American Joint Committee On Cancer Staging Research Articles

Nomogram for predicting survival in T1–T2 stage patients with supraglottic squamous cell carcinoma

BackgroundSupraglottic squamous cell carcinoma (SGSCC) is characterized by low differentiation, rapid growth, and inconspicuous initial manifestations. Early detection and prompt treatment can significantly improve survival rates. The main focus of treatment is to maintain optimal laryngeal function.MethodsUsing the Surveillance, Epidemiology, and End Results (SEER) database, we conducted univariate and multivariate Cox regression analyses to identify independent prognostic factors for T1–T2 SGSCC. We also enrolled 109 patients with T1–T2 SGSCC from the First Affiliated Hospital of Xinjiang Medical University as an external validation set. In addition, we developed a nomogram to predict the prognosis of T1–T2 SGSCC, assessed the predictive accuracy and discriminatory ability of the nomogram using the area under the curve (AUC), C-index, receiver operating characteristic (ROC) curve and calibration curve, and confirmed the clinical validity of the nomogram using decision curve analysis (DCA).ResultsOur investigation identified nine prognostic indicators for T1–T2 SGSCC: age (≥ 65 years), marital status, American Joint Committee on Cancer (AJCC) stage (II–IV), grade (III–IV), M stage (M1), radiotherapy, chemotherapy, sex (female), and surgery. These variables were used to create accurate nomograms that predict overall and specific survival rates at 1, 3, and 5 years. The nomograms demonstrated superior prognostic value and accuracy compared to AJCC staging. Laryngectomy with partial laryngectomy is the preferred treatment option for T1–T2 SGSCC cases, providing superior overall survival (OS) and cancer-specific survival (CSS). Radiotherapy also improves OS and CSS. Our results were based on a comprehensive analysis of various indicators, including the C-index, ROC curve, calibration curve, and DCA curve.ConclusionNomograms provide significant advantages in treatment decision making and diagnosis. Laryngectomy with partial laryngectomy is the most appropriate method for T1–T2 SGSCC cases. However, radiotherapy can also be used. Thus, patients with T1–T2 SGSCC should be evaluated to determine if combination therapy is the optimal treatment approach. Nevertheless, further research is needed to understand the role of chemotherapy. Overall, this study identified nine key predictors of future outcomes, aiding healthcare professionals in assessing risks and making treatment decisions for T1–T2 SGSCC patients.

Abstract PO3-22-03: Effect of Radiation Therapy on Survival Outcomes in Elderly ER/PR Positive AJCC Stage T1 Breast Cancer Patients

Abstract Background: The CALGB 9343 study in 2004 reported that women >70 years of age with pT1NO hormone receptor positive breast cancer received no meaningful benefit from breast irradiation following lumpectomy. The effectiveness of adjuvant radiation therapy in node negative, T1 tumor patients has been limited to institutional experiences with relatively lack of information in population-based studies. In this hospital-based study using National Cancer Data Base (NCDB) from 2004 to 2020, we report on adjuvant radiation therapy (XRT) usage and the survival outcomes in stage IA, IB, IC breast cancer patients who received XRT versus those who did not. Materials and Methods: Patients in the National Cancer Database (NCDB) who were registered between 2004 and 2019 and followed up to the end of 2020 were used in this research. We evaluated a cohort of 126,156 female breast cancer patients of age >= 70 years with the American Joint Committee on Cancer (AJCC) stage TIA, TIB, and TIC. All patients were ER/PR positive, received hormonal therapy, all patients were either white or black, were either privately or Medicare insured, received hormonal therapy, and patients were receipt of adjuvant chemotherapy or not. Radiation statuses were grouped as no radiation, radiation only and/or radiation with boost. Other adjusted variables included: age, race, Charlson comorbidity index, payer status, income, education, distance traveled, diagnosing/treating facility, location of treatment and treatment delay. SAS for Windows 9.4 was used for all data management and statistical analysis. Multivariate Cox regression was used to assess the effect of the XRT on overall survival while adjusting for other factors. All p-values < 0.05 or 95% Confidence Limits of Hazards Ratio (HR) does not include 1 were considered statistically significant. Results: Among 126,156 female breast cancer patients, the age distribution was 47.87%, 31.72%, and 20.4% for age group of 70-74, 75-79, 80 or older, respectively. Ninety-three percent of patients were white. 89% of patients were Medicare and 11% were privately insured at diagnosis. 21% of patient were with the Charlson Comorbidity index score 1 or above. 10.77%, 34.42% and 54.81% of patients were AJCC Stage T1A, T1B, and T1C respectively. 11.7% of patients received chemotherapy and 57.9% of patients received radiation therapy. 54.38%, 59.59%, and 57.59% of T1a, T1b, and T1c patients received the XRT, respectively. In univariate analysis, the XRT demonstrated a statistically significant increase of 10-year survival by 9.75%, 9.8%, and 12.6% in T1a, T1b, and T1c (all p < 0.0001) patients respectfully. In multivariate cox regression analysis, age was a predictor of survival outcome with a HR at 1.65, 3.02 for 75-79, and 80 or older as compared to 70-74 years old, respectively. In addition, compared to patients who traveled less than 20 miles, the HR were 0.90 for patients travelled more than 20 miles. AJCC stage was also associated the survival, a HR of 1.05 (95% CI: 1.01-1.186) for T1b, 1.16 (95% CI: 1.11 – 1.21) for T1c as compared for T1a. Adjusting for other factors, the usage of radiation reduced the risk of dying by 25% (HR=0.75, 95% CI: 0.73-0.77) when compared to no radiation therapy. Conclusion: The current study has demonstrated that more than 54% of these patients received radiation therapy. In univariate analysis, the benefit of adjuvant radiation therapy is more than 9.7% for T1a, T1b, and T1c on 10-year overall survival which is statistically significant. Adjusting for other factors, patients receiving radiation therapy in these elderly population have a 25% less chance of dying as compared to no radiation therapy regardless of AJCC Stage. A further propensity score matching method would help assess the effectiveness of radiation therapy on these patients’ population. A randomized prospective clinical trial would be better to define the precise benefit of adjuvant radiation therapy in this elderly population. Table 1 Patients Demographic, Social Economic and Clinical Characteristics XX- XX Table 3 Hazard Ratios estimate from Multivariate Cox Regression XX- XX Radiation Effect on Overall Survival of Elderly Breast Cancer Patients by Stage T1A, T1B, T1C XX- XX Citation Format: Runhua Shi, Gary Burton. Effect of Radiation Therapy on Survival Outcomes in Elderly ER/PR Positive AJCC Stage T1 Breast Cancer Patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-22-03.

Abstract PO5-27-01: Comparison between the 7th and the 8th edition of TNM Staging System of American joint committee on Cancer(AJCC) for Breast Cancer Patients, diagnosed and treated at King Abdulaziz Medical City

Abstract Breast cancer is a heterogeneous disease with different biological and molecular characteristics that affect local recurrence, metastases, prognoses, and response to available therapies. The 8th edition of the American Joint Committee on Cancer (AJCC) staging system for breast cancer has incorporated the biological and molecular characteristics, tumor grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor status(Her-2)and tumor grade, into the traditional anatomical 7th edition that only considers tumor size, lymph nodes status, and distant metastases. The aim of this study is to compare the 7th to 8th editions of the AJCC staging system for prognostic impact, assess the effect of the new edition on the treatment and treatment outcome Method A retrospective cohort study was performed for all breast cancer patients, diagnosed between (2013 -2019) and underwent upfront surgery at King Abdulaziz Medical City (KAMC).Patients’ clinicopathological, immunohistochemical, surgical, therapeutic, and follow-up data were retrieved. Overall survival (OS) and breast cancer-specific survival (BCSS) were estimated using the Kaplan–Meier method. Univariate analysis using Fisher's exact test was used to compare staging systems' prognostic accuracy. Multivariable analysis using Cox proportional hazards regression analysis was performed to identify factors independently associated with disease outcome Results A total of 169 female patients were included, the 8th AJCC staging system resulted in a reallocation of 108 (63.9%) of the patients to a different stage group. The majority were downstaged(60.9%) while only 3% were upstaged, most of the upstaged patients had a triple-negative subtype (80%). The change in overall stage for the 8th edition was most significant in stage I (an increase by 140.3%), followed by stage II (downstaged by 74.3%) and stage III (downstaged by 49.7%) compared to the 7th edition. Critically 42.8% of all downstaged patients treated with adjuvant chemotherapy were found to have received unnecessary chemotherapy after applying the 8th AJCC staging system as per National Comprehensive Cancer Network (NCCN) clinical practice guidelines recommendations. The 8th AJCC staging system was a better prognostic predictor of disease survival, patients in 8th AJCC stage 3 had a higher tendency to develop metastases and die because of their disease (P-value 0.03, 0.051, respectively). Conclusions: Our comparison revealed that the AJCC 8th edition staging system is superior and more comprehensive than the 7th, in term of prognostic value, and in defining the precise adjuvant therapy Figure 1 Comparison of the 7th AJCC and the 8th AJCC staging system Citation Format: Nafisa Abdelhafiez, Abdulmohsen Alkushi, Lolwah Alriyees, Mohammad Arabi, Faris Alsalamah, Emad Masuadi, Ahmad Omair, Mohammad Alkaiyat, Hussam Shehata. Comparison between the 7th and the 8th edition of TNM Staging System of American joint committee on Cancer(AJCC) for Breast Cancer Patients, diagnosed and treated at King Abdulaziz Medical City [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-27-01.

Critical updates in neuroendocrine tumors: Version 9 American Joint Committee on Cancer staging system for gastroenteropancreatic neuroendocrine tumors.

The American Joint Committee on Cancer (AJCC) staging system for all cancer sites, including gastroenteropancreatic neuroendocrine tumors (GEP-NETs), is meant to be dynamic, requiring periodic updates to optimize AJCC staging definitions. This entails the collaboration of experts charged with evaluating new evidence that supports changes to each staging system. GEP-NETs are the second most prevalent neoplasm of gastrointestinal origin after colorectal cancer. Since publication of the AJCC eighth edition, the World Health Organization has updated the classification and separates grade 3 GEP-NETs from poorly differentiated neuroendocrine carcinoma. In addition, because of major advancements in diagnostic and therapeutic technologies for GEP-NETs, AJCC version 9 advocates against the use of serum chromogranin A for the diagnosis and monitoring of GEP-NETs. Furthermore, AJCC version 9 recognizes the increasing role of endoscopy and endoscopic resection in the diagnosis and management of NETs, particularly in the stomach, duodenum, and colorectum. Finally, T1NXM0 has been added to stage I in these disease sites as well as in the appendix.

Nomogram model for predicting cancer-specific mortality in patients with early-onset colorectal cancer: a competing risk analysis insight from the SEER database and an external validation cohort.

Early-onset colorectal cancer (EOCRC) is increasing in incidence and poses a growing threat. Urgent research is needed, especially in survival analysis, to enhance comprehension and treatment strategies. This study aimed to explore the risk factors associated with cancer-specific mortality (CSM) and other-cause mortality (OCM) in patients with EOCRC. Additionally, the study aimed to develop a nomogram predicting CSM using a competitive risk model and validate its accuracy through the use of training, using internal and external cohorts. Data from EOCRC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database (2008-2017). EOCRC patients who were treated at a tertiary hospital in northeast China between 2014 and 2020 were also included in the study. The SEER data were divided into the training and validation sets at a 7:3 ratio. A univariate Cox regression model was employed to identify prognostic factors. Subsequently, multivariate Cox regression models were applied to ascertain the presence of independent risk factors. A nomogram was generated to visualize the results, which were evaluated using the concordance index (C-index), area under the curve (AUC), and calibration curves. The clinical utility was assessed via decision curve analysis (DCA). Multivariable Cox regression analysis demonstrated that factors such as race, tumor differentiation, levels of carcinoembryonic antigen (CEA), marital status, histological type, American Joint Committee on Cancer (AJCC) stage, and surgical status were independent risk factors for CSM in EOCRC patients. In addition, age, gender, chemotherapy details, CEA levels, marital status, and AJCC stage were established as independent risk factors for OCM in individuals diagnosed with EOCRC. A nomogram was developed using the identified independent risk factors, demonstrating excellent performance with a C-index of 0.806, 0.801, and 0.810 for the training, internal validation, and external validation cohorts, respectively. The calibration curves and AUC further confirmed the accuracy and discriminative ability of the nomogram. Furthermore, the DCA results indicated that the model had good clinical value. In this study, a competing risk model for CSM was developed in EOCRC patients. The model demonstrates a high level of predictive accuracy, providing valuable insights into the treatment decision-making process.

A nomogram incorporating treatment data for predicting overall survival in gastroenteropancreatic neuroendocrine tumors: a population-based cohort study.

Over the last few decades, the annual global incidence of gastroenteropancreatic neuroendocrine tumours (GEP-NETs) has steadily increased. Because of the complex and inconsistent treatment of GEP-NETs, the prognosis of patients with GEP-NETs is still difficult to assess. The study aimed to construct and validate the nomograms included treatment data for prediction overall survival (OS) in GEP-NETs patients. GEP-NETs patients determined from the Surveillance, Epidemiology, and End Results (SEER)-13 registry database (1992-2018) and with additional treatment data from the SEER-18 registry database (1975-2016). In order to select independent prognostic factors that contribute significantly to patient survival and can be included in the nomogram, multivariate Cox regression analysis was performed using the minimum value of Akaike information criterion (AIC) and we analyzed the relationship of variables with OS by calculating hazard ratios (HRs) and 95% CIs. In addition, we also comprehensively compared the nomogram using to predict OS with the current 7th American Joint Committee on Cancer (AJCC) staging system. From 2004 to 2015, a total of 42662 patients at diagnosis years with GEP-NETs were determined from the SEER database. The results indicated that the increasing incidence of GEP-NETs per year and the highest incidence is in patients aged 50-54. After removing cases lacking adequate clinicopathologic characteristics, the remaining eligible patients ( n =7564) were randomly divided into training (3782 patients) and testing sets (3782 patients). In the univariate analysis, sex, age, race, tumour location, SEER historic stage, pathology type, TNM, stage, surgery, radiation, chemotherapy, and CS tumour size were found to be significantly related to OS. Ultimately, the key factors for predicting OS were determined, involving sex, age, race, tumour location, SEER historic stage, M, N, grade, surgery, radiation, and chemotherapy. For internal validation, the C-index of the nomogram used to estimate OS in the training set was 0.816 (0.804-0.828). For external validation, the concordance index (C-index) of the nomogram used to predict OS was 0.822 (0.812-0.832). In the training and testing sets, our nomogram produced minimum AIC values and C-index of OS compared with AJCC stage. Decision curve analysis (DCA) indicated that the nomogram was better than the AJCC staging system because more clinical net benefits were obtained within a wider threshold probability range. A nomogram combined treatment data may be better discrimination in predicting overall survival than AJCC staging system. The authors highly recommend to use their nomogram to evaluate individual risks based on different clinical features of GEP-NETs, which can improve the diagnosis and treatment outcomes of GEP-NETs patients and improve their quality of life.

Utilizing the AJCC 8th Staging to Discriminate Survival Outcomes in HPV-associated Oropharyngeal Squamous Cell Carcinoma.

The American Joint Committee on Cancer (AJCC) staging 8th edition introduced major changes in the TNM staging of oropharyngeal squamous cell carcinoma (OPSCC) based on the human papillomavirus (HPV) status. This study aimed to observe how well the AJCC staging 8th edition precisely discriminates survival outcomes in patients with HPV-associated OPSCC using a large population database. Using the Surveillance, Epidemiology, and End Results database between 2010 and 2016, 7,448 patients with HPV-associated OPSCC were enrolled. Patients diagnosed with OPSCC and tested positive for HPV with information on the TNM staging according to the AJCC staging 7th edition were selected. Next, T-, N-, and clinical staging were reconstructed based on the AJCC staging 8th edition. Survival probabilities in both AJCC staging 7th and 8th editions were estimated and compared. Most patients (93.44%) were down-staged from the 7th to the 8th edition. The AJCC staging 8th edition showed more discriminatory power in predicting survival of patients with HPV-associated OPSCC than the AJCC staging 7th edition, regardless of the primary subsites. Additionally, clinical stage I patients with HPV-associated OPSCC according to the AJCC 8th edition showed better prognosis in case of high T staging than high N staging. Clinical staging according to the AJCC 8th edition compared to that of the 7th edition was an independent prognostic factor in patients with HPV-associated OPSCC. This study emphasizes the advantages of the new classification system for discriminating survival in HPV-associated OPSCC according to various factors.

Evaluation of AJCC staging system and proposal of a novel stage grouping system in retroperitoneal liposarcoma: the Fudan Zhongshan experience.

Overall survival (OS) varies significantly among individuals with heterogeneous retroperitoneal liposarcoma (RPLS), even among those with the same clinical stage. Improved staging of RPLS is a critical unmet need, given the disappointing results of external validations of the 8th American Joint Committee on Cancer (AJCC) TNM staging system. The cohort study included 220 consecutive patients who underwent surgical resection for primary RPLS at the largest sarcoma centre of Fudan University in China from September 2009 to August 2021, combined with 277 adult patients with RPLS in the SEER database from 1975 to 2020. Data analysis was performed from December 2021 to December 2022. Patients were retrospectively restaged according to the 8th and 7th editions of the TNM staging system as well as the new TNM (nTNM) staging system. The primary endpoint was overall survival (OS). Comparative analysis of postoperative survival was performed using the Kaplan-Meier method, and differences between subgroups were tested using the log-rank test. The OS prediction nomogram was generated based on baseline variables and tumour characteristics. Harrell's consistency index (C-index), area under the curve (AUC) of receiver operating characteristic curves (ROC), and calibration curves were used to evaluate the performance of the nomogram. A total of 497 patients were enrolled in the study, including 282 (56.7%) male patients. The median follow-up was 51 months (interquartile range, IQR, 23-83), and the OS rates at 1, 3, and 5 years were 87.9%, 75.3%, and 64.9%, respectively. According to the staging distribution of the AJCC 7th edition, 6 patients were stage IA (1.2%), 189 patients were stage IB (38%), 12 patients were stage IIA (2.4%), 150 patients were stage IIB (30.1%), 131 patients were stage III (26.3%), and 9 patients were stage IV (1.8%). With the 8th edition staging, this distribution changed: 6 patients (1.2%) were stage IA, 189 patients (38%) were stage IB, 12 patients (2.4%) were stage II, 24 patients (4.8%) were stage IIIA, 257 patients (51.7%) were stage IIIB, and 9 patients (1.8%) were stage IV. 182 patients (36.6%) were reclassified according to the nTNM staging system with the new T stage classification. The C-index and log-rank score improved after implementation of nTNM implementation. The nTNM system was associated with improved identification of high-risk patients compared with the AJCC 7th and 8th TNM. The FNCLCC stage proved to be highly prognostic with significant intergroup differences in OS. The calibration curve shows a high degree of agreement between the actual OS rate and the nomogram estimated OS rate. Compared with 8th AJCC TNM, 7th AJCC TNM staging system showed a more homogeneous staging distribution and a slight improvement in the prognostic accuracy of RPLS. The revised T-stage and nTNM systems showed better risk stratification performance. The FNCLCC stage was found to have high prognostic value, further emphasising histological grade is the least negligible prognostic factor in predicting patient survival. The constructed nomogram model enables individualized prognostic analysis and helps to develop risk-adapted therapy for RPLS patients.

Comparison between pelvic MRI, CT, and PET/CT in baseline staging and radiation planning of anal squamous cell carcinoma.

To investigate the differences in baseline staging of anal squamous cell carcinoma based on CT, MRI, and PET/CT, and the resultant impact on the radiation plan. This retrospective study included consecutive patients with anal squamous cell carcinoma who underwent baseline pelvic MRI, CT, and PET/CT (all examinations within 3weeks of each other) from January 2010 to April 2020. CTs, MRIs, and PET/CTs were re-interpreted by three separate radiologists. Several imaging features were assessed; tumor stage was determined based on the eight edition of the American Joint Committee on Cancer (AJCC) staging manual; and T (tumor), N (node), and M (metastasis) categories were determined based on National Comprehensive Cancer Network (NCCN) guidelines. Radiologist assessments were then randomly presented to a radiation oncologist who formulated the radiation plan in a blinded fashion. Across 28 patients (median age, 62years [range, 31-78], T-category classification was significantly different on PET/CT compared to MRI and CT (p = 0.037 and 0.031, respectively). PET/CT staged a higher proportion of patients with T1/T2 disease (16/28, 57%) compared to MRI (11/28, 39%) and CT (10/28, 36%). MRI staged a higher proportion of patients with T3/T4 disease (14/28, 50%) compared to CT (12/28, 43%) and PET/CT (11/28, 39%). However, there was no significant difference between the three imaging modalities in terms of either N-category, AJCC staging, or NCCN TNM group classification, or in treatment planning. Our exploratory study showed that MRI demonstrated a higher proportion of T3/T4 tumors, while PET/CT demonstrated more T1/T2 tumors; however, MRI, CT, and PET/CT did not show any significant differences in AJCC and TNM group categories, nor was there any significant difference in treatment doses between them when assessed independently by an experienced radiation oncologist.

Development and validation of a deep learning model for predicting postoperative survival of patients with gastric cancer

BackgroundDeep learning (DL), a specialized form of machine learning (ML), is valuable for forecasting survival in various diseases. Its clinical applicability in real-world patients with gastric cancer (GC) has yet to be extensively validated.MethodsA combined cohort of 11,414 GC patients from the Surveillance, Epidemiology and End Results (SEER) database and 2,846 patients from a Chinese dataset were utilized. The internal validation of different algorithms, including DL model, traditional ML models, and American Joint Committee on Cancer (AJCC) stage model, was conducted by training and testing sets on the SEER database, followed by external validation on the Chinese dataset. The performance of the algorithms was assessed using the area under the receiver operating characteristic curve, decision curve, and calibration curve.ResultsDL model demonstrated superior performance in terms of the area under the curve (AUC) at 1, 3, and, 5 years post-surgery across both datasets, surpassing other ML models and AJCC stage model, with AUCs of 0.77, 0.80, and 0.82 in the SEER dataset and 0.77, 0.76, and 0.75 in the Chinese dataset, respectively. Furthermore, decision curve analysis revealed that the DL model yielded greater net gains at 3 years than other ML models and AJCC stage model, and calibration plots at 3 years indicated a favorable level of consistency between the ML and actual observations during external validation.ConclusionsDL-based model was established to accurately predict the survival rate of postoperative patients with GC.

Prognostic nomogram for predicting the overall survival rate of patients with uterine clear-cell carcinoma: Based on SEER database.

To evaluate the risk factors for uterine clear-cell carcinoma (UCCC) and construct nomograms predicting 1-, 3-, and 5-year overall survival rates of patients with UCCC. The demographic and clinical information of 1674 patients diagnosed with UCCC between 2004 and 2015, including age, race, marital status, tumor size, American Joint Committee on Cancer (AJCC) stage, and details of surgery and radiotherapy/chemotherapy, was collected from the Surveillance, Epidemiology, and End Results (SEER) database. After excluding patients with unknown AJCC stage, race, marital status, or lymph node information, 1469 patients remained. Risk factors were determined using univariate and multivariate analyses, and nomograms were developed to predict 1-, 3-, and 5-year overall survival of UCCC. Various indicators were used to evaluate the performance of the nomogram, such as the C-index, net classification improvement (NRI) and decision curve analysis (DCA). Age, log odds of positive lymph nodes, AJCC stage, surgery status, and chemotherapy status were independent risk factors for UCCC. The C-indexes of the training group and AJCC stage groups were 0.771 and 0.697, respectively. The results for the area under the receiver operating characteristics curve, NRI, and calibration curves indicated that the nomogram had good predictive ability. DCA revealed that the nomogram had greater clinical applicability than AJCC stage alone. Internal validation using the validation cohort also demonstrated that this nomogram had good predictive performance. A new nomogram comprising a combination of demographic and clinical characteristics provided better survival predictions than the AJCC staging system alone, which will facilitate prognostic assessments and clinical decision-making.

A prognostic nomogram for predicting breast cancer survival based on mammography and AJCC staging

Rationale and objectivesTo develop a prognostic nomogram using mammography data and AJCC staging to predict breast cancer survival. Materials and methodsA prognostic nomogram was created using data from 1000 women diagnosed with breast cancer at a medical cancer center in Taiwan between 2011 and 2015. The variables included age at diagnosis (≤60 or > 60 years), mammography purpose (screening or diagnostic), mammography modality (digital mammogram or digital breast tomosynthesis), and the 7th American Joint Committee on Cancer (AJCC) stage. The outcome predicted was breast cancer-related mortality. The nomogram utilized Kaplan-Meier analysis for all subsets and Cox proportional hazards regression analysis for prediction. The nomogram's accuracy was internally validated using the concordance index and receiver operating characteristic (ROC) curve analysis, focusing on 3-year and 5-year survival predictions. ResultsParticipants' mean age at breast cancer diagnosis was 54 years (SD = 11.2 years). The 1-year, 3-year, and 5-year overall survival (OS) rates were found to be 99.7%, 95.3%, and 91.4%, respectively. The bootstrap-corrected concordance indices indicated the following: nomogram, 0.807 and AJCC, 0.759. A significant difference was observed between the nomogram's area under the curve (AUC) and the AJCC stage in predicting the probability of 5-year survival (p = 0.005). A nomogram, constructed based on mammography and AJCC, demonstrated excellent calibration through internal validation using bootstrapping. ConclusionThe utilization of a nomogram that incorporates mammography data and the AJCC registry data has been demonstrated to be a reliable predictor of breast cancer survival.

Health Economic Consequences Associated With COVID-19–Related Delay in Melanoma Diagnosis in Europe

The COVID-19 pandemic resulted in delayed access to medical care. Restrictions to health care specialists, staff shortages, and fear of SARS-CoV-2 infection led to interruptions in routine care, such as early melanoma detection; however, premature mortality and economic burden associated with this postponement have not been studied yet. To determine the premature mortality and economic costs associated with suspended melanoma screenings during COVID-19 pandemic lockdowns by estimating the total burden of delayed melanoma diagnoses for Europe. This multicenter economic evaluation used population-based data from patients aged at least 18 years with invasive primary cutaneous melanomas stages I to IV according to the American Joint Committee on Cancer (AJCC) seventh and eighth editions, including melanomas of unknown primary (T0). Data were collected from January 2017 to December 2021 in Switzerland and from January 2019 to December 2021 in Hungary. Data were used to develop an estimation of melanoma upstaging rates in AJCC stages, which was verified with peripandemic data. Years of life lost (YLL) were calculated and were, together with cost data, used for financial estimations. The total financial burden was assessed through direct and indirect treatment costs. Models were building using data from 50 072 patients aged 18 years and older with invasive primary cutaneous melanomas stages I to IV according to the AJCC seventh and eighth edition, including melanomas of unknown primary (T0) from 2 European tertiary centers. Data from European cancer registries included patient-based direct and indirect cost data, country-level economic indicators, melanoma incidence, and population rates per country. Data were analyzed from July 2021 to September 2022. COVID-19 lockdown-related delay of melanoma detection and consecutive public health and economic burden. As lockdown restrictions varied by country, lockdown scenario was defined as elimination of routine medical examinations and severely restricted access to follow-up examinations for at least 4 weeks. Primary outcomes were the total burden of a delay in melanoma diagnosis during COVID-19 lockdown periods, measured using the direct (in US$) and indirect (calculated as YLL plus years lost due to disability [YLD] and disability-adjusted life-years [DALYs]) costs for Europe. Secondary outcomes included estimation of upstaging rate, estimated YLD, YLL, and DALY for each European country, absolute direct and indirect treatment costs per European country, proportion of the relative direct and indirect treatment costs for the countries, and European health expenditure. There were an estimated 111 464 (range, 52 454-295 051) YLL due to pandemic-associated delay in melanoma diagnosis in Europe, and estimated total additional costs were $7.65 (range, $3.60 to $20.25) billion. Indirect treatment costs were the main cost driver, accounting for 94.5% of total costs. Estimates for YLD in Europe resulted in 15 360 years for the 17% upstaging model, ranging from 7228 years (8% upstaging model) to 40 660 years (45% upstaging model). Together, YLL and YLD constitute the overall disease burden, ranging from 59 682 DALYs (8% upstaging model) to 335 711 DALYs (45% upstaging model), with 126 824 DALYs for the real-world 17% scenario. This economic analysis emphasizes the importance of continuing secondary skin cancer prevention measures during pandemics. Beyond the personal outcomes of a delayed melanoma diagnosis, the additional economic and public health consequences are underscored, emphasizing the need to include indirect economic costs in future decision-making processes. These estimates on DALYs and the associated financial losses complement previous studies highlighting the cost-effectiveness of screening for melanoma.

Prognostic evaluation of a multi-target magnetic bead-enriched circulating tumor cell-enriched identification system for colorectal cancer.

Colorectal cancer (CRC) is the third most prevalent cancer in the world. Traditional tissue biopsy cannot provide dynamic monitoring of patients' tumors or reflect the characteristics of tumors in real time because the sampling process is invasive and accompanied by risks. Circulating tumor cells (CTCs) are considered a major cause of tumor metastasis, and investigating CTCs helps to understand the biology and vulnerability of malignant tumors during hematogenous metastasis. We sequentially used epithelial cell adhesion molecule (EpCAM)-coated immunoliposomal magnetic beads (Ep-IMBs) and vimentin-coated immunoliposomal magnetic beads (Vi-IMBs) to capture and characterize CTCs from 110 CRC patients. We further constructed a Cox risk regression model, optimized the model composition using backward stepwise regression, and finally applied nomograms to show the effect of each variable on survival risk. The specificity of the CTCs enrichment and identification system was 100% and the sensitivity was 79.0%. Multivariate analysis indicated total CTC number was an important predictor for bad survival, whereas American Joint Committee on Cancer (AJCC) stage, lymph node metastasis, and carcinoembryonic antigen (CEA) level were associated with prognosis, and the risk of mortality was associated with the AJCC stage of the CRC. The CTC enrichment and identification system constructed in this research demonstrated superior accuracy. In addition, CTCs can be used as an important predictor for prognosis of patients with CRC, and the combination of other clinical predictive factors can help clinicians to better design individualized treatment regimens, which is of great clinical application value.

Optimized Nomogram for Nasopharyngeal Carcinoma Prognosis Prediction in Younger Patients (Aged 18-59): Development and Validation.

To develop a nomogram model for the predicted overall survival (OS) in patients aged 18 to 59 years with nasopharyngeal carcinoma (NPC) and assess the value of the clinical application. In total, 1334 registers of NPC patients from 2010 to 2015 were retrieved from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox analysis were used to screen out independent risk factors affecting patients. Cox analysis predicted OS for patients with NPC at 3, 5, and 8 years. Nomogram performance was validated using the concordance index (C-index), receiver operating characteristic, calibration curve, and decision curve analysis (DCA). Age, sex, race, marital, histological type, tumor size, AJCC stage, and radiotherapy were independent risk factors. The C-index of the nomogram was 0.69 [95% confidence interval (CI): 0.68-0.71] for the training set, and the C-index of the AJCC stage was 0.63 (95% CI: 0.62-0.65), both statistically significant (P < .01). The area under the curve for the nomogram at these intervals (0.755, 0.729, and 0.729, respectively) was higher than that of the AJCC stage (0.667, 0.646, and 0.646, respectively), indicating better predictive accuracy. The calibration curves revealed a high degree of agreement between the observation and the prediction. Compared to the American Joint Committee on Cancer (AJCC) stage, DCA showed better clinical utility. The nomogram as novel predictor for nasopharyngeal carcinoma patients' survival.

Multivariate analysis of prognostic factors in patients with lip squamous cell carcinoma after surgery

BackgroundLip squamous cell carcinoma (LSCC) was one of the most common cancer types of head and neck tumors. This study aimed to find more predictors of the prognosis in postoperative LSCC patients.MethodsA total of 147 LSCC patients between June 2012 and June 2018 were collected from two tertiary care institutions. There were 21 clinicopathological factors included and analyzed in our study. The univariate and multivariate Cox regression analyses were performed to find the independent prognostic factors for predicting progression-free survival (PFS) and overall survival (OS) in postoperative LSCC patients. The role of adjuvant radiotherapy in various subgroups was displayed by Kaplan–Meier plots.ResultsThe 1-, 3-, and 5-year PFS of postoperative LSCC patients were 88.4%, 70.1%, and 57.8%, respectively. Similarly, the 1-, 3-, and 5-year OS of postoperative LSCC patients were 94.6%, 76.9%, and 69.4%, respectively. The results suggested that postoperative LSCC patients with age at diagnosis ≥ 70 years, grade with moderate or poor differentiate, the American Joint Committee on Cancer (AJCC) stage IV, higher systemic immune-inflammation index (SII), surgical margin < 5, and age-adjusted Charlson Comorbidity Index (ACCI) ≥ 5 tend to have a poorer PFS (all P < 0.05). Besides, postoperative LSCC patients with age at diagnosis ≥ 70 years, AJCC stage IV, higher GPS, higher SII, and ACCI ≥ 5 tend to have a worse OS (all P < 0.05). Additionally, postoperative patients with LSCC in the subgroup of ACCI < 5 and AJCC III–IV stage was more likely to benefit from adjuvant radiotherapy, but not for the other subgroups.ConclusionWe identified a series of significant immune-inflammation-related and comorbidity-related clinicopathological factors associated with the prognosis of postoperative LSCC patients by local data from two tertiary care institutions in China, which can be helpful for patients and surgeons to pay more attention to nutrition, inflammation, and complications and finally obtained a better prognosis.

Development and validation of web-based nomograms for predicting survival status in patients with intrahepatic cholangiocarcinoma depending on the surgical status: a SEER database analysis

This study aimed to develop and validate prognostic nomograms that can estimate the probability of 1-, 3- and 5-year overall survival (OS) as well as cancer-specific survival (CSS) for Intrahepatic cholangiocarcinoma (ICCA) patients. Clinical data of 1446 patients diagnosed with ICCA between 2010 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed. In both the OS and the CSS group, the training cohort and validation cohort were divided into a 7:3 ratio. Age, sex, AJCC T stage, AJCC N stage, AJCC M stage, surgical status, and tumor grade were selected as independent prognostic risk factors to build the nomograms. To compare the efficacy of predicting 1-, 3-, and 5-year OS and CSS rates of the nomogram with the 8th edition of the American Joint Committee on Cancer (AJCC) staging system, we evaluated the Harrell’s index of concordance (C-index), area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) in both cohorts. The results showed the nomogram for 1-, 3-, and 5-year OS and CSS prediction performed better than the AJCC staging system. In the subgroup analysis for patients could not receive surgery as the primary treatment. We developed two nomograms for predicting the 1-, and 2-year OS and CSS rates following the same analysis procedure. Results indicate that the performance of both nomograms, which contained sex, AJCC T stage, AJCC M stage, chemotherapy, and tumor grade and prognostic factors, was also superior to the AJCC staging system. Meanwhile, four dynamic network-based nomograms were published. The survival analysis showed the survival rate of patients classified as high-risk based on the nomogram score was significantly lower compared to those categorized as low-risk (P < 0.0001). Finally, accurate and convenient nomograms were established to assist clinicians in making more personalized prognosis predictions for ICCA patients.

LASSO-derived prognostic model predicts cancer-specific survival in advanced pancreatic ductal adenocarcinoma over 50 years of age: a retrospective study of SEER database research.

This study aimed to develop a prognostic model for patients with advanced ductal adenocarcinoma aged ≥50 years. Patient information was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to screen the model variables. Cases from Nanchang Central Hospital were collected for external validation. The new nomogram and the American Joint Committee on Cancer (AJCC) criteria were evaluated using integrated discrimination improvement (IDI) and net reclassification index (NRI) indicators. Survival curves presented the prognosis of the new classification system and AJCC criteria. In total, 17,621 eligible patients were included. Lasso Cox regression selected 4 variables including age, chemotherapy, radiotherapy and AJCC stage. The C-index of the training cohort was 0.721. The C-index value of the validation cohort was 0.729. The AUCs for the training cohorts at 1, 2, and 3 years were 0.749, 0.729, and 0.715, respectively. The calibration curves showed that the predicted and actual probabilities at 1, 2, and 3 years matched. External validation confirmed the model's outstanding predictive power. Decision curve analysis indicated that the clinical benefit of the nomogram was higher than that of the AJCC staging system. The model evaluation indices preceded the AJCC staging with NRI (1-year: 0.88, 2-year: 0.94, 3-year: 0.72) and IDI (1-year: 0.24, 2-year: 0.23, 3-year: 0.22). The Kaplan-Meier curves implied that the new classification system was more capable of distinguishing between patients at different risks. This study established a prognostic nomogram and risk classification system for advanced pancreatic cancer in patients aged ≥50 years to provide a practical tool for the clinical management of patients with pancreatic ductal adenocarcinoma.

Prognostic value of deep learning-derived body composition in advanced pancreatic cancer—a retrospective multicenter study

Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC). We retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers. We applied a deep learning approach to assess sarcopenia by the abdominal muscle-to-bone ratio (MBR) and myosteatosis by the ratio of abdominal inter- and intramuscular fat to muscle volume. In the pooled cohort, univariable and multivariable analyses were carried out to analyze the association between body composition markers and overall survival (OS). We analyzed the relationship between body composition markers and laboratory values during the first year of therapy in a subgroup using linear regression analysis adjusted for age, sex, and American Joint Committee on Cancer (AJCC) stage. Deep learning-derived MBR [hazard ratio (HR) 0.60, 95% confidence interval (CI) 0.47-0.77, P < 0.005] and myosteatosis (HR 3.73, 95% CI 1.66-8.39, P < 0.005) were significantly associated with OS in univariable analysis. In multivariable analysis, MBR (P= 0.019) and myosteatosis (P= 0.02) were associated with OS independent of age, sex, and AJCC stage. In a subgroup, MBR and myosteatosis were associated with albumin and C-reactive protein levels after initiation of therapy. Additionally, MBR was also associated with hemoglobin and total protein levels. Our work demonstrates that deep learning can be applied across cancer centers to automatically assess sarcopenia and myosteatosis from routine CT scans. We highlight the prognostic role of our proposed markers and show a strong relationship with protein levels, inflammation, and anemia. In clinical practice, automated body composition analysis holds the potential to further personalize cancer treatment.

Development and Validation of Nomograms to Predict Risk and Prognosis in Salivary Gland Carcinoma Patient with Distant Metastases.

Background: Salivary gland carcinoma (SGC) patients with distant metastasis (DM) are rare, and understanding this disease is insufficient. Nomograms can predict the prognostic probability of patients, while few studies have examined diagnostic and prognostic factors in SGC patients with DM. The purpose of this study was to establish and validate the risk and prognostic nomograms of SGC patients with DM. Methods: Based on the SEER database, we analyzed the data of SGC patients between 2004 and 2015. Logistic regression analyses and Cox proportional hazards regression analyses were used to identify risk and prognostic factors for DM in SGC patients. Based on the Akaike information criterion (AIC) value and likelihood ratio test, the best-fitting model was selected to build risk and prognostic nomograms, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and Kaplan-Meier (K-M) survival curves. ROC curves were also used to compare the nomograms with the American Joint Committee on Cancer (AJCC) staging system. Results: 7418 SGC patients were included in the study, and 307 (4.14%) of them were diagnosed with DM. This study identified that there are variables (age ≥ 80, no-parotid gland primary site, histologic type of mucoepidermoid carcinoma and squamous cell carcinoma, T stage ≥ T2, N staged ≥ N1, histologic grade ≥ III, and tumor size ≥ 41 mm) associated with the occurrence of DM in SGC patients. Therefore, we constructed diagnostic and prognostic nomograms after incorporating these variables. ROC curves illustrated the better predictive efficacy of 2 nomograms over the AJCC staging system. DCA curves, calibration curves, and K-M survival curves showed that 2 nomograms can accurately predict the occurrence and prognosis of DM among SGC patients in training and validation sets. Conclusion: It was shown that the nomograms were highly discriminative in predicting the diagnosis and prognosis of SGC patients with DM, and could identify high-risk patients, thereby providing SGC patients with individualized treatment plans.