This statistic shows the death rate of esophagus cancer in the United States from 1999 to 2021. The maximum rate in the given period was *** per every 100,000 age-adjusted population, while the minimum rate stood at ***.

NHIS 2007: Prevalence of Major Risk Factors of EA and ESCC. Our supplemental data relied on the National Health Interview Surveys (NHIS), which conducts nationally representative surveys of the health of civilian, non-institutionalized U.S. population. Using data from the 2007 NHIS, we calculated census region prevalence of reflux, obesity, ever-smoked cigarettes, and moderate to heavy alcohol consumption among whites aged 45–84 years, stratified by sex and census region. (DOC)

BackgroundColon cancer, esophageal cancer, and stomach cancer are the common causes of morbidity and mortality in China, Japan, the US., and India. The current study aims to assess and compare secular trends of the mortality of gastrointestinal cancers during the period, 1990–2017 in age-specific, time period, and birth cohort effects.MethodWe used the Joinpoint model to collect age-standardized mortality rates (ASMRs) for four countries. We designed an age-period-cohort (APC) analysis to estimate the independent effects on the mortality of three types of cancers.ResultThe Joinpoint model shows that in addition to the death rate of esophageal cancer in Japan, the ASMR of esophageal cancer and stomach cancer in other countries declined rapidly. The APC analysis presented a similar pattern of age effect between four countries for colon cancer and stomach cancer, which increased from 20 to 89 age groups. Differently, the period effect rapidly increased for esophageal cancer and stomach cancer in the US, and the period effect in China presented a declining volatility, showing its highest value in 2007. In future, highest mortality trends are likely to occur in China.ConclusionTherefore, the obvious increase in colon cancer recommended that earlier tactics must be performed to reduce mortality from specific causes from 2018 to 2027.

Medical Molecular Testing for Esophageal Cancer Market Outlook

The global medical molecular testing for esophageal cancer market is projected to reach USD 1.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.8% from 2024 to 2032. The rising prevalence of esophageal cancer worldwide and advances in molecular diagnostic technologies are major growth factors driving this market.

The increasing incidence of esophageal cancer globally is one of the primary growth drivers for the medical molecular testing market. According to the World Health Organization (WHO), esophageal cancer is the eighth most common cancer worldwide, with approximately 572,000 new cases and 508,000 deaths reported in 2018. This alarming rate of incidence and mortality emphasizes the need for early and accurate diagnosis, thereby boosting the demand for molecular testing. Such tests offer superior sensitivity and specificity compared to traditional diagnostic methods, making them a preferred choice for healthcare professionals.

Technological advancements in molecular diagnostic techniques are significantly contributing to market growth. Innovations such as next-generation sequencing (NGS), polymerase chain reaction (PCR), and fluorescence in situ hybridization (FISH) have revolutionized cancer diagnostics. These techniques enable the identification of genetic mutations and molecular markers that are critical for early detection and personalized treatment of esophageal cancer. The integration of artificial intelligence (AI) and machine learning (ML) in molecular diagnostics is further enhancing the accuracy and efficiency of these tests, thereby driving market growth.

Government initiatives and funding for cancer research are also playing a crucial role in the expansion of the medical molecular testing market. Various governments and non-profit organizations are investing heavily in cancer research to develop advanced diagnostic and therapeutic solutions. For instance, the National Cancer Institute (NCI) in the United States allocates significant funds for cancer research, including esophageal cancer. Such initiatives are expected to propel the development and adoption of molecular testing technologies, thereby fostering market growth.

In the realm of hereditary colorectal cancer, molecular diagnosis plays a pivotal role in identifying individuals at risk and guiding preventive measures. The Molecular Diagnosis of Hereditary Colorectal Cancer involves analyzing specific genetic mutations that predispose individuals to this type of cancer. Techniques such as next-generation sequencing and multiplex ligation-dependent probe amplification are employed to detect mutations in genes like APC, MLH1, and MSH2. This diagnostic approach not only aids in early detection but also informs family members about their potential risk, enabling proactive health management. As molecular diagnostics continue to evolve, they offer hope for more personalized and effective interventions in hereditary colorectal cancer.

Regionally, North America holds the largest market share due to the high prevalence of esophageal cancer, well-established healthcare infrastructure, and robust research and development activities. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. Factors such as increasing healthcare expenditure, rising awareness about early cancer diagnosis, and a growing patient population are driving the market in this region.

Test Type Analysis

The medical molecular testing for esophageal cancer market is segmented by test type into PCR, NGS, FISH, IHC, and others. Each of these test types offers unique advantages and is utilized based on specific diagnostic requirements. PCR, or polymerase chain reaction, is one of the most widely used techniques due to its high sensitivity and ability to amplify small amounts of DNA. This makes it an invaluable tool for detecting genetic mutations associated with esophageal cancer. PCR's widespread adoption in clinical and research settings is expected to continue driving market growth in this segment.

Next-generation sequencing (NGS) is another crucial segment within the test type category. NGS allows for comprehensive analysis of genetic alterations and provides detailed insights into the molecular landscape of esophageal cancer. This technology is particularly beneficial for identifying novel geneti

Numbers in parentheses () are the column percentages.Distribution of esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) by tumor stage and sex.

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Global Esophageal Cancer Drugs Industry Overview

Various combinations of drugs such as fluorouracil, cisplatin, docetaxel, and carboplatin are involved in standard chemotherapy regimens for the treatment of esophageal cancer. These drugs are selected based on adverse drug reactions and cancer subtypes. To ensure patient access to available therapeutics, governments in developed countries are introducing reimbursement policies. Medicare comprising of Medicare Part A (hospital insurance) and Medicare Part B (medical insurance) is a federal government program for healthcare in the US that covers chemotherapy for cancer patients. Similarly, the Ministry of Health and Long-term Care in Canada provides funds for CYRAMZA and Health Insurance Review and Assessment Service of South Korea has approved CYRAMZA for the treatment of esophageal cancer. The availability of such favorable reimbursements is one of the critical reasons that will drive esophageal cancer drugs market growth.

Additionally, the use of multimodality treatment approach will also contribute to the growth of the esophageal cancer drugs market which will register a CAGR of over 8% during the forecast period. The optimal treatment of esophageal cancer requires a combination of chemotherapy. This treatment approach aims at providing optimal treatment for esophageal cancer by combining the strengths of various treatments such as chemotherapy and targeted therapy (immunotherapy). It also combines procedures such as perioperative chemotherapy or definitive chemoradiation, radiotherapy, and surgery, which will help improve patient outcome and enhance the survival rate.

Top esophageal cancer drugs companies covered in this market research report

The esophageal cancer drugs market is fairly fragmented. By offering a complete analysis of the market’s competitive landscape and with information on the products offered by companies, this esophageal cancer drugs industry analysis report will help clients identify new growth opportunities and design innovative strategies to improve their share in the market.

The report offers a complete analysis of various companies including:

Bristol-Myers Squibb Company
Eli Lilly and Company
F. Hoffmann-La Roche Ltd
Merck & Co., Inc.
Sanofi

Esophageal cancer drugs market segmentation based on geographic regions

Asia
Europe
North America
ROW

North America will account for the largest esophageal cancer drugs market share throughout the forecast period. The growth of esophageal cancer drugs market size in this region is due to major factors such as increasing incidence of esophageal cancer, availability of favorable reimbursement schemes, government initiatives, and growing awareness of esophageal cancer.

Esophageal cancer drugs market segmentation based on product

Targeted therapy
Chemotherapy

The targeted therapy market segment will account for the highest esophageal cancer drugs market share because of the increasing research on novel drugs. This involves targeting specific proteins such as the vascular endothelial growth factor (VEGF) or human epidermal growth factor receptor 2 (HER2) that contribute to cancer growth and survival.

Key highlights of the global esophageal cancer drugs market for the forecast years 2019-2023:

CAGR of the market during the forecast period 2019-2023
Detailed information on factors that will accelerate the growth of the esophageal cancer drugs market during the next five years
Precise estimation of the global esophageal cancer drugs market size and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
The growth of the esophageal cancer drugs industry across various geographies such as Asia, Europe, North America, and ROW
A thorough analysis of the market’s competitive landscape and detailed information on several vendors
Comprehensive details on factors that will challenge the growth of esophageal cancer drugs companies 

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IQR: Interquartile range.‡P-value associated with the test for equal proportions of men between EAC and ESCC patients.±P-value associated with the comparison of median ages between EAC and ESCC patients.Distribution of esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) by sex and age.

These are peer-reviewed supplementary materials for the article 'Cost–effectiveness analysis of pembrolizumab for patients with advanced esophageal cancer at PD-L1 combined positive score ≥10' published in the Journal of Comparative Effectiveness Research.Supplementary materialsAim: Due to the high price of pembrolizumab, it is still unknown whether the use of pembrolizumab for advanced esophageal cancer would be a cost-effective option for patients whose PD-L1 combined positive score is ≥10. Methods: A Markov simulation model was performed based on clinical trial KEYNOTE-181. Incremental cost–effectiveness ratios were calculated to compare the two treatments. Results: The total costs were US$193,575.60 and $8789.24 for pembrolizumab and chemotherapy treatment, respectively. The pembrolizumab group produced 0.93 quality-adjusted life years (QALYs), while the chemotherapy group produced 0.58 QALYs. Thus, patients in the pembrolizumab group spent an additional US$184,786.36 and produced 0.35 QALYs more than the chemotherapy group, which resulted in an incremental cost–effectiveness ratio of US$527,961.03 per QALY. Conclusion: For patients with advanced esophageal cancer whose PD-L1 combined positive score is ≥10, pembrolizumab is not a cost-effective second-line therapy versus chemotherapy from the US payer perspective.

Objective: The study aimed to assess the cost-effectiveness of sintilimab combined with cisplatin plus paclitaxel versus chemotherapy alone as first-line treatment in patients with advanced or metastatic esophageal squamous cell carcinoma from the Chinese healthcare system.Materials and methods: A partitioned survival model was developed based on the ORIENT-15 clinical trial. Drug costs and health state utility were obtained from the literature. Outcomes included the health outcomes in life-years, quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio. One-way and probabilistic sensitivity analyses were performed to evaluate the model uncertainty.Result: In overall population, patients given sintilimab plus chemotherapy gained more health benefits (0.90 QALYs vs. 0.61 QALYs), and the cost was more (15,399.21 US$ VS. 7475.58 US$) than that for patients in the chemotherapy group. In the subgroup, patients given sintilimab plus chemotherapy gained more health benefits (0.89 QALYs vs. 0.68 QALYs), and the cost was more (15,656.19 US$ vs. 9,162.77 US$) than that for patients in the chemotherapy group. Compared with chemotherapy, patients receiving sintilimab plus chemotherapy had ICERs of $26,773.68/QALY in the overall population and $30,065.50/QALY in the subgroup, which was above the threshold of WTP.Conclusion: Sintilimab plus chemotherapy was more cost-effective than chemotherapy alone for patients with advanced esophageal cancer from the perspective of the Chinese healthcare system.

Precise forecasting of cancer outcomes is essential for medical professionals to assess the well-being of patients and develop customized therapeutic plans. Despite its importance, achieving precise forecasts remains a formidable challenge. To tackle this issue, we present an innovative method that harmonizes the Grey Wolf Optimizer (GWO) with Levy flight to optimize the weights and biases of a Backpropagation (BP) neural network—a prominent machine learning model extensively employed in classification tasks. Our novel approach, LGWO-BP, is tailored to augment the precision of cancer prognosis predictions. We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. The experimental results show the exceptional strengths of the proposed LGWO-BP method, particularly its accuracy and reliability compared to GWO-BP, and show that it achieves comparative results against state-of-the-art (SOTA) methods. Our assessment of the LGWO-BP technique’s efficacy involved undertaking empirical tests across half a dozen openly accessible datasets. For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. For the diabetes health indicators dataset, LGWO-BP achieved an accuracy of 0.9 and an AUC of 1. Leveraging data from The Cancer Genome Atlas (TCGA) — a U.S.-led initiative conducting in-depth molecular research to elucidate the causative mechanisms of cancer — this study focuses on three specific cancer types within the dataset: lung, breast, and esophageal cancers. TCGA provides a rich repository of genomic, transcriptomic, epigenomic, and patient-specific clinical data across 33 cancer types. In evaluating the prognostic performance of the LGWO-BP (Lévy flight-enhanced Grey Wolf Optimizer integrated with Back Propagation) model, we observed AUC (Area Under the Curve) scores of 0.70 for miRNA expression, 0.72 for gene expression, and 0.72 for DNA methylation. Regarding precision, the model achieved accuracies of 0.67, 0.69, and 0.66 for miRNA expression, gene expression, and DNA methylation, respectively. For recall, the corresponding values were 0.71, 0.61, and 0.62. Notably, the F1-scores, which balance precision and recall, were 0.69 for miRNA expression, 0.65 for gene expression, and 0.62 for DNA methylation. This research not only advances the application of machine learning in medical prognosis but also offers crucial guidance for clinicians in developing more precise and reliable prognostic tools for cancer patients. By enhancing the efficacy of machine learning-driven cancer prognosis, our proposed LGWO-BP approach has the potential to improve patient care and treatment outcomes significantly.

Precise forecasting of cancer outcomes is essential for medical professionals to assess the well-being of patients and develop customized therapeutic plans. Despite its importance, achieving precise forecasts remains a formidable challenge. To tackle this issue, we present an innovative method that harmonizes the Grey Wolf Optimizer (GWO) with Levy flight to optimize the weights and biases of a Backpropagation (BP) neural network—a prominent machine learning model extensively employed in classification tasks. Our novel approach, LGWO-BP, is tailored to augment the precision of cancer prognosis predictions. We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. The experimental results show the exceptional strengths of the proposed LGWO-BP method, particularly its accuracy and reliability compared to GWO-BP, and show that it achieves comparative results against state-of-the-art (SOTA) methods. Our assessment of the LGWO-BP technique’s efficacy involved undertaking empirical tests across half a dozen openly accessible datasets. For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. For the diabetes health indicators dataset, LGWO-BP achieved an accuracy of 0.9 and an AUC of 1. Leveraging data from The Cancer Genome Atlas (TCGA) — a U.S.-led initiative conducting in-depth molecular research to elucidate the causative mechanisms of cancer — this study focuses on three specific cancer types within the dataset: lung, breast, and esophageal cancers. TCGA provides a rich repository of genomic, transcriptomic, epigenomic, and patient-specific clinical data across 33 cancer types. In evaluating the prognostic performance of the LGWO-BP (Lévy flight-enhanced Grey Wolf Optimizer integrated with Back Propagation) model, we observed AUC (Area Under the Curve) scores of 0.70 for miRNA expression, 0.72 for gene expression, and 0.72 for DNA methylation. Regarding precision, the model achieved accuracies of 0.67, 0.69, and 0.66 for miRNA expression, gene expression, and DNA methylation, respectively. For recall, the corresponding values were 0.71, 0.61, and 0.62. Notably, the F1-scores, which balance precision and recall, were 0.69 for miRNA expression, 0.65 for gene expression, and 0.62 for DNA methylation. This research not only advances the application of machine learning in medical prognosis but also offers crucial guidance for clinicians in developing more precise and reliable prognostic tools for cancer patients. By enhancing the efficacy of machine learning-driven cancer prognosis, our proposed LGWO-BP approach has the potential to improve patient care and treatment outcomes significantly.

Precise forecasting of cancer outcomes is essential for medical professionals to assess the well-being of patients and develop customized therapeutic plans. Despite its importance, achieving precise forecasts remains a formidable challenge. To tackle this issue, we present an innovative method that harmonizes the Grey Wolf Optimizer (GWO) with Levy flight to optimize the weights and biases of a Backpropagation (BP) neural network—a prominent machine learning model extensively employed in classification tasks. Our novel approach, LGWO-BP, is tailored to augment the precision of cancer prognosis predictions. We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. The experimental results show the exceptional strengths of the proposed LGWO-BP method, particularly its accuracy and reliability compared to GWO-BP, and show that it achieves comparative results against state-of-the-art (SOTA) methods. Our assessment of the LGWO-BP technique’s efficacy involved undertaking empirical tests across half a dozen openly accessible datasets. For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. For the diabetes health indicators dataset, LGWO-BP achieved an accuracy of 0.9 and an AUC of 1. Leveraging data from The Cancer Genome Atlas (TCGA) — a U.S.-led initiative conducting in-depth molecular research to elucidate the causative mechanisms of cancer — this study focuses on three specific cancer types within the dataset: lung, breast, and esophageal cancers. TCGA provides a rich repository of genomic, transcriptomic, epigenomic, and patient-specific clinical data across 33 cancer types. In evaluating the prognostic performance of the LGWO-BP (Lévy flight-enhanced Grey Wolf Optimizer integrated with Back Propagation) model, we observed AUC (Area Under the Curve) scores of 0.70 for miRNA expression, 0.72 for gene expression, and 0.72 for DNA methylation. Regarding precision, the model achieved accuracies of 0.67, 0.69, and 0.66 for miRNA expression, gene expression, and DNA methylation, respectively. For recall, the corresponding values were 0.71, 0.61, and 0.62. Notably, the F1-scores, which balance precision and recall, were 0.69 for miRNA expression, 0.65 for gene expression, and 0.62 for DNA methylation. This research not only advances the application of machine learning in medical prognosis but also offers crucial guidance for clinicians in developing more precise and reliable prognostic tools for cancer patients. By enhancing the efficacy of machine learning-driven cancer prognosis, our proposed LGWO-BP approach has the potential to improve patient care and treatment outcomes significantly.

PurposeProton beam therapy (PBT) has recently been included in Japan’s health insurance benefit package for certain cancer types. This study aimed to determine the cost-effectiveness of PBT as a replacement for conventional three-dimensional conformal radiotherapy (3D-CRT) for locally advanced esophageal cancer (LAEC) that is not covered by social insurance.MethodsWe estimated the incremental cost-effectiveness ratio (ICER) of PBT as a replacement for 3D-CRT, using clinical evidence from the literature and expert opinions. We used an economic model, decision tree, and Markov model to illustrate the courses followed by patients with LAEC. Effectiveness was estimated as quality-adjusted life years (QALY) using utility weights for the health state. Social insurance fees were calculated as costs. We assumed two base cases depending on the two existing levels of fees for PBT in social insurance: 2,735,000 Japanese yen (US$20,652) or 1,600,000 yen (US$13,913). The stability of the ICER against these assumptions was appraised using sensitivity analysis.ResultsThe effectiveness of PBT and 3D-CRT was 2.62 and 2.51 QALY, respectively. The estimated ICER was 14,025,268 yen (US$121,958) per QALY for the higher fee level and 7,026,402 yen (US$61,099) for the lower fee level. According to the Japanese threshold for cost-effectiveness of anticancer therapy of 7,500,000 yen (US$65,217) per QALY gain, the inclusion of PBT for LAEC in the benefit package of social insurance is cost-effective if a lower fee is applied.ConclusionPBT is a cost-effective alternative to 3D-CRT for LAEC and making it available to patients under social insurance could be justifiable.

Findings from a study on diabetes across 130 US hospitals spanning the years 1999 to 2008.