailable in resource-limited settings like Bangladesh. Because elevated serum CEA has been linked to these genetic alterations, it could serve as an accessible screening tool. This study aims to evaluate the association between serum CEA levels and EGFR mutation status to determine if routine CEA testing can reliably predict these mutations and guide treatment. Methodology: In this cross-sectional analytical study, we recruited 58 patients with histologically confirmed treatment naive lung adenocarcinoma. The presence of EGFR mutations in the ctDNA was determined via ARMS (Amplification Refractory Mutation System) PCR. Patient data was statistically analyzed to assess the diagnostic correlation between serum CEA levels and the presence of EGFR mutations. Result: The overall EGFR mutation rate was 43.1% with exon 19 deletion (48%) and exon 21 mutations (44%) were the predominant types. Median serum CEA levels were significantly higher in patients with EGFR mutations compared to wild-type cases (14.6 ng/ml vs 2.8 ng/ml, p<0.001). A multivariate analysis revealed a 14% increased likelihood of an EGFR mutation for 1 ng/ml rise in serum CEA. Furthermore, serum CEA showed strong diagnostic accuracy for ctDNA samples at a 6.39 ng/ml cut-off (AUC 0.82, sensitivity 68.0%, specificity 84.8%). Conclusion: Serum CEA is a valuable, cost-effective, and non-invasive biomarker demonstrating significantly higher levels and strong diagnostic accuracy in EGFR-mutated lung adenocarcinoma compared to wild-type cases.

Preterm birth is the leading cause of neonatal death. Despite sustained efforts to identify high-risk women in the mid-trimester, accurate prediction remains difficult. Quantitative cervical ultrasound texture has been proposed as a predictor of spontaneous preterm birth. However, earlier models were developed in small single-centre samples and were not externally validated. We developed image-texture (Local Binary Patterns with a Random Forest), deep-learning (Vision Transformer), clinical-variable, and multimodal models to predict spontaneous preterm birth on the prospective GARBH-Ini cohort. We then externally validated our best models on an independent cohort scanned on a different ultrasound machine. Our best overall model reached an internal-test area under the receiver-operating-characteristic curve of 0.71 (95% CI 0.60, 0.82), but performed modestly at 0.52 (95% CI 0.38, 0.64) externally. The deep-learning and multimodal models did not perform better. Discrimination appeared higher in a clinically high-risk subgroup at the 34-week threshold. These estimates were imprecise because of few cases and need to be confirmed in future studies. Among the several likely reasons for the modest external performance is the heterogeneity of preterm birth. Predicting distinct preterm-birth subtypes separately, and integrating additional biomarkers and data domains, might improve model performance. Keywords: preterm birth; cervical ultrasound; prediction model; external validation; deep learning

Background: KRAS mutation status is an important biomarker in rectal cancer, with implications for prognosis and treatment response. MRI-based radiomics has emerged as a non-invasive approach for predicting tumor genotypes. However, the diagnostic performance of MRI radiomics for predicting KRAS mutation status remains unclear. This study aimed to evaluate the diagnostic accuracy of MRI radiomics for predicting KRAS mutations in rectal cancer. Methods: A systematic search of PubMed, Cochrane Library, Scopus, and Web of Science was performed through July 2025. Diagnostic test accuracy studies evaluating MRI-based radiomics or artificial intelligence models for predicting KRAS mutation status in adult patients with rectal cancer were included, using molecular testing as the reference standard. Risk of bias was assessed using the QUADAS-2 tool. Pooled sensitivity and specificity were estimated using a bivariate random-effects model. Results: Seven studies involving 1,224 patients were included. The pooled sensitivity was 0.736 (95% CI: 0.697-0.772) and the pooled specificity was 0.645 (95% CI: 0.586-0.701). The false positive rate was 0.355 (95% CI: 0.299-0.414). The area under the hierarchical summary receiver operating characteristic curve was 0.754, with a normalized partial AUC of 0.666. Between-study heterogeneity ranged from low to moderate depending on the estimation method (I2 = 8.4%-53.3%). Conclusion: MRI radiomics demonstrates moderate diagnostic accuracy for predicting KRAS mutation status in rectal cancer and may serve as a promising non-invasive biomarker for preoperative molecular stratification. Further large-scale studies with external validation are required to confirm its clinical utility.