Materiały źródłowe

• #1 Current Status and Future Prospects for Esophageal Cancer

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9913274/

  Esophageal cancer (EC) is the ninth most common cancer and the sixth leading cause of cancer deaths worldwide. […] Recent advances in understanding the genomic aspects of EC have led to using specific genomic alterations in EC tumors as biomarkers for early diagnosis, treatment, and prognosis of this cancer. Nevertheless, the prognosis of EC is still poor, with a five-year survival rate of less than 20%. […] Currently, there are significant challenges for early detection and secondary prevention for both ESCC and EAC subtypes, but Cytosponge is shifting this position for EAC. […] Despite the improvements in EC survival rates in the past two decades, particularly among younger patients with EAC, the prognosis of EC is still poor, with a five-year survival rate of less than 20%. […] The incidence rate and absolute numbers of EAC are predicted to considerably increase across high-income countries, and the incidence rate of ESCC is predicted to consistently decrease in almost all populations (especially in those with high ESCC rates) during the next few decades. […] Despite the advances in therapeutic strategies and the improvement in EC survival rates over the past two decades, EC still has a poor prognosis with an overall five-year survival of less than 20%.

• #2 Esophageal carcinoma | Radiology Reference Article | Radiopaedia.org

  https://radiopaedia.org/articles/oesophageal-carcinoma-1?lang=us

  Esophageal carcinoma is globally the 7th most common cancer and 6th most common cause of cancer-related death as per NCCN version 3.2023. […] The 5-year mortality depends on the stage of the tumor. Unfortunately, most cases present with regional or distant metastatic disease (30% and 40%, respectively). […] localized disease: ~40% 5-year survival […] distant metastatic disease: ~5% 5-year survival.

• #3 Esophageal adenocarcinoma: A dire need for early detection and treatment | Cleveland Clinic Journal of Medicine

  https://www.ccjm.org/content/89/5/269

  Esophageal cancer is the sixth most common cause of cancer-related death worldwide. […] However, most cases are diagnosed at a late stage, when the prognosis is poor, emphasizing the need for an effective screening strategy. […] Esophageal adenocarcinoma has a favorable prognosis if diagnosed early, when it is isolated to the mucosal and submucosal layers of the esophagus. Unfortunately, most cases are diagnosed at a late stage, when the prognosis is dismal. The 5-year overall survival rate of patients with esophageal adenocarcinoma is less than 20%, comparable to that of patients who have liver, lung, or pancreas cancer. […] Thus, there is a dire need for effective screening strategies to diagnose it earlier. […] For esophageal cancer in general, a multidisciplinary approach may help identify the best therapeutic strategy based on patient and tumor characteristics and local expertise.

• #4 Survival statistics for esophageal cancer | Canadian Cancer Society

  https://cancer.ca/en/cancer-information/cancer-types/esophageal/prognosis-and-survival/survival-statistics

  Survival statistics for esophageal cancer are very general estimates and must be interpreted very carefully. Because these statistics are based on the experience of groups of people, they cannot be used to predict a particular persons chances of survival. […] In Canada, the 5-year net survival for esophageal cancer is 16%. This means that, on average, about 16% of people diagnosed with esophageal cancer will survive for at least 5 years. […] Generally, the earlier esophageal cancer is diagnosed and treated, the better the outcome. Often esophageal cancer is not found until it is at an advanced stage, which can make it more difficult to treat. […] Stage 5 year relative survival Localized (cancer is found only in the esophagus) 43% Regional (cancer has spread to the nearby lymph nodes or tissues) 23% Distant (cancer has spread to other parts of the body) 5%.

• #5 Predicting long-term survival and time-to-recurrence after esophagectomy in patients with esophageal cancer – Development and validation of a multivariate prediction model

  https://pmc.ncbi.nlm.nih.gov/articles/PMC7614526/

  Esophageal cancer remains a significant cause of disease burden and cancer mortality worldwide. […] Furthermore, recurrence remains common and substantially influences long-term prognosis with patients experiencing an overall 5-year survival of 20-50% in most settings. […] In the postoperative setting, early identification of individuals at risk of an unfavorable prognosis following treatment is key to improving long-term survival, with an expanding range of therapeutic alternatives after primary treatment failure including immunotherapy, radiofrequency ablation and salvage locoregional surgery. […] Prediction of patients with poor prognosis remains challenging. […] A limited number of predictive tools exist currently, with The American Joint Committee on Cancer (AJCC) Tumor, Node, Metastases (TNM) staging criteria being widely utilized to stratify patients and their resulting survival based on anatomical stage.

• #6 Esophageal adenocarcinoma: A dire need for early detection and treatment | Cleveland Clinic Journal of Medicine

  https://www.ccjm.org/content/89/5/269

  Once esophageal adenocarcinoma is diagnosed, its stage needs to be assessed to determine prognosis and treatment. […] The tumor, node, metastasis (TNM) staging system for esophageal cancer helps determine prognosis and treatment based on tumor depth, number of affected lymph nodes, and metastasis to distant organs. […] The role of 18-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET/CT) and endoscopic ultrasonography in early esophageal adenocarcinoma staging is controversial. […] However, PET/CT is less beneficial in early esophageal adenocarcinoma than in advanced disease. […] Patients with early esophageal adenocarcinoma and risk of lymph node metastasis are best treated with surgical resection, which allows for lymph node dissection, but many patients over age 65 or those with significant comorbidities may not be candidates for surgery.

• #7 Survival for oesophageal cancer | Cancer Research UK

  https://www.cancerresearchuk.org/about-cancer/oesophageal-cancer/survival

  Survival depends on many factors. No one can tell you exactly how long you will live. […] Your doctor can give you more information about your own outlook (prognosis). […] 5 year survival is the number of people who have not died from their cancer within 5 years after diagnosis. […] Almost 65 out of 100 people (almost 65%) with stage 1 oesophageal cancer will survive their cancer for 5 years or more after they’re diagnosed. […] Around 30 out of 100 people (around 30%) with stage 2 oesophageal cancer will survive their cancer for 5 years or more after they’re diagnosed. […] Around 20 out of 100 people (around 20%) with stage 3 oesophageal cancer will survive their cancer for 5 years or more after they’re diagnosed. […] 5 out of 100 people (5%) with stage 4 oesophageal cancer will survive their cancer for 4 years or more after they are diagnosed.

• #8 Occurrence and Prognosis of Mixed Subtype Adenocarcinoma and Adeno-Squamous Carcinoma in Esophageal Cancer

  https://www.jcancer.org/v15p1442.htm

  The overall incidence and trends of esophageal adenocarcinoma (EAM) had been consistently increasing until 2014, followed by a subsequent decline. […] The overall incidence and trends of esophageal squamous cell carcinoma (EASC) had been decreasing until 2017, after which they showed an increase. […] Using the Kaplan-Meier (K-M) method, the clinical outcomes of EAM were significantly superior to those of EASC, with a p-value 0.001. […] EAM patients had 1-year, 3-year, and 5-year overall survival rates (OS) of 48.4%, 20.2%, and 11.0%, respectively. […] The cancer-specific survival (CSS) rates for EAM patients at these time points were 47.9%, 19.6%, and 11.5%. […] For EASC patients, the OS rates were 35.9%, 13.5%, and 9.93% at 1, 3, and 5 years, respectively. […] The CSS rates for these patients were 35.8%, 13.2%, and 9.7%.

• #8 Occurrence and Prognosis of Mixed Subtype Adenocarcinoma and Adeno-Squamous Carcinoma in Esophageal Cancer

  https://www.jcancer.org/v15p1442.htm

  The study found that the incidence of EAM increased until 2014, followed by a decline, while the incidence of EASC decreased until 2017, followed by an increase. […] For EAM patients, preoperative chemoradiotherapy was associated with better survival rates, while for EASC patients, preoperative radiotherapy combined with adjuvant chemotherapy improved survival. […] EAM and EASC are rare subtypes of esophageal cancer, and an in-depth exploration of their incidence and survival rates provides valuable data and insights for understanding these rare esophageal cancer subtypes. […] The 5-year survival rate for EC remains one of the lowest among all cancer types, with a 20% 5-year survival rate reported in the United States. […] A major reason for the poor prognosis of esophageal malignancies is their tendency not to manifest symptoms until late stages of the disease.

• #9 Occurrence and Prognosis of Mixed Subtype Adenocarcinoma and Adeno-Squamous Carcinoma in Esophageal Cancer

  https://www.jcancer.org/v15p1442.htm

  EAM patients in stages I-II had OS rates of 73.3%, 41.9%, and 27.2% at 1, 3, and 5 years, respectively. […] The CSS rates for these patients were 77.8%, 45.2%, and 27.6%. […] In contrast, EAM patients in stages III-IV had OS rates of 44.3%, 15.6%, and 8.5% at 1, 3, and 5 years, respectively. […] The CSS rates for these patients were 36.8%, 12.6%, and 4.2%. […] EASC patients in stages I-II had OS rates of 53.1%, 25.0%, and 18.8% at 1, 3, and 5 years, respectively. […] EASC patients in stages III-IV had OS rates of 34.6%, 12.6%, and 9.2% at 1, 3, and 5 years, respectively. […] The results indicate that EAM patients in stages I-II had better OS and CSS than EASC patients, but significant differences were only observed in the OS of stages III-IV. […] The prognosis of both types is currently unclear, and this study aims to promote a better understanding of the incidence and survival rates of esophageal AM and EASC.

• #10 Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery

  https://arxiv.org/html/2409.00163v1

  Esophageal cancer is a major cause of cancer-related mortality internationally, with high recurrence rates and poor survival even among patients treated with curative-intent surgery. […] The average 5-year Overall Survival (OS) rate is less than 25%, ranging from 10% to 55% depending on the stage of which the disease is detected. […] Despite advancements in cancer management strategy, more than 50% of the patients experience a recurrence within 1-3 years following curative-intent surgery, with a median survival time of 24 months. […] Therefore, identifying prognostic factors associated with a higher risk of recurrence, as well as predicting and stratifying patients based on their recurrence and survival probabilities, are crucial to the delivery of personalised medicine approaches that could potentially improve oncologic outcomes.

• #11 Survival outcomes of esophageal cancer patients with recurrence after curative treatments | BMC Cancer | Full Text

  https://bmccancer.biomedcentral.com/articles/10.1186/s12885-023-11568-w

  Little is known about predictive factors for survival outcomes of esophageal carcinoma (EC) patients who developed recurrence after undergoing multimodal therapies. […] The median PRS was 12.9 months. […] The survival outcomes of patients with OM were significantly better than those of patients with NOM (P<0.01). [...] Surgical treatments provided significantly better survival outcomes than CRT or chemo-/radiotherapy alone (3-year overall survival (OS); 78.1% vs. 42.5% vs. 28.9%, P<0.01), mainly due to prolonging survival after the recurrence (3-year PRS 62.9% vs. 16.7% vs. 16.2%, P<0.01). [...] The survival outcomes of patients with relapsed EC remain poor. [...] Overall, post-recurrence survival is the major determinant of the long-term outcomes of EC patients with recurrence. [...] Shorter DFI (1 year) (HR 1.57, 95%CI 1.07-2.31, P=0.02), GPS 1 or 2 (HR 1.81, 95%CI 1.08-3.03, P=0.02) and non-surgical treatments (P<0.01), to be independently associated with poor PRS outcomes.

• #12 Survival outcomes of esophageal cancer patients with recurrence after curative treatments | BMC Cancer | Full Text

  https://bmccancer.biomedcentral.com/articles/10.1186/s12885-023-11568-w

  In patients with OM, several factors (cStage III-IV, high GPS and non-surgical treatments) were identified as independent predictors of poor PRS. […] The survival outcomes of those with NOM were quite poor regardless of patient status, original tumor background and treatments. […] The finding that cStage III-IV disease is independently associated with poor PRS is in line with a recent study, although the precise mechanism has yet to be clarified. […] In conclusion, the survival outcomes of patients with relapsed EC remain poor. Surgical treatments might provide survival benefits for patients with relapsed EC, especially for patients with OM, due to prolonging survival after the recurrence.

• #13 A model for predicting prognosis in patients with esophageal squamous cell carcinoma based on joint representation learning

  https://www.spandidos-publications.com/10.3892/ol.2020.12250

  Esophageal squamous cell carcinoma (ESCC) is one of the deadliest cancer types with a poor prognosis due to the lack of symptoms in the early stages and a delayed diagnosis. […] Usually, ESCC has a poor prognosis as most patients lack symptoms at an early stage and are diagnosed too late to achieve curative treatment. […] In China, the survival rate of patients with ESCC with a late diagnosis is 10%, but if it is diagnosed at an early stage, the survival rate can be as high as 85%. […] The two survival subgroups obtained via the joint representation learning-based model had good robustness, and had prognostic significance for patients with ESCC. […] The risk subgroup G2 was significantly associated with prognosis with a hazard ratio (HR) of 3.465, a 95% CI of 1.618-7.421, and a significant P-value of 1.38103.

• #14 Prognostic evaluation of esophageal cancer patients with stages I-III | Aging

  https://www.aging-us.com/article/103532/text

  Purpose: The purpose of this study was to investigate the impact of clinicopathological factors and treatments on the overall survival (OS) and esophageal cancer-specific survival (ECSS) of stages I-III esophageal cancer (EC) patients and to establish a prognostic visual nomogram. […] The OS and ECSS rates of stages I-III EC patients with a total score of more than 150 points in the nomogram were both only 40% after 3 years and 30% after 5 years. […] Conclusion: The nomogram established in this study is an effective clinical tool to predict the prognosis of stages I-III EC patients without chemotherapy. […] Our study also revealed that married patients had better survival, especially for OS in the stages I and II patients. […] Our data showed that the OS and ECSS of patients who had surgery were significantly longer than those who had no surgery.

• #15 Prediction of Early Mortality in Esophageal Cancer Patients with Liver Metastasis Using Machine Learning Approaches

  https://www.mdpi.com/2075-1729/14/11/1437

  The accurate prediction of early death can help clinicians identify high-risk patients at an early stage, allowing for timely adjustments in treatment strategies to avoid unnecessary invasive procedures and maximize quality of life. […] Our study indicates that chemotherapy is an important protective factor against early death in esophageal cancer patients with liver metastasis. […] This study provides new insights into early death prediction for esophageal cancer patients with liver metastasis.

• #15 Prediction of Early Mortality in Esophageal Cancer Patients with Liver Metastasis Using Machine Learning Approaches

  https://www.mdpi.com/2075-1729/14/11/1437

  Patients with esophageal cancer liver metastasis face a high risk of early mortality, making accurate prediction crucial for guiding clinical decisions. […] Key predictors of early mortality included age, location, chemotherapy, and lung metastasis. […] Among the models, XGBoost performed best in predicting all-cause early mortality, while SVM excelled in predicting cancer-specific early mortality. […] These findings demonstrate that machine learning models, particularly XGBoost and SVM, can serve as valuable tools for predicting early mortality in patients with esophageal cancer liver metastasis, aiding clinical decision making. […] The 5-year survival rate for patients with esophageal cancer liver metastasis is less than 5%. […] More than half of esophageal cancer patients already have lymph node or distant organ metastases at the time of initial diagnosis.

• #16 Predicting long-term survival and time-to-recurrence after esophagectomy in patients with esophageal cancer – Development and validation of a multivariate prediction model

  https://pmc.ncbi.nlm.nih.gov/articles/PMC7614526/

  This study illustrated that a prediction model derived from an assortment of routinely collected data can accurately predict differing survival and recurrence times in patients. […] The models in this study perform favorably when compared to pre-existing literature with four studies predicting OS, two predicting DFS, and only one predicting both. […] The CPH models developed in this study illustrated very good discrimination and performance, with ML techniques from the RSF demonstrating similar performance. […] Identification of patient groups at risk of recurrence and poor long-term can improve patient outcomes by enhancing selection of treatment methods and surveillance strategies.

• #17 Predicting long-term survival and time-to-recurrence after esophagectomy in patients with esophageal cancer – Development and validation of a multivariate prediction model

  https://pmc.ncbi.nlm.nih.gov/articles/PMC7614526/

  Nonetheless, a paucity of evidence exists in the literature concerning prognostication for esophageal cancer. […] This study aims to develop and validate multivariable prediction models that can provide clinicians with accurate long-term survival and time-to-recurrence for patients with esophageal cancer following surgical resection. […] The median OS time was 54 months. Recurrence was found in 2653 patients (56.2%), with a median DFS time of 35 months. OS at 1, 3 and 5 years was 89.3%, 59.2% and 47.7% respectively. […] The RSF model demonstrated good discrimination with a bootstrapped tAUC of 77.1% (95% CI 76.1%-78.1%) at 5 years in internal validation. […] The CPH model showed good agreement between the observed and predicted survival times for all patients when grouped in quintiles according to their survival predictions.

• #18 Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma | Scientific Reports

  https://www.nature.com/articles/s41598-023-40780-8

  The risk stratification predicted 3-year overall survival probabilities of 80.8%, 58.2%, and 29.5% for low-, intermediate-, and high-risk subgroups, respectively, in the training cohort, and 75.4%, 48.8%, and 26.9% in the validation cohort. […] Our findings indicate that the machine learning-extended CoxPH model demonstrated the best performance for predicting overall survival in ESCC patients. […] The risk scores derived from the CoxPH model effectively stratified ESCC patients into three prognostic risk groups with distinct survival events. […] Accurately predicting mortality risks in ESCC patients remains an unmet need, and to our knowledge, this is the first study to compare the performance of different machine learning algorithms for developing and validating survival-prediction models in ESCC patients.

• #18 Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma | Scientific Reports

  https://www.nature.com/articles/s41598-023-40780-8

  The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. […] The study found that N stage, T stage, surgical margin, tumor grade, tumor length, sex, MPV, AST, FIB, and Mg are the important feature for ESCC patients survival. […] The risk scores derived from the CoxPH model effectively stratified ESCC patients into low-, intermediate-, and high-risk groups with distinctly different 3-year overall survival (OS) probabilities of 80.8%, 58.2%, and 29.5%, respectively. […] Furthermore, the risk model demonstrated greater discriminative ability and net benefit than the AJCC8th stage, suggesting its potential as a prognostic tool for predicting survival events and guiding clinical decision-making.

• #19 Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery

  https://arxiv.org/html/2409.00163v1

  The significant prognostic factors identified aligned well with clinical literature and experiences. […] For example, pathologic tumor staging features appear to be strong prognostic factors, and are generally more significant than clinical staging. […] In terms of prediction, DeepSurv consistently outperformed CoxPH in both DFS and OS tasks, with C-index of 0.735 and 0.740, respectively, when C-index serving as the primary metric. […] Overall, the two DNN-based models demonstrated comparable discriminative performance to CoxPH; though DeepHit was found to exhibit poorer calibration performance compared to the other two models. […] The use of a multicenter international dataset, which includes patients with either adenocarcinoma or squamous cell carcinoma, suggested broader applicability of these findings across diverse cohort in various clinical settings. […] In general, despite their ability to model more complex interactions, DNN-based models did not greatly outperform the CoxPH. […] The CoxPH, which is interpretable and computationally efficient, still remains a sufficiently good prediction model with tabular data.

• #20 Prognostic gene expression profiling in esophageal cancer: a systematic review | Oncotarget

  https://www.oncotarget.com/article/13328/text/

  Background: Individual variability in prognosis of esophageal cancer highlights the need for advances in personalized therapy. This systematic review aimed at elucidating the prognostic role of gene expression profiles and at identifying gene signatures to predict clinical outcome. […] Overall, 16 studies reported a prognostic gene signature, which was externally validated in 10 studies. […] This systematic review shows heterogeneous findings in associating gene expression with clinical outcome in esophageal cancer. Larger validated studies employing RNA next-generation sequencing are required to establish gene expression profiles to predict clinical outcome and to select optimal personalized therapy. […] Evidence for the potential prognostic role of gene expression profiles is accumulating. Gene signatures may find clinical application in predicting survival, response to neoadjuvant treatment and metastatic potential.

• #21 Prognostic gene expression profiling in esophageal cancer: a systematic review | Oncotarget

  https://www.oncotarget.com/article/13328/text/

  The results demonstrate a large heterogeneity in gene expression profiles and gene signatures predicting survival, response to chemo(radio)therapy, and lymph node metastasis. […] Despite these limitations, the current findings show that gene signatures can be of great prognostic value for clinical outcomes and are therefore paramount in understanding pathogenesis and selecting optimal personalized therapy for the individual patient. […] A gene signature to predict survival may be able to explain why some patients with good tumor characteristics show shorter disease-free survival than expected, and vice versa, thus offering information that is not accurately provided by the pathologic TNM classification. […] Moreover, patients who are unlikely to benefit from chemo(radio)therapy could be selected to receive direct surgical resection, avoiding unnecessary toxicity and delay in surgical treatment with risk of disease progression. […] This systematic review shows potential for prognostic gene expression analysis and future research should aim at translation to clinical practice.

• #22 The detective, prognostic, and predictive value of DNA methylation in human esophageal squamous cell carcinoma | Clinical Epigenetics | Full Text

  https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-016-0210-9

  Esophageal cancer is one of the most common malignancies in the world. […] Despite surgery or chemo-radiotherapy, the prognosis of esophageal cancer still remains poor with the overall 5-year survival ranging from 15 to 25 %. […] FHIT methylation is associated with poor prognosis in ESCC. […] Aberrant DNA methylation patterns may serve as early detection, diagnostic, prognostic, and chemo-sensitive markers. […] LINE-1 hypomethylation is a marker of a poor prognosis in patients with early stage tumors, but not in those with advanced stage tumors. […] The methylation frequency increased with the progression of esophageal cancer. […] DACT2 methylation is associated with TNM stage and lymph node metastasis. […] These results suggest that DACT2 methylation may serve as ESCC early detective and prognostic markers. […] Methylation of MGMT was found to be associated with responsiveness to alkylator-based chemotherapy and an increase in overall survival and time to progression of gliomas. […] CHFR methylation is a sensitive marker for taxanes in human ESCC.

• #23 Prognostic value of pre-therapeutic FDG-PET radiomic analysis in gastro-esophageal junction cancer | Scientific Reports

  https://www.nature.com/articles/s41598-023-31587-8

  The main aim of this study was to evaluate the prognostic value of radiomic approach in pre-therapeutic 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET/CT) in a large cohort of patients with gastro-esophageal junction cancer (GEJC). […] Our results suggest that radiomic approach reveals independent prognostic factors for survival in patients with GEJC. […] Indeed, in a recent European review of 111.006 GEJC, overall survival (OS) was 13.9% at 5 years and 75% of all recurrences occur in the first year after surgery. […] Nevertheless, despite careful evaluation of these clinical factors, it remains difficult to reliably predict patients outcome after a selected treatment. […] In our study, the SUVmean parameter was correlated with RFS in univariate analysis and remained an independent prognostic factor of OS with a cut-off of 5.808.

• #24 Prognostic value of pre-therapeutic FDG-PET radiomic analysis in gastro-esophageal junction cancer | Scientific Reports

  https://www.nature.com/articles/s41598-023-31587-8

  In our analysis of textural features, we showed that Histogram-Entropy was an independent prognostic factor of RFS (p=0.016) and OS (p=0.006) with a cut-off of 0.899. […] For RFS, Cox proportional hazard regression model found that surgery (HR=0.376; 95% CI 0.2370.597; p=0.000) and Histogram-Entropy (HR=2.004; 95% CI 1.1373.535; p=0.016) were independent predictive factors. […] For OS, Cox proportional hazard regression model found that surgery (HR=0.375; 95% CI 0.2270.620; p0.001), SUVmean5.808 (HR=2.201; 95% CI 1.1204.326; p=0.022) and Histogram-Entropy0.899 (HR=2.397; 95% CI 1.2574.573; p=0.006) were independent predictive factors. […] Histogram-Entropy and SUVmean were independently associated with survival in patients with GEJC.

• #25 Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

  https://www.mdpi.com/2072-6694/14/12/3035

  Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma? […] The aim of this retrospective study was to assess the usefulness of 18F-FDG PET/CT radiomics features in predicting outcomes such as tumor and nodal categories, PET-based response to induction chemotherapy, progression-free survival, and overall survival. […] A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. […] The diagnostic accuracies of the CT test datasets for tumor and nodal categories, PET response, and PFS were between 60.0 and 70.4%. […] The PET and the combined PET/CT dataset radiomics models for these clinical outcomes demonstrated diagnostic accuracies from 66.7 to 85.7%, and from 75.0 to 86.2%, respectively.

• #26 Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

  https://www.mdpi.com/2072-6694/14/12/3035

  The prediction of 3-year OS in our datasets was lower than that of the other clinical outcomes studied. […] Our results demonstrate that radiomics-based analysis of 18F-FDG PET/CT can predict clinical outcomes and prognostic factors better than the conventional cross-sectional imaging and are comparable to other studies in the literature.

• #27 Prognostic evaluation of esophageal cancer patients with stages I-III | Aging

  https://www.aging-us.com/article/103532/text

  The C-index and AUC of ROC can evaluate the accuracy of nomograms. […] These results indicated a good prediction accuracy. […] This study utilized a large number of cases and incorporated various clinical information to construct and validate a universally applicable stages I-III EC prediction model that can forecast the dynamic survival rate of patients at different time points during follow-up after diagnosis.

• #28 Survival for oesophageal cancer | Cancer Research UK

  https://www.cancerresearchuk.org/about-cancer/oesophageal-cancer/survival

  For people with oesophageal cancer in England: more than 45 out of every 100 (more than 45%) will survive their cancer for 1 year or more after they are diagnosed, almost 20 out of every 100 (almost 20%) will survive their cancer for 5 years or more, more than 10 out of 100 (more than 10%) will survive their cancer for 10 years or more. […] Your outlook depends on the stage of the cancer when it was diagnosed. This means how big it is and whether it has spread. […] The type of cancer and grade of the cancer cells can also affect your survival. Grade means how abnormal the cells look under the microscope. […] Your general fitness and other health conditions also affect survival. Health conditions could affect the treatments you can have. And good general fitness might help you cope better with your cancer and treatment.

• #29 Survival statistics for esophageal cancer | Canadian Cancer Society

  https://cancer.ca/en/cancer-information/cancer-types/esophageal/prognosis-and-survival/survival-statistics

  Talk to your doctor about your prognosis. A prognosis depends on many factors, including: your health history, the type of cancer, the stage, certain characteristics of the cancer, the treatments chosen, how the cancer responds to treatment. Only a doctor familiar with these factors can put all of this information together with survival statistics to arrive at a prognosis.

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