JNCI Journal of the National Cancer Institute最新文献

Childhood cancer survivors are at high risk for developing breast cancer (BC) as a treatment-related neoplasm. Their risk for, and survival after, BC recurrence has not been characterized. In this study, female survivors had a ten-year BC recurrence risk of 14% (95% CI: 10-20%), similar to controls with sporadic BC matched by age, race, ethnicity, and disease characteristics (N = 201 pairs; P = .62). Among survivors with recurrent BC (N = 68), first BCs were largely early stage (0/I/II: 77%). Nearly half (47%) underwent bilateral mastectomies, with 81% performed before recurrence, predominantly from distant metastases. Survivors' ten-year mortality risk after BC recurrence was 89% (95% CI: 61-97%), significantly exceeding controls (40%, 95% CI: 16-57%; P = .0013), for an adjusted 2.8-fold greater risk. BC was the leading cause of death in these survivors; the ten-year cause-specific mortality probability was 67% (95% CI: 53-83%). Comprehensive investigations of BC recurrence drivers and adverse outcomes in this population are needed.

Background: Reference sets are needed to evaluate performance of multi-cancer detection (MCD) assays. The National Cancer Institute (NCI) funded the Alliance reference set study to assess MCDs for use in future trials.

Methods: Individuals with cancer and controls were recruited; blood specimens were collected prior to cancer treatment. A performance evaluation study was designed utilizing reference set samples. Companies (n = 6) were selected to participate based on review of performance data and ability to utilize the blood collection tube. Companies received samples from cancer types their assay was designed to detect ("targeted"), plus additional "non-targeted" and control samples. Companies reported positive/negative calls, risk scores, and tissue-of-origin (TOO) predictions. Sensitivity was computed for early (I-II) and late (III-IV) stage cases, based on positive/negative calls (SEPN) and at fixed 98% specificity (SE98). Specificity and TOO accuracy were computed.

Results: 549 cases (encompassing 13 cancer types) and 413 controls from the reference set were included in the study. Companies assessed samples from median 6 (range 5-9) targeted cancer types and median 8 (range: 7-11) overall cancer types. Median (range) specificity was 92.3% (76.5%-98.5%). Median (range) SEPN was 32% (25%-42%) for early stage 73% (48%-89%) for late stage; while median (range) SE98 was 19% (8%-35%) for early stage and 66% (13%-79%) for late stage. Median sensitivity for non-targeted types was 40% (early stage) and 52% (late stage). Median (range) TOO accuracy (primary predicted site) was 75% (64%-78%).

Conclusions: Sensitivity and specificity varied widely across assays with early-stage sensitivity substantially lower than late-stage sensitivity.

Background: Self-reported smoking may not fully capture individualized risk of smoking-related cancer. Circulating proteins may reflect biological consequences of smoking. Thus, we developed a score from smoking-related proteins and evaluated its association with smoking-related cancer.

Methods: This prospective cohort study included 10,563 participants aged 47-70 years in the Atherosclerosis Risk in Communities study. Plasma proteins were measured by SomaScan. The score was constructed from proteins associated with current smoking, packyears, and/or recent quitting identified by linear regression and elastic net regression. Cox regression was used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI). We confirmed the association in a case-cohort study in the European Prospective Investigation into Cancer and Nutrition (EPIC).

Results: aHRs comparing score quartiles Q4 to Q1 for total incidence and mortality of 13 smoking-related cancers were 3.89 (95% CI 3.06-4.96) and 5.73 (95% CI 4.08-8.06) before, and 2.28 (95% CI 1.65-3.15) and 2.07 (95% CI 1.74-4.10) after adjusting for self-reported smoking. aHRs for lung cancer were 12.1 (95% CI 7.11-20.6) and 14.2 (95% CI 7.58-26.8) before, 3.04 (95% CI 1.59-5.81) and 4.12 (95% CI 1.99-8.53) after adjusting. In EPIC, aHRs for lung cancer were 9.47 (95% CI 6.82-13.15) before and 2.23 (95% CI 1.48-3.35) after adjusting.

Conclusion: The smoking-related protein score provided relative risk information for smoking-associated cancers beyond self-reported smoking, which was confirmed in an independent cohort. Such a score may be considered for use in risk stratification for prevention and cancer screening in settings in which detailed smoking history cannot be obtained.

Previous cervical cancer incidence trend analyses primarily used period-based approaches, limiting assessment of generational risk shifts against the backdrop of human papillomavirus (HPV) vaccination. Using U.S. Cancer Statistics data and age-period-cohort modeling, we estimated fitted incidence rates at ages 30-31 across birth cohorts (1917-1919 to 1999-2001), adjusted for period deviation. Incidence rates decreased by 1.9% per birth year (95%CI, -1.8% to -2.1%) up to 1951-1953 cohorts, then decelerated to 0.3% annually (95%CI, -0.4% to -0.2%). Starting with 1987-1989 cohort, incidence rates dropped sharply by 10.5% annually (95%CI, -12.7% to -8.4%). Compared with 1970-1979 cohorts, 1990-1999 cohorts had a 54% lower incidence rate (10.2 vs 4.7 per 100,000; rate ratio = 0.46; 95%CI, 0.42 to 0.50). The markedly lower risk among post-1987-1989 cohorts suggests a future reduction in population-level burden as these cohorts age. The finding also has implications for reinforcing HPV vaccination efforts and informing discussions on raising the screening initiation age.

Background: There is growing interest in artificial intelligence (AI) models for predicting future breast cancer (BC). We performed a systematic review of studies of mammography-based AI models for future BC risk prediction to summarize current evidence, identify knowledge gaps and inform future research directions.

Methods: We searched six databases for studies from January 1, 2012 to February 28, 2025 that evaluated mammography-based AI models for future BC risk prediction. We extracted study design, participants' race and ethnicity, geographic origin, mammogram type, vendor, prediction time frame, BC type predicted, external validation and exclusion of cancers diagnosed on the index screening mammogram. Areas Under the Receiver Operating Curve (AUCs) were summarized overall and by study characteristics.

Results: Forty-one studies met our inclusion criteria. All studies were retrospective, and most used 2D mammograms (n = 37 studies) acquired using Hologic equipment (n = 25) and performed in the United States (n = 17); White, non-Hispanic women were most represented. Nearly all (40) studies assessed discrimination performance with median AUC of 0.71 for ≤2-year risk prediction, 0.72 for 3-4 year, and 0.71 for ≥5-year prediction. Median AUC was 0.75 for studies including index cancers versus 0.68 when excluded. Six studies reported model calibration performance ranging from good to overestimation of risk.

Conclusion: Future studies should evaluate models using digital breast tomosynthesis, examine performance for aggressive or advanced BC, include diverse populations, and evaluate both discrimination and model calibration. Prospective evaluations are needed to determine the clinical utility of mammography-based AI models for personalized risk-based breast cancer screening before implementation.