Development and Assessment of a Clinical Calculator for Estimating the Likelihood of Recurrence and Survival Among Patients With Locally Advanced Rectal Cancer Treated With Chemotherapy, Radiotherapy, and Surgery

Nomogram for Predicting 1-, 3-, and 5-Year Survival in Hemodynamically Significant Aortic Regurgitation: The ARISE Score

BACKGROUND

A user-friendly tool that integrates key clinical variables to estimate prognosis in aortic regurgitation is lacking. We aimed to develop and validate a nomogram-based score to predict survival and identify high-risk patients for timely aortic valve surgery referral.

METHODS AND RESULTS

From 2008 to 2022, 1229 patients (derivation data set: 764 Taiwanese; validation data set: 465 Japanese; age: 64±17 years) with isolated chronic moderately severe to severe aortic regurgitation from 3 centers were included. All echocardiograms were reviewed de novo. At a median follow-up of 5.0 (interquartile range, 2.2-8.2) years, 204 all-cause deaths occurred and 247 underwent aortic valve surgery within 3 months. In multivariable analysis, age (P<0.001), Charlson Comorbidity Index (P<0.001), New York Heart Association functional class IV (P<0.001), left ventricular ejection fraction (P<0.001), left ventricular end-systolic dimension index (P=0.03), and aortic valve surgery in 3 months (P=0.03) were associated with all-cause death. These variables, along with sex and maximal aorta diameter index, were incorporated into the combined left ventricular ejection fraction and left ventricular end-systolic dimension index nomogram to estimate 1-, 3-, and 5-year survival and to calculate the Aortic Regurgitation/Insufficiency Survival Estimation (ARISE) score. Calibration plots demonstrated good performance, with the area under the receiver operating characteristic curve reaching 0.79 in the validation data set. The left ventricular end-systolic dimension index-based nomogram showed similar performance. By using the tertiles of the ARISE score to risk stratify individuals, Kaplan-Meier curves demonstrated significant survival differences among 3 risk groups in both the derivation and validation cohorts (P<0.001).

CONCLUSIONS

The ARISE score (https://arise-score.vercel.app/), which includes guideline-recommended parameters, effectively predicts survival in patients with aortic regurgitation. It may facilitate shared decision-making between the heart team and patients.

Risk score model for predicting local control and survival in patients with rectal cancer treated with neoadjuvant chemoradiotherapy

The present study aimed to investigate clinicopathological factors affecting local recurrence and survival in patients with locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT) and to create a risk-scoring model predicting local control (LC) and survival. The clinical and pathological data of 115 patients who received nCRT for LARC between February 2010 and December 2020 were reviewed retrospectively. A risk-scoring model was developed to predict LC and survival using statistically significant prognostic factors in univariate and multivariate analyses. In the multivariate analysis, the LC rate was improved in patients with a good pathological response to nCRT. By contrast, the disease-free survival (DFS) rate was significantly worse in patients with perineural invasion (PNI). The overall survival (OS) rate was significantly worse in patients who were >60 years of age, who had tumors ≥5 cm, who were PNI-positive and who had pathological N2 stage disease. Patients were grouped to analyze the ability of the scoring system to predict LC and survival. The total score was derived by assigning points to the prognostic factors in univariate and multivariate analyses and was subsequently divided into three groups according to tertile. The median LC times in groups 1-3 were significantly different at 143.6, 97.2 and 93.6 months, respectively. The median DFS times in groups 1-3 were significantly different at 136.1, 108.5 and 67.2 months, respectively, while the median OS times in groups 1-3 were significantly different at 138.3, 87.2 and 64.6 months, respectively. In conclusion, risk score modeling with prognostic factors effectively determined the difference in LC and survival between the groups. Adding effective systemic therapy to nCRT may improve results, especially in patients with multiple poor prognostic factors, including larger tumors, PNI and multiple nodal involvement.

A multivariable disease-specific model enhances prognostication beyond current Merkel cell carcinoma staging: An international cohort study of 10,958 patients

BACKGROUND

Merkel cell carcinoma (MCC) is a highly aggressive cutaneous malignancy for which accurate prognostication is necessary to support clinical management.

OBJECTIVE

(1) To determine which survival endpoint-disease-specific death (DSD) or overall survival (OS)-was better stratified by MCC American Joint Cancer Committee eighth edition staging. (2) To develop a multivariable model for enhanced MCC survival predictions.

METHODS

A retrospective analysis of 10,958 histologically confirmed MCC patients between January 2000 and December 2020 was performed. Patient and tumor features were analyzed from 2 cohorts: a US cohort and an external validation UK cohort. A multivariable Fine and Gray competing risk (FG) model was utilized to account for the competing risk.

RESULTS

DSD demonstrated greater discriminatory power as a survival endpoint when compared with OS. Multivariate FG analysis identified the most impactful features of DSD: truncal lesions (subdistribution hazard ratios [SHRs] = 1.96, P < .001), age >84 years (SHR = 1.79, P < .001), male sex (SHR = 1.34, P < .001), and marital status (SHR = 1.09, P < .001). A second FG model incorporating those impactful features enhanced survival predictions beyond current MCC staging criteria alone in both the US (C-index 0.75 vs 0.64, P < .001) and external validation UK cohort (C-index 0.77).

CONCLUSIONS

MCC staging can stratify DSD better than OS. Additional patient and tumor features enhanced prognostication beyond current staging systems.

Gut microbiome model predicts response to neoadjuvant immunotherapy plus chemoradiotherapy in rectal cancer

BACKGROUND

Accurate evaluation of the response to preoperative treatment enables the provision of a more appropriate personalized therapeutic schedule for locally advanced rectal cancer (LARC), which remains an enormous challenge, especially neoadjuvant immunotherapy plus chemoradiotherapy (nICRT).

METHODS

This prospective, multicenter cohort study enrolled patients with LARC from 6 centers who received nICRT. The dynamic variation in the gut microbiome during nICRT was evaluated. A species-level gut microbiome prediction (SPEED) model was developed and validated to predict the pathological complete response (pCR) to nICRT.

FINDINGS

A total of 50 patients were enrolled, 75 fecal samples were collected from 33 patients at different time points, and the pCR rate reached 42.4% (14/33). Lactobacillus and Eubacterium were observed to increase after nICRT. Additionally, significant differences in the gut microbiome were observed between responders and non-responders at baseline. Significantly higher abundances of Lachnospiraceae bacterium and Blautia wexlerae were found in responders, while Bacteroides, Prevotella, and Porphyromonas were found in non-responders. The SPEED model showcased a superior predictive performance with areas under the curve of 98.80% (95% confidence interval [CI]: 95.67%-100%) in the training cohort and 77.78% (95% CI: 65.42%-88.29%) in the validation cohort.

CONCLUSIONS

Programmed death 1 (PD-1) blockade plus concurrent long-course CRT showed a favorable pCR rate and is well tolerated in microsatellite-stable (MSS)/mismatch repair-proficient (pMMR) patients with LARC. The SPEED model can be used to predict the pCR to nICRT based on the baseline gut microbiome with high robustness and accuracy, thereby assisting clinical physicians in providing individualized management for patients with LARC.

FUNDING

This research was funded by the China National Natural Science Foundation (82202884).

Clinical Calculator for Predicting Freedom From Recurrence After Resection of Stage I-III Colon Cancer in Patients With Microsatellite Instability

PURPOSE

Outcome for patients with nonmetastatic, microsatellite instability (MSI) colon cancer is favorable: however, high-risk cohorts exist. This study was aimed at developing and validating a nomogram model to predict freedom from recurrence (FFR) for patients with resected MSI colon cancer.

PATIENTS AND METHODS

Data from patients who underwent curative resection of stage I, II, or III MSI colon cancer in 2014-2021 (model training cohort, 384 patients, 33 events; median follow-up, 38.8 months) were retrospectively collected from institutional databases. Variables associated with recurrence in multivariable analysis were selected for inclusion in the clinical calculator. The calculator's predictive accuracy was measured with the concordance index and validated using data from patients who underwent treatment for MSI colon cancer in 2007-2013 (validation cohort, 164 patients, eight events; median follow-up, 84.8 months).

RESULTS

T category and number of positive lymph nodes were significantly associated with recurrence in multivariable analysis and were selected for inclusion in the clinical calculator. The calculator's concordance index for FFR in the model training cohort was 0.812 (95% CI, 0.742 to 0.873), compared with 0.759 (95% CI, 0.683 to 0.840) for the staging schema of the eighth edition of the American Joint Committee on Cancer Staging Manual. The concordance index for the validation cohort was 0.744 (95% CI, 0.666 to 0.822), confirming robust predictive accuracy.

CONCLUSION

Although in general patients with nonmetastatic MSI colon cancer had favorable outcome, patients with advanced T category and multiple metastatic lymph nodes had higher risk of recurrence. The clinical calculator identified patients with MSI colon cancer at high risk for recurrence, and this could inform surveillance strategies. In addition, the model could be used in trial design to identify patients suitable for novel adjuvant therapy.

The effects of oral nutritional supplements interventions on nutritional status in patients undergoing colorectal cancer surgery: A systematic review

BACKGROUND

The high incidence of malnutrition in patients undergoing colorectal cancer surgery can lead to unplanned weight loss, sarcopenia and reduced grip strength to the extent that it can seriously affect the prognosis of colorectal cancer patients.

OBJECTIVE

This study investigated the effect of oral nutritional supplements (ONS) on the prevalence of grip strength, unplanned weight loss and sarcopenia in patients undergoing colorectal cancer surgery.

METHODS

We systematically searched randomized controlled studies from CINAHL, PubMed, Embase, Cochrane and Web of Science and three Chinese databases (CNKI, Wan-Fang database, VIP database) from database creation to September 2023. The risk of bias in individual studies was assessed using the Cochrane Collaboration tool, and the certainty of evidence was assessed using the five GRADE criteria. Statistical analysis was performed using the RevMan 5.3 software, and information that could not be meta-analysed was reviewed in the form of a literature summary.

RESULTS

Eleven papers met the inclusion criteria with a combined sample size of 1070 cases, including 532 cases in the trial group and 538 cases in the control group. Four papers reported the effect of ONS on grip strength and included very low-quality evidence supporting no effect of ONS on grip strength. Ten studies reported the effect of ONS on body weight and body mass index (BMI) and included very low-quality evidence supporting a positive ONS on weight and BMI changes. Meta-analysis showed a significant reduction in weight loss (12-15 weeks) and BMI loss (12-15 weeks) in patients with colorectal cancer in the ONS group. The effect of ONS on the prevalence of sarcopenia after hospital discharge was reported in two studies, and meta-analysis showed a significant reduction in the prevalence of postoperative sarcopenia in colorectal cancer patients in the ONS group, but the quality of evidence was low.

CONCLUSIONS

This study showed that the use of ONS in patients undergoing surgery for colorectal cancer improved patient weight loss and BMI reduction and reduced the prevalence of postoperative sarcopenia but did not improve patient grip strength. The quality of evidence for inclusion in the article was low or very low, and further studies are needed to provide better evidence.

Tumor regression and safe distance of distal margin after neoadjuvant therapy for rectal cancer

Neoadjuvant therapy has been widely employed in the treatment of rectal cancer, demonstrating its utility in reducing tumor volume, downstaging tumors, and improving patient prognosis. It has become the standard preoperative treatment modality for locally advanced rectal cancer. However, the efficacy of neoadjuvant therapy varies significantly among patients, with notable differences in tumor regression outcomes. In some cases, patients exhibit substantial tumor regression, even achieving pathological complete response. The assessment of tumor regression outcomes holds crucial significance for determining surgical approaches and establishing safe margins. Nonetheless, current research on tumor regression patterns remains limited, and there is considerable controversy surrounding the determination of a safe margin after neoadjuvant therapy. In light of these factors, this study aims to summarize the primary patterns of tumor regression observed following neoadjuvant therapy for rectal cancer, categorizing them into three types: tumor shrinkage, tumor fragmentation, and mucinous lake formation. Furthermore, a comparison will be made between gross and microscopic tumor regression, highlighting the asynchronous nature of regression in the two contexts. Additionally, this study will analyze the safety of non-surgical treatment in patients who achieve complete clinical response, elucidating the necessity of surgical intervention. Lastly, the study will investigate the optimal range for safe surgical resection margins and explore the concept of a safe margin distance post-neoadjuvant therapy.

MRI-based radiomics for preoperative prediction of recurrence and metastasis in rectal cancer

OBJECTIVES

To explore the value of multi-parametric MRI (mp-MRI) radiomic model for preoperative prediction of recurrence and/or metastasis (RM) as well as survival benefits in patients with rectal cancer.

METHODS

A retrospective analysis of 234 patients from two centers with histologically confirmed rectal adenocarcinoma was conducted. All patients were divided into three groups: training, internal validation (in-vad) and external validation (ex-vad) sets. In the training set, radiomic features were extracted from T2WI, DWI, and contrast enhancement T1WI (CE-T1) sequence. Radiomic signature (RS) score was then calculated for feature screening to construct a rad-score model. Subsequently, preoperative clinical features with statistical significance were selected to construct a clinical model. Independent predictors from clinical and RS related to RM were selected to build the combined model and nomogram.

RESULTS

After feature extraction, 26 features were selected to construct the rad-score model. RS (OR = 0.007, p < 0.01), MR-detected T stage (mrT) (OR = 2.92, p = 0.03) and MR-detected circumferential resection margin (mrCRM) (OR = 4.70, p = 0.01) were identified as independent predictors of RM. Then, clinical model and combined model were constructed. ROC curve showed that the AUC, accuracy, sensitivity and specificity of the combined model were higher than that of the other two models in three sets. Kaplan-Meier curves showed that poorer disease-free survival (DFS) time was observed for patients in pT3-4 stages with low RS score (p < 0.001), similar results were also found in pCRM-positive patients (p < 0.05).

CONCLUSION

The mp-MRI radiomics model can be served as a noninvasive and accurate predictors of RM in rectal cancer that may support clinical decision-making.

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

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Merkel cell carcinoma recurrence risk estimation is improved by integrating factors beyond cancer stage: A multivariable model and web-based calculator

BACKGROUND

Merkel cell carcinoma (MCC) recurs in 40% of patients. In addition to stage, factors known to affect recurrence risk include: sex, immunosuppression, unknown primary status, age, site of primary tumor, and time since diagnosis.

PURPOSE

Create a multivariable model and web-based calculator to predict MCC recurrence risk more accurately than stage alone.

METHODS

Data from 618 patients in a prospective cohort were used in a competing risk regression model to estimate recurrence risk using stage and other factors.

RESULTS

In this multivariable model, the most impactful recurrence risk factors were: American Joint Committee on Cancer stage (P < .001), immunosuppression (hazard ratio 2.05; P < .001), male sex (1.59; P = .003) and unknown primary (0.65; P = .064). Compared to stage alone, the model improved prognostic accuracy (concordance index for 2-year risk, 0.66 vs 0.70; P < .001), and modified estimated recurrence risk by up to 4-fold (18% for low-risk stage IIIA vs 78% for high-risk IIIA over 5 years).

LIMITATIONS

Lack of an external data set for model validation.

CONCLUSION/RELEVANCE

As demonstrated by this multivariable model, accurate recurrence risk prediction requires integration of factors beyond stage. An online calculator based on this model (at merkelcell.org/recur) integrates time since diagnosis and provides new data for optimizing surveillance for MCC patients.

Validation of a Clinical Calculator Predicting Freedom From Colon Cancer Recurrence After Surgery on the Basis of Molecular and Clinical Variables

BACKGROUND

The Memorial Sloan Kettering clinical calculator for estimating the likelihood of freedom from colon cancer recurrence on the basis of clinical and molecular variables was developed at a time when testing for microsatellite instability was performed selectively, based on patient age, family history, and histologic features. Microsatellite stability was assumed if no testing was done.

OBJECTIVE

This study aimed to validate the calculator in a cohort of patients who had all been tested for microsatellite instability.

DESIGN

Retrospective cohort analysis.

SETTINGS

Comprehensive cancer center.

PATIENTS

This study included consecutive patients who underwent curative resection for stage I, II, or III colon cancer between 2017 and 2019.

INTERVENTION

Universal testing of mircrosatellite phenotype in all cases.

MAIN OUTCOME MEASURES

The calculator's predictive accuracy was assessed using the concordance index and a calibration plot of predicted versus actual freedom from recurrence at 3 years after surgery. For a secondary sensitivity analysis, the presence of a tumor deposit(s) (disease category N1c) was considered equivalent to one positive lymph node (category N1a).

RESULTS

With a median follow-up of 32 months among survivors, the concordance index for the 745 patients in the cohort was 0.748 (95% CI, 0.693-0.801), and a plot of predicted versus observed recurrences approached the 45° diagonal, indicating good discrimination and calibration. In the secondary sensitivity analysis for tumor deposits, the concordance index was 0.755 (95% CI, 0.700-0.806).

LIMITATIONS

This study was limited by its retrospective, single-institution design.

CONCLUSIONS

These results, based on inclusion of actual rather than imputed microsatellite stability status and presence of tumor deposits, confirm the predictive accuracy and reliability of the calculator. See Video Abstract .

VALIDACIN DE UNA CALCULADORA CLNICA QUE PREDICE LA AUSENCIA DE RECURRENCIA POSTQUIRURGICA DEL CNCER DE COLON SOBRE LA BASE DE VARIABLES MOLECULARES Y CLNICAS

ANTECEDENTES:La calculadora clínica del Memorial Sloan Kettering para la estimación de la probabilidad de ausencia de recurrencia del cáncer de colon sobre la base de variables clínicas y moleculares, se desarrolló en un momento en que las pruebas para la inestabilidad de microsatélites se realizaban de forma selectiva, basadas en la edad del paciente, los antecedentes familiares y las características histológicas. Se asumía la estabilidad micro satelital si no se realizaba ninguna prueba.OBJETIVO:El objetivo de este estudio fue validar la calculadora en una cohorte de pacientes a los que se les había realizado la prueba de inestabilidad de microsatélites.DISEÑO:Análisis de cohorte retrospectivo.AJUSTE:Centro integral de cáncer.PACIENTES:Pacientes consecutivos con cáncer de colon que fueron sometidos a resección curativa por cáncer de colon en estadios I, II o III entre los años 2017 y 2019.PRINCIPALES MEDIDAS DE RESULTADO:La precisión predictiva de la calculadora fue evaluada mediante el índice de concordancia y un gráfico de calibración de la ausencia de recurrencia predecida versus la real a los 3 años tras la cirugía. A los efectos de un análisis secundario de sensibilidad, la presencia de depósito(s) tumoral(es) (categoría de enfermedad N1c) se consideró equivalente a un ganglio linfático positivo (categoría N1a).RESULTADOS:Con una mediana de seguimiento de 32 meses entre los supervivientes, el índice de concordancia para los 745 pacientes de la cohorte fue de 0,748 (intervalo de confianza del 95 %, 0,693 a 0,801), y una gráfica de recurrencias previstas versus observadas se acercó a la diagonal de 45°, indicando una buena discriminación y calibración. En el análisis secundario de sensibilidad para depósitos tumorales, el índice de concordancia fue de 0,755 (intervalo de confianza del 95 %, 0,700 a 0,806).LIMITACIONES:Diseño retrospectivo, institución única.CONCLUSIONES:Estos resultados, basados en la inclusión real del estado de estabilidad de microsatélites en lugar de imputado y la presencia de depósitos tumorales, confirman la precisión predictiva y la confiabilidad de la calculadora. (Traducción-Dr Osvaldo Gauto ).

Development and validation of a nomogram to provide individualized predictions of functional outcomes in patients with convulsive status epilepticus at 3 months: The modified END-IT tool

AIMS

The prediction of outcomes in convulsive status epilepticus (CSE) remains a constant challenge. The Encephalitis-Nonconvulsive Status Epilepticus-Diazepam Resistance-Image Abnormalities-Tracheal Intubation (END-IT) score was a useful tool for predicting the functional outcomes of CSE patients, excluding cerebral hypoxia patients. With further understanding of CSE, and in view of the deficiencies of END-IT itself, we consider it necessary to modify the prediction tool.

METHODS

The prediction model was designed from a cohort of CSE patients from Xijing Hospital (China), between 2008 and 2020. The enrolled subjects were randomly divided into training cohort and validation cohort as a ratio of 2:1. The logistic regression analysis was performed to identify the predictors and construct the nomogram. The performance of the nomogram was assessed by calculating the concordance index, and creating calibration plots to check the consistency between the predicted probabilities of poor prognosis and the actual outcomes of CSE.

RESULTS

The training cohort included 131 patients and validation cohort included 66 patients. Variables included in the nomogram were age, etiology of CSE, non-convulsive SE, mechanical ventilation, abnormal albumin level at CSE onset. The concordance index of the nomogram in the training and validation cohorts was 0.853 (95% CI, 0.787-0.920) and 0.806 (95% CI, 0.683-0.923), respectively. The calibration plots showed an adequate consistency between the reported and predicted unfavorable outcomes of patients with CSE at 3 months after discharge.

CONCLUSIONS

A nomogram for predicting the individualized risks of poor functional outcomes in CSE was constructed and validated, which has been an important modification of END-IT score.

Machine-Learning Algorithms Using Systemic Inflammatory Markers to Predict the Oncologic Outcomes of Colorectal Cancer After Surgery

BACKGROUND

This study aimed to investigate the clinical significance of machine-learning (ML) algorithms based on serum inflammatory markers to predict survival outcomes for patients with colorectal cancer (CRC).

METHODS

The study included 941 patients with stages I to III CRC. Based on random forest algorithms using 15 compositions of inflammatory markers, four different prediction scores (DFS score-1, DFS score-2, DFS score-3, and DFS score-4) were developed for the Yonsei cohort (training set, n = 803) and tested in the Ulsan cohort (test set, n = 138). The Cox proportional hazards model was used to determine correlation between prediction scores and disease-free survival (DFS). Harrell's concordance index (C-index) was used to compare the predictive ability of prediction scores for each composition.

RESULTS

The multivariable analysis showed the DFS score-4 to be an independent prognostic factor after adjustment for clinicopathologic factors in both the training and test sets (hazard ratio [HR], 8.98; 95% confidence interval [CI] 6.7-12.04; P < 0.001 for the training set and HR, 2.55; 95% CI 1.1-5.89; P = 0.028 for the test set]. With regard to DFS, the highest C-index among single compositions was observed in the lymphocyte-to-C-reactive protein ratio (LCR) (0.659; 95% CI 0.656-0.662), and the C-index of DFS score-4 (0.727; 95% CI 0.724-0.729) was significantly higher than that of LCR in the test set. The C-index of DFS score-3 (0.725; 95% CI 0.723-0.728) was similar to that of DFS score-4, but higher than that of DFS score-2 (0.680; 95% CI 0.676-0.683).

CONCLUSIONS

The ML-based approaches showed prognostic utility in predicting DFS. They could enhance clinical use of inflammatory markers in patients with CRC.

Improving prognosis and assessing adjuvant chemotherapy benefit in locally advanced rectal cancer with deep learning for MRI: A retrospective, multi-cohort study

PURPOSE

Adjuvant therapy is recommended to minimize the risk of distant metastasis (DM) and local recurrence (LR) in patients with locally advanced rectal cancer (LARC). However, its role is controversial. We aimed to develop a pretreatment MRI-based deep learning model to predict LR, DM, and overall survival (OS) over 5 years after surgery and to identify patients benefitting from adjuvant chemotherapy (AC).

MATERIALS AND METHODS

The multi-survival tasks network (MuST) model was developed in a primary cohort (n = 308) and validated using two external cohorts (n = 247, 245). An AC decision tree integrating the MuST-DM score, perineural invasion (PNI), and preoperative carbohydrate antigen 19-9 (CA19-9) was constructed to assess chemotherapy benefits and aid personalized treatment of patients. We also quantified the prognostic improvement of the decision tree.

RESULTS

The MuST network demonstrated high prognostic accuracy in the primary and two external cohorts for the prediction of three different survival tasks. Within the stratified analysis and decision tree, patients with CA19-9 levels > 37 U/mL and high MuST-DM scores exhibited favorable chemotherapy efficacy. Similar results were observed in PNI-positive patients with low MuST-DM scores. PNI-negative patients with low MuST-DM scores exhibited poor chemotherapy efficacy. Based on the decision tree, 14 additional patients benefiting from AC and 391 patients who received over-treatment were identified in this retrospective study.

CONCLUSION

The MuST model accurately and non-invasively predicted OS, DM, and LR. A specific and direct tool linking chemotherapy decisions and benefit quantification has also been provided.

Utility of Machine Learning Algorithms in Predicting Preoperative Lymph Node Metastasis in Patients With Rectal Cancer Based on Three-Dimensional Endorectal Ultrasound and Clinical and Laboratory Data

BACKGROUND

We aimed to investigate the value of a machine learning (ML) algorithm in the preoperative prediction of lymph node metastasis in patients with rectal cancer.

METHODS

Based on the histopathological results, 126 rectal cancer patients were divided into two groups: lymph node metastasis-positive and metastasis-negative groups. We collected clinical and laboratory data, three-dimensional endorectal ultrasound (3D-ERUS) findings, and parameters of the tumor for between-group comparisons. We constructed a clinical prediction model based on the ML algorithm, which demonstrated the best diagnostic performance. Finally, we analyzed the diagnostic results and processes of the ML model.

RESULTS

Between the two groups, there were significant differences in serum carcinoembryonic antigen (CEA) levels, tumor length, tumor breadth, circumferential extent of the tumor, resistance index (RI), and ultrasound T-stage (P < 0.05). The extreme gradient boosting (XGBoost) model had the best comprehensive diagnostic performance for predicting lymph node metastasis in patients with rectal cancer. Compared with experienced radiologists, the XGBoost model showed significantly higher diagnostic value in predicting lymph node metastasis; the area under curve (AUC) value of the receiver operating characteristic (ROC) curve of the XGBoost model and experienced radiologists was 0.82 and 0.60, respectively.

CONCLUSIONS

Preoperative predictive utility in lymph node metastasis was demonstrated by the XGBoost model based on the 3D-ERUS finding and related clinical information. This could be useful in guiding clinical decisions on the selection of different treatment strategies.

Nomogram for Predicting Risk of Cancer Therapy-Related Cardiac Dysfunction in Patients With Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer

BACKGROUND

Cancer therapy-related cardiac dysfunction (CTRCD) is an important treatment-limiting toxicity for patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer that adversely affects cancer and cardiovascular outcomes. Easy-to-use tools that incorporate readily accessible clinical variables for individual estimation of CTRCD risk are needed.

METHODS AND RESULTS

From 2004 to 2013, 1440 patients with stage I to III HER2-positive breast cancer treated with trastuzumab-based therapy were identified. A multivariable Cox proportional hazards model was constructed to identify risk factors for CTRCD and included the 1377 patients in whom data were complete. Nine clinical variables, including age, race, body mass index, left ventricular ejection fraction, systolic blood pressure, coronary artery disease, diabetes, arrhythmia, and anthracycline exposure were built into a nomogram estimating risk of CTRCD at 1 year. The nomogram was validated for calibration and discrimination using bootstrap resampling. A total of 177 CTRCD events occurred within 1 year of HER2-targeted treatment. The nomogram for prediction of 1-year CTRCD probability demonstrated good discrimination, with a concordance index of 0.687. The predicted and observed probabilities of CTRCD were similar, demonstrating good model calibration.

CONCLUSIONS

A nomogram composed of 9 readily accessible clinical variables provides an individualized 1-year risk estimate of CTRCD among women with HER2-positive breast cancer receiving HER2-targeted therapy. This nomogram represents a simple-to-use tool for clinicians and patients that can inform clinical decision-making on breast cancer treatment options, optimal frequency of cardiac surveillance, and role of cardioprotective strategies.

A narrative review of prognostic indices in the evaluation of gastrointestinal cancers

Background and Objective

Accurate cancer prognostication allows for conscious decision-making. There is a need for precise indices, along with predictive biomarkers, which aid cancer prognostication. We sought to conduct an overview of the current state of prognostic indices and biomarkers in the evaluation of gastrointestinal (GI) cancers, specifically esophageal, colon and rectal.

Methods

We conducted a comprehensive review of articles in the PubMed database between September 2001 and February 2022. Only articles written in English were included. We reviewed retrospective analyses and prospective observational studies.

Key Content and Findings

Nomograms are well-described tools that provide estimates of specific cancer-related events, such as overall survival (OS). They are also useful in unroofing specific patient-related variables, which may be associated with cancer survival. Certain prognostic indices have been tested against each other with the goal of discerning superiority. Finally, specific biomarkers have emerged as promising prognostic indicators.

Conclusions

Nomograms play a significant role in the prognostication of GI cancer. The identification of specific biomarkers in cancer prognostication is evolving. As we embark on the era of precision medicine, further investigation of reliable prognostic indices and biomarkers is needed.

Combining prognostic nutritional index (PNI) and controlling nutritional status (CONUT) score as a valuable prognostic factor for overall survival in patients with stage I-III colorectal cancer

Background and aims

This study compared the prognostic significance of various nutritional and inflammatory indicators such as neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio and platelet-to-lymphocyte ratio, prognostic nutritional index, and controlling nutritional status score. In addition, we aimed to establish a more accurate prognostic indicator.

Methods

We retrospectively evaluated 1112 patients with stage I-III colorectal cancer between January 2004 and April 2014. The controlling nutritional status scores were classified as low (0-1), intermediate (2-4), and high (5-12) scores. The cut-off values for prognostic nutritional index and inflammatory markers were calculated using the X-tile program. P-CONUT, a combination of prognostic nutritional index and the controlling nutritional status score, was suggested. The integrated areas under the curve were then compared.

Results

The multivariable analysis showed that prognostic nutritional index was an independent prognostic factor for overall survival, whereas the controlling nutritional status score, neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and platelet-to-lymphocyte ratio were not. The patients were divided into three P-CONUT groups as follows: G1, controlling nutritional status (0-4) and high prognostic nutritional index; G2, controlling nutritional status (0-4) and low prognostic nutritional index; and G3, controlling nutritional status (5-12) and low prognostic nutritional index. There were significant survival differences between the P-CONUT groups (5-year overall survival of G1, G2, and G3 were 91.7%, 81.2%, and 64.1%, respectively; p < 0.0001). The integrated areas under the curve of P-CONUT (0.610, CI: 0.578-0.642) was superior to those of the controlling nutritional status score alone (bootstrap integrated areas under the curve mean difference=0.050; 95% CI=0.022-0.079) and prognostic nutritional index alone (bootstrap integrated areas under the curve mean difference=0.012; 95% CI=0.001-0.025).

Conclusion

Prognostic effect of P-CONUT may be better than inflammatory markers such as neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio and platelet-to-lymphocyte ratio. Thus, it could be used as a reliable nutritional risk stratification tool in patients with colorectal cancer.

A nomogram and risk classification system forecasting the cancer-specific survival of lymph- node- positive rectal cancer patient after radical proctectomy

Background

The aim of the study was to develop and validate a nomogram for predicting cancer-specific survival (CSS) in lymph- node- positive rectal cancer patients after radical proctectomy.

Methods

In this study, we analyzed data collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. In addition, in a 7:3 randomized design, all patients were split into two groups (development and validation cohorts). CSS predictors were selected via univariate and multivariate Cox regressions. The nomogram was constructed by analyzing univariate and multivariate predictors. The effectiveness of this nomogram was evaluated by concordance index (C-index), calibration plots, and receiver operating characteristic (ROC) curve. Based on the total score of each patient in the development cohort in the nomogram, a risk stratification system was developed. In order to analyze the survival outcomes among different risk groups, Kaplan-Meier method was used.

Results

We selected 4,310 lymph- node- positive rectal cancer patients after radical proctectomy, including a development cohort (70%, 3,017) and a validation cohort (30%, 1,293). The nomogram correlation C-index for the development cohort and the validation cohort was 0.702 (95% CI, 0.687-0.717) and 0.690 (95% CI, 0.665-0.715), respectively. The calibration curves for 3- and 5-year CSS showed great concordance. The 3- and 5-year areas under the curve (AUC) of ROC curves in the development cohort were 0.758 and 0.740, respectively, and 0.735 and 0.730 in the validation cohort, respectively. Following the establishment of the nomogram, we also established a risk stratification system. According to their nomogram total points, patients were divided into three risk groups. There were significant differences between the low-, intermediate-, and high-risk groups (p< 0.05).

Conclusions

As a result of our research, we developed a highly discriminatory and accurate nomogram and associated risk classification system to predict CSS in lymph-node- positive rectal cancer patients after radical proctectomy. This model can help predict the prognosis of patients with lymph- node- positive rectal cancer.

A Dynamic Clinical Calculator for Estimating Conditional Recurrence-Free Survival After Total Neoadjuvant Therapy for Rectal Cancer and Either Surgery or Watch-and-Wait Management

IMPORTANCE

The risk of recurrence in patients with locally advanced rectal cancer has historically been determined after surgery, relying on pathologic variables. A growing number of patients are being treated without surgery, and their risk of recurrence needs to be calculated differently.

OBJECTIVE

To develop a dynamic calculator for estimating the probability of recurrence-free survival (RFS) in patients with rectal cancer who undergo total neoadjuvant therapy (TNT) (induction systemic chemotherapy and chemoradiotherapy) and either surgery or watch-and-wait management.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included patients who presented with stage II or III rectal cancer between June 1, 2009, and March 1, 2015, at a comprehensive cancer center. Conditional modeling was incorporated into a previously validated clinical calculator to allow the probability of RFS to be updated based on whether the patient remained in watch-and-wait management or underwent delayed surgery. Data were analyzed from November 2021 to March 2022.

EXPOSURE

TNT followed by immediate surgery or watch-and-wait management with the possibility of delayed surgery.

MAIN OUTCOMES AND MEASURES

RFS, concordance index, calibration curves.

RESULTS

Of the 302 patients in the cohort, 204 (68%) underwent surgery within 3 months from TNT completion (median [range] age, 51 [22-82] years; 78 [38%] women), 54 (18%) underwent surgery more than 3 months from TNT completion (ie, delayed surgery; median [range] age, 62 [31-87] years; 30 [56%] female), and 44 (14%) remained in watch-and-wait management as of April 21, 2021 (median [range] age, 58 [32-89] years; 16 [36%] women). Among patients who initially opted for watch-and-wait management, migration to surgery due to regrowth or patient choice occurred mostly within the first year following completion of TNT, and RFS did not differ significantly whether surgery was performed 3.0 to 5.9 months (73%; 95% CI, 52%-92%) vs 6.0 to 11.9 months (71%; 95% CI, 51%-99%) vs more than 12.0 months (70%; 95% CI, 49%-100%) from TNT completion (P = .70). RFS for patients in the watch-and-wait cohort at 12 months from completion of TNT more closely resembled patients who had undergone surgery and had a pathologic complete response than the watch-and-wait cohort at 3 months from completion of TNT. Accordingly, model performance improved over time, and the concordance index increased from 0.62 (95% CI, 0.53-0.71) at 3 months after TNT to 0.66 (95% CI, 0-0.75) at 12 months.

CONCLUSIONS AND RELEVANCE

In this cohort study of patients with rectal cancer, the clinical calculator reliably estimated the likelihood of RFS for patients who underwent surgery immediately after TNT, patients who underwent delayed surgery after entering watch-and-wait management, and patients who remained in watch-and-wait management. Delayed surgery following attempted watch-and-wait did not appear to compromise oncologic outcomes. The risk calculator provided conditional survival estimates at any time during surveillance and could help physicians counsel patients with rectal cancer about the consequences of alternative treatment pathways and thereby support informed decisions that incorporate patients' preferences.

Implication of Different Tumor Biomarkers in Drug Resistance and Invasiveness in Primary and Metastatic Colorectal Cancer Cell Lines

Protein expression profiles are directly related to the different properties of cells and are conditioned by the cellular niche. As an example, they are the cause of the characteristic cell plasticity, epithelium-mesenchymal transition (EMT), and drug resistance of cancer cells. This article characterizes ten biomarkers related to these features in three human colorectal cancer cell lines: SW-480, SW-620, and DLD-1, evaluated by flow cytometry; and in turn, resistance to oxaliplatin is studied through dose-response trials. The main biomarkers present in the three studied lines correspond to EpCAM, CD-133, and AC-133, with the latter two in low proportions in the DLD-1 line. The biomarker CD166 is present in greater amounts in SW-620 and DLD-1 compared to SW-480. Finally, DLD-1 shows high values of Trop2, which may explain the aggressiveness and resistance of these cells to oxaliplatin treatments, as EpCAM is also highly expressed. Exposure to oxaliplatin slows cell growth but also helps generate resistance to the treatment. In conclusion, the response of the cell lines is variable, due to their genetic variability, which will condition protein expression and cell growth. Further analyses in this area will provide important information for better understanding of patients' cellular response and how to prevent resistance.

Pathological Evaluation of Rectal Cancer Specimens Using Micro-Computed Tomography

Whole-block imaging (WBI) using micro-computed tomography (micro-CT) allows the nondestructive reconstruction of a three-dimensional view of tissues, implying that WBI may be used for accurate pathological evaluation of patients with rectal cancer. HOWEVER, the clinical impact of this approach is unclear. We aimed to clarify the efficacy of WBI in the whole-mount specimens of locally advanced rectal cancer. A total of 237 whole-mount formalin-fixed paraffin-embedded blocks from 13 patients with rectal cancer who underwent surgical treatment were enrolled and scanned with micro-CT to generate three-dimensional images. WBI was evaluated following the conventional pathological review of the corresponding whole-slide imaging (WSI). WBI identified all tumor sites detected using WSI. Furthermore, WBI revealed one additional tumor site, which was not detected using WSI. Tumor resection margin was significantly closer to the soft-tissue edge when measured using WBI (7.7 mm vs. 6.6 mm, p < 0.01). Seventy-six percent of tumor deposits on WSI were changed according to the evidence of tumor interaction with the surrounding tissues confirmed using WBI. Furthermore, WBI revealed 25 additional lymph nodes, six of which were metastatic. The combination of conventional hematoxylin and eosin-stained imaging and WBI may contribute to an accurate pathological assessment.