Nomograms for predicting pathological response to neoadjuvant treatments in patients with rectal cancer

Prediction of pathologic complete response to neoadjuvant chemoradiation in locally advanced rectal cancer

Purpose

To investigate the predictive factors of pathologic complete response (pCR) in locally advanced rectal cancer (LARC) patients who had been treated with neoadjuvant chemoradiation (nCRT).

Methods and materials

For this retrospective study, 53 LARC patients (37 males and 16 females; age range 25 to 79 years) were selected. Clinical characteristics, baseline mrTNM staging, MR gross tumor volumes (GTV), and pCR were evaluated. The diagnostic accuracy of GTV for predicting pCR was calculated.

Results

Among 53 LARC patients, 15 patients achieved pCR (28.3%), while 38 patients achieved non-pCR. Only three (5.7%) out of 53 patients did not downstage after nCRT. GTV and tumor differentiation were the significant prognostic parameters for predicting pCR. A tumor volume threshold of 21.1 cm3 was determined as a predictor for pCR, with a sensitivity of 84% and specificity of 47%. In addition, GTV was associated with mrN stage, circumferential resection margin (CRM) status, extramural vascular invasion (EMVI) status, and pretreatment serum CEA level.

Conclusion

Tumor volume and tumor differentiation have significant predictive values in preoperative assessment of pCR among LARC patients. These findings aid clinicians to discriminate those patients who may likely benefit from preoperative regimens and to make optimal treatment plans.

Validated Pretreatment Prediction Models for Response to Neoadjuvant Therapy in Patients with Rectal Cancer: A Systematic Review and Critical Appraisal

Pretreatment response prediction is crucial to select those patients with rectal cancer who will benefit from organ preservation strategies following (intensified) neoadjuvant therapy and to avoid unnecessary toxicity in those who will not. The combination of individual predictors in multivariable prediction models might improve predictive accuracy. The aim of this systematic review was to summarize and critically appraise validated pretreatment prediction models (other than radiomics-based models or image-based deep learning models) for response to neoadjuvant therapy in patients with rectal cancer and provide evidence-based recommendations for future research. MEDLINE via Ovid, Embase.com, and Scopus were searched for eligible studies published up to November 2022. A total of 5006 studies were screened and 16 were included for data extraction and risk of bias assessment using Prediction model Risk Of Bias Assessment Tool (PROBAST). All selected models were unique and grouped into five predictor categories: clinical, combined, genetics, metabolites, and pathology. Studies generally included patients with intermediate or advanced tumor stages who were treated with neoadjuvant chemoradiotherapy. Evaluated outcomes were pathological complete response and pathological tumor response. All studies were considered to have a high risk of bias and none of the models were externally validated in an independent study. Discriminative performances, estimated with the area under the curve (AUC), ranged per predictor category from 0.60 to 0.70 (clinical), 0.78 to 0.81 (combined), 0.66 to 0.91 (genetics), 0.54 to 0.80 (metabolites), and 0.71 to 0.91 (pathology). Model calibration outcomes were reported in five studies. Two collagen feature-based models showed the best predictive performance (AUCs 0.83-0.91 and good calibration). In conclusion, some pretreatment models for response prediction in rectal cancer show encouraging predictive potential but, given the high risk of bias in these studies, their value should be evaluated in future, well-designed studies.

Accurate tumor segmentation and treatment outcome prediction with DeepTOP

BACKGROUND

Accurate outcome prediction prior to treatment can facilitate trial design and clinical decision making to achieve better treatment outcome.

METHOD

We developed the DeepTOP tool with deep learning approach for region-of-interest segmentation and clinical outcome prediction using magnetic resonance imaging (MRI). DeepTOP was constructed with an automatic pipeline from tumor segmentation to outcome prediction. In DeepTOP, the segmentation model used U-Net with a codec structure, and the prediction model was built with a three-layer convolutional neural network. In addition, the weight distribution algorithm was developed and applied in the prediction model to optimize the performance of DeepTOP.

RESULTS

A total of 1889 MRI slices from 99 patients in the phase III multicenter randomized clinical trial (NCT01211210) on neoadjuvant treatment for rectal cancer was used to train and validate DeepTOP. We systematically optimized and validated DeepTOP with multiple devised pipelines in the clinical trial, demonstrating a better performance than other competitive algorithms in accurate tumor segmentation (Dice coefficient: 0.79; IoU: 0.75; slice-specific sensitivity: 0.98) and predicting pathological complete response to chemo/radiotherapy (accuracy: 0.789; specificity: 0.725; and sensitivity: 0.812). DeepTOP is a deep learning tool that could avoid manual labeling and feature extraction and realize automatic tumor segmentation and treatment outcome prediction by using the original MRI images.

CONCLUSION

DeepTOP is open to provide a tractable framework for the development of other segmentation and predicting tools in clinical settings. DeepTOP-based tumor assessment can provide a reference for clinical decision making and facilitate imaging marker-driven trial design.

Automatic treatment outcome prediction with DeepInteg based on multimodal radiological images in rectal cancer

Neoadjuvant systemic treatment before surgery is a prevalent regimen in the patients with advanced-stage or high-risk tumor, which has shaped the treatment strategies and cancer survival in the past decades. However, some patients present with poor response to the neoadjuvant treatment. Therefore, it is of great significance to develop tools to help distinguish the patients that could achieve pathological complete response before surgery to avoid inappropriate treatment. Here, this study demonstrated a multi-task deep learning tool called DeepInteg. In the DeepInteg framework, the segmentation module was constructed based on the CE-Net with a context extractor to achieve end-to-end delineation of region of interest (ROI) from radiological images, then the features of segmented Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images of each case were fused and input to the classification module based on a convolution neural network for treatment outcome prediction. The dataset with 1700 MRI and CT slices collected from the prospectively randomized clinical trial (NCT01211210) on systemic treatment for rectal cancer was used to develop and systematically optimize DeepInteg. As a result, DeepInteg achieved automatic segmentation of tumoral ROI with Dices of 0.766 and 0.719 and mIoUs of 0.788 and 0.756 in CT and MRI images, respectively. In addition, DeepInteg achieved AUC of 0.833, accuracy of 0.826 and specificity of 0.856 in the prediction for pathological complete response after treatment, which showed better performance compared with the model based on CT or MRI alone. This study provide a robust framework to develop disease-specific tools for automatic delineation of ROI and clinical outcome prediction. The well-trained DeepInteg could be readily applied in clinic to predict pathological complete response after neoadjuvant therapy in rectal cancer patients.

Extracellular volume fraction determined by equilibrium contrast-enhanced CT for the prediction of the pathological complete response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer

OBJECTIVES

To determine the extracellular volume (ECV) fraction derived from equilibrium contrast-enhanced CT for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC).

METHODS

The ECV fraction before NCRT (ECV_pre) and/or ECV after NCRT (ECV_post) of rectal tumors was assessed, and ECV_Δ was calculated as ECV_post - ECV_pre. The histopathologic tumor regression grading (TRG) was assessed. pCR (TRG 0 grade) was defined as the absence of viable tumor cells in the primary tumor and lymph nodes. Demographic and clinicopathological characteristics and ECV fraction were compared between the pCR and non-pCR groups. A mixed model was constructed by logistic regression. The performance for predicting pCR was assessed with the area under the receiver-operator curve (AUC). The AUCs of the different methods were compared by the method proposed by DeLong et al. RESULTS: Seventy-five patients were included; 17 achieved pCR, and 58 achieved non-pCR. The ECV_post (17.05 ± 2.36% vs. 29.94 ± 1.20%; p < 0.001) and ECV_Δ (- 17.01 ± 3.01% vs. 0.44 ± 1.45%; p < 0.001) values in the pCR group were significantly lower than those in the non-pCR group. The mixed model that combined ECV_post with ECV_Δ achieved an AUC of 0.92 (95% confidence interval (CI) = 0.81-0.98), which was higher than that of ECV_post (AUC, 0.91 (95% CI = 0.80-0.97); p = 0.60) or ECV_Δ (AUC, 0.90 (95% CI = 0.79-0.97); p = 0.61).

CONCLUSIONS

ECV_post and ECV_Δ determined by using equilibrium contrast-enhanced CT were useful in distinguishing between pCR and non-pCR patients with LARC who received NCRT.

KEY POINTS

• ECV_post and ECV_Δ (ECV_post - ECV_pre) differed significantly between the non-pCR and pCR groups. • ECV_pre cannot be used to predict the efficacy of neoadjuvant chemoradiotherapy. • ECV_post combined with ECV_Δ had the best performance with an AUC of 0.92 for predicting pCR after NCRT in LARC.

A novel prediction model for pathological complete response based on clinical and blood parameters in locally advanced rectal cancer

Background

The aim of this study was to investigate whether clinical and blood parameters can be used for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).

Methods

We retrospectively enrolled 226 patients with LARC [allocated in a 7:3 ratio to a training (n = 158) or validation (n = 68) cohort] who received nCRT before radical surgery. Backward stepwise logistic regression was performed to identify clinical and blood parameters associated with achieving pCR. Models based on clinical parameters (CP), blood parameters (BP), and clinical-blood parameters (CBP) were constructed for comparison with previously reported Tan's model. The performance of the four models was evaluated by receiver operating characteristic (ROC) curve analysis, calibration, and decision curve analysis (DCA) in both cohorts. A dynamic nomogram was constructed for the presentation of the best model.

Results

The CP and BP models based on multivariate logistic regression analysis showed that interval, Grade, CEA and fibrinogen-albumin ratio index (FARI), sodium-to-globulin ratio (SGR) were the independent clinical and blood predictors for achieving pCR, respectively. The area under the ROC curve of the CBP model achieved a score of 0.818 and 0.752 in both cohorts, better than CP (0.762 and 0.589), BP (0.695 and 0.718), Tan (0.738 and 0.552). CBP also showed better calibration and DCA than other models in both cohorts. Moreover, CBP revealed significant improvement compared with other models in training cohort (P < 0.05), and CBP showed significant improvement compared with CP and Tan's model in validation cohort (P < 0.05).

Conclusion

We demonstrated that CBP predicting model have potential in predicting pCR to nCRT in patient with LARC.

Evaluation of Neutrophil-Lymphocyte Ratio and Platelet-Lymphocyte Ratio on Predicting Responsiveness to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients

Objective

Neutrophil-lymphocyte ratio (NLR) and Platelet-lymphocyte ratio (PLR) have been proposed as prognostic biomarkers in multiple cancers. However, the implications of NLR and PLR in the responsiveness to neoadjuvant chemoradiotherapy (nCRT) remain to be clarified in locally advanced rectal cancer (LARC) patients. This retrospective study investigated the prognostic value of NLR and PLR in nCRT responsiveness of LARC patients.

Methods

A total number of 86 patients diagnosed with LARC and treated with nCRT and total mesorectal excision were retrospectively followed from 2013 to 2016. Receiver operating characteristic (ROC) curve was used to determine the cutoff values of NLR and PLR, and the patients were divided into NLR elevation and NLR decrease groups, or PLR elevation and PLR decrease groups. The correlation between NLR and PLR changes, and clinicopathological factors were analyzed. The relationship between NLR and PLR changes and the curative responsiveness towards nCRT were further evaluated.

Results

NLR and PLR changes after nCRT were significantly correlated with the distance of tumors to the anus and BMI (body mass index) (P < 0.05). The clinical remission rate of patients with NLR reduction was 72.09% (31/43), which was significantly higher than that in patients with NLR increment (22/43, 51.16%). There was no significant difference in the clinic remission rate between the patients with PLR reduction and those with PLR increment (P > 0.05). However, the pathological responsiveness rate was significantly higher in patients with PLR reduction (21/43, 48.84%) when compared to the ones with PLR increment (9/43, 20.9%) (P = 0.036).

Conclusion

Our data indicate that in LARC patients with nCRT, the reduction of NLR and the reduction of PLR could serves as predictors for the clinic remission rate and pathological responsiveness rate, respectively. The combination of NLR and PLR changes may be employed as a simple and effective prognostic parameter to predict the treatment outcome of nCRT in LARC.

Factors influencing downstaging after neoadjuvant long-course chemoradiotherapy in rectal carcinoma

PURPOSE

This single-centre cohort study was designed to identify factors that can predict primary tumour downstaging by neoadjuvant chemoradiotherapy (nCRT) in rectal carcinoma.

METHODS

Prospectively collected data from 555 patients with clinical T category (cT) cT3-4 rectal carcinoma treated between 1995 and 2019 were retrospectively analysed. All patients received long-term neoadjuvant chemoradiotherapy followed by surgery with curative intent at the Department of Surgery, University Hospital Erlangen, Germany. Patient-, tumour- and treatment-related factors with a potential impact on the downstaging of rectal carcinoma to pathological T category (pT) ≤ ypT2 and ypT0 were analysed in univariate and multivariate logistic regression analyses. The prognosis of patients with and without downstaging of the primary tumour was compared.

RESULTS

A total of 288 (51.9%) patients showed downstaging to ≤ ypT2. Eighty-six (15.5%) patients achieved clinical complete regression (ypT0). In the multivariate logistic regression analysis, the factors cT category, BMI, ECOG score, CEA, histological type, extension in the rectum and year of the start of treatment were found to be independent factors for predicting downstaging to ≤ ypT2 after neoadjuvant chemoradiotherapy. The year of treatment initiation also remained an independent significant predictor for pathological complete regression. The prognosis was superior in patients with downstaging to ≤ ypT2 in terms of locoregional and distant recurrence as well as disease-free and overall survival.

CONCLUSION

Factors predicting downstaging after long-term nCRT could be identified. This may be helpful for counselling patients and selecting the optimal treatment for patients with advanced rectal carcinoma.

Circulating miRNA Signature Predicts Response to Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer

Patients with locally advanced rectal cancer (LARC) are recommended to receive preoperative chemoradiotherapy (PCRT) followed by surgery. Response to PCRT varies widely: 60%-70% of patients with LARC do not derive therapeutic benefit from PCRT, whereas 15%-20% of patients achieve pathologic complete response (pCR). We sought to develop a liquid biopsy assay for identifying response to PCRT in patients with LARC.

MATERIALS AND METHODS

We analyzed two genome-wide microRNA (miRNA) expression profiling data sets from tumor tissue samples for in silico discovery (GSE68204) and validation (GSE29298). We prioritized biomarkers in pretreatment plasma specimens from clinical training (n = 41; 15 responders and 26 nonresponders) and validation (n = 65; 29 responders and 36 nonresponders) cohorts of patients with LARC. We developed an integrated miRNA panel and established a risk assessment model, which was combined with the miRNA panel and carcinoembryonic antigen levels.

RESULTS

Our comprehensive discovery effort identified an 8-miRNA panel that robustly predicted response to PCRT, with an excellent accuracy in the discovery (area under the curve [AUC] = 0.95) and validation (AUC = 0.92) cohorts. We successfully established a circulating miRNA panel with remarkable diagnostic accuracy in the clinical training (AUC = 0.82) and validation (AUC = 0.81) cohorts. Moreover, the predictive accuracy of the panel was significantly superior to conventional clinical factors in both cohorts (P < .01) and the risk assessment model was superior (AUC = 0.83). Finally, we applied our model to detect patients with pathologic complete response and showed that it was dramatically superior to currently used pathologic features (AUC = 0.92).

CONCLUSION

Our novel risk assessment signature for predicting response to PCRT has a potential for clinical translation as a liquid biopsy assay in patients with LARC.

Current treatment and surveillance modalities are not sufficient for advanced stage III colon cancer: Result from a multicenter cohort analysis

OBJECTIVE

We conducted this multicenter cohort study to evaluate the current tumor-node-metastasis staging system and treatment modality by analyzing the survival outcomes of patient groups with stage III and IV colon cancer.

PATIENTS AND METHODS

Stage III and IV colon cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database (SEER cohort) and prospectively maintained Sun Yat-sen University (SYSU) cohort were included in this study. Kaplan-Meier method was used to estimate the cumulative rate of overall survival (OS) between patient groups, and the inverse probability weighting method was used to calculated age and sex-adjusted survival curves. The Cox regression model was used to identify the risk factors for OS.

RESULTS

A total of 17,911 and 1135 stage III-IV cases were included in the SEER and SYSU cohorts, respectively. Among them, 1448 and 124 resectable stage IV cases underwent curative-intent treatment in the SEER and SYSU cohorts, respectively. The T4N2b group showed a significantly worse survival outcome compared with the M1a subset receiving curative-intent treatment (HR, 1.46; p < 0.001). This finding was validated in the SYSU cohort, in which the T4N2 group had a worse outcome than that of the M1 group receiving curative-intent treatment (HR, 2.44; p < 0.001). These findings were confirmed in the adjusted survival analysis. In the multivariate analysis, the right-side tumor, poor-undifferentiated tumor, and age over 60 years were identified as independent risk factors for T4N2b patients. Based on this multivariate model, the high-risk T4N2b subgroup had a worse survival outcome compared with resectable M1b patients (HR, 1.24; p = 0.03).

CONCLUSION

By comparing stage III with stage IV colon cancer patients, we identified a subgroup of stage III patients at a higher risk of death than more advanced stages, implying that current cancer care modalities are not sufficient for these high-risk substages.

A Nomogram for Predicting Pathological Complete Response to Neoadjuvant Chemoradiotherapy Using Semiquantitative Parameters Derived From Sequential PET/CT in Locally Advanced Rectal Cancer

We evaluated the predictive value of semiquantitative volumetric parameters derived from sequential PET/CT and developed a nomogram to predict pathological complete response (pCR) in patients with rectal cancer treated by neoadjuvant chemoradiotherapy (nCRT). From April 2008 to December 2013, among the patients who underwent nCRT, those who were taken sequential PET/CT before and after nCRT were included. MRI-based staging and semiquantitative parameters of PET/CT including standardized uptake value (SUV), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were evaluated before and after nCRT. Multivariable analysis was performed to select significant predictors to construct a nomogram. Sensitivity, specificity, accuracy, and area under the receiver operating characteristics curve (AUC) of the model were evaluated to determine its performance. Among 137 eligible patients, 17 (12.4%) had pCR. All post-PET/CT parameters showed significant differences between pCR and non-pCR groups. Patients were randomly assigned to a training group (91 patients) and a validation group (46 patients). In multivariable analysis with the training group, post-CEA, post-MRI T staging, post-SUV_max, and post-MTV were significantly associated with pCR. There was no significant pre-nCRT variable for predicting pCR. Using significant predictors, a nomogram was developed. Sensitivity, specificity, accuracy, and AUC of the nomogram were 0.882, 0.808, 0.848, and 0.884 with the training group and 0.857, 0.781, 0.783, and 0.828 with the validation group, respectively. This model showed the better performance than other predictive models that did not contain PET/CT parameters. A nomogram containing semiquantitative post-PET/CT could effectively select candidates for organ-sparing strategies.

Systemic Inflammation Status Relates to Anti-Inflammatory Drug Benefit and Survival in Rectal Cancer

BACKGROUND

Systemic inflammation status has been recognized as a sensitive marker associated with survival in cancers and anti-inflammatory treatment outcomes in inflammation-derived diseases. This study aimed to investigate the role of systemic inflammation status as a predictive marker for survival and anti-inflammatory treatment benefit in rectal cancer patients.

METHODS

A total of 475 patients with stage I-III rectal cancer receiving curative resection were prospectively enrolled. The platelet-neutrophils to lymphocytes ratio (PNLR) that integrates neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios was applied to enable a comprehensive evaluation of systemic inflammation status and investigate its association with survival and nonsteroidal anti-inflammatory drugs (NSAIDs) benefit. Patients were grouped according to baseline PNLR and perioperative use of NSAIDs.

RESULTS

The high-PNLR group had worse 5-y disease-free survival (DFS) compared with the low-PNLR group (61.2% versus 70.9%, P = 0.014). Multivariate analyses confirmed that PNLR was an independent predictor for DFS (hazard ratio [HR] 1.42, 95% CI: 1.03-1.97, P = 0.031). A nomogram including PNLR and other independent prognostic factors was developed and validated to predict DFS. In the high-PNLR subset, NSAIDs group had a 21.3% lower risk of recurrence than non-NSAIDs group (P = 0.009), and multivariate analysis confirmed the independently significant association of perioperative NSAIDs use with better DFS (hazard ratio 0.36, 95% CI 0.16-0.78, P = 0.010). However, this association was not significant in the low-PLR subset.

CONCLUSIONS

Baseline PNLR could be used to predict DFS and NSAIDs benefit in rectal cancer patients. This study highlights the potential survival benefit from the anti-inflammatory treatment in the patients with elevated systemic inflammation status in cancer patients.

Clinical outcome of neoadjuvant chemoradiation in rectal cancer treatment

To determine the clinical and pathological outcome of locally advanced rectal cancer patients treated with neoadjuvant chemoradiation (chemoradiotherapy [CRT]) followed by curative surgery and to identify predictive factors of pathological complete response (pCR).Locally advanced rectal cancer patients undergoing CRT followed by curative surgery from January 2012 to December 2017 were included. Patient's demographic data, pretreatment tumor characteristics, type of CRT regimens, type of surgery, postoperative complications, pathological reports and follow up records were analyzed. Univariate and multivariate analyses were applied to identify predictive factors for pCR. Five-year disease free and overall survival were estimated by Kaplan-Meier method and compared between pCR and non-pCR groups.A total of 85 patients were analyzed. Eighteen patients (21.1%) achieved pCR. The sphincter-saving surgery rate was 57.6%. After univariate analyses, tumor length >4 cm (P = .007) and positive lymph nodes (P = .040) were significantly associated with decreased rate of pCR. Complete clinical response was significantly associated with higher rate of pCR (P = .015). Multivariate analyses demonstrated that tumor length >4 cm (P = .010) was significantly associated with decreased rate of pCR. After a median follow-up of 65 months (IQR 34-79), the calculated 5-year overall survival and disease-free survival rates were 81.4% and 69.7%, respectively. Patients who achieved pCR tend to had longer 5-year disease-free survival (P = .355) and overall survival (P = .361) than those who did not.Tumor length >4 cm was associated with decreased rate of pCR in locally advanced rectal cancer who had CRT followed by surgery. Longer waiting time or more intense adjuvant treatment may be considered to improved pCR and oncological outcomes.

Can histologic features predict neoadjuvant therapy response in rectal adenocarcinoma?

Current standard therapy for locally advanced rectal cancer (LARC) is neoadjuvant therapy followed by surgical resection; however, treatment response is variable among patients. This study aimed to identify histologic features that predict tumor response. This retrospective study included 105 patients with LARC, all of whom underwent biopsy followed by neoadjuvant therapy and subsequent surgical resection. Each patient's initial biopsy was evaluated for tumor grade, tumor budding, intraepithelial lymphocytes, intraepithelial neutrophils, desmoplasia, apoptosis, adjacent stromal lymphocytes, signet ring cells, mucinous features, tumoral Paneth cells, dirty necrosis, microscopic ulceration, and prominent lymphoid aggregates. These histologic features, along with patient age at diagnosis and tumor microsatellite status, were compared to tumor regression grades from the respective resection specimens. No histologic factors in tumor biopsies predictive of treatment response in post-therapy resection specimens were identified. Histologic features in pre-therapy biopsy samples of LARC do not predict subsequent response to neoadjuvant therapy. Effective and reliable methods to predict response to neoadjuvant therapy in rectal cancer remain elusive.

Evaluation of potential tumor markers that may predict neoadjuvant treatment efficiency in rectal cancer

Objectives

The recurrence of rectal cancer or its resistance to neoadjuvant treatment develops due to the adaptation to hypoxia, apoptosis or autophagy. Survivin, one of the inhibitors of apoptosis; Beclin 1, which is a positive regulator in the autophagy pathway; and hypoxia-inducible factor-1α (HIF-1α) and carbonic anhydrase-9 (CA9), which are associated with tumor tissue hypoxia, may be related to resistance to treatment. Our aim was to evaluate the potential tumor markers that may help to monitor the response to neoadjuvant treatment in locally advanced rectal cancer (RC).

Methods

Twenty-five patients with locally advanced RC were included in the study. Gene expression and protein levels of Beclin 1, Survivin, HIF-1α, and CA9 were analyzed in fresh tissue specimens and blood samples. The relationships of these markers to tumor staging and regression grade were evaluated.

Results

Higher blood CA9 gene expression levels and lower blood HIF-1α protein levels were found in the response group according to tumor regression grade. After neoadjuvant treatment, tissue Beclin 1 and blood Survivin gene expressions and tissue CA9, blood Beclin 1 and blood HIF-1α protein levels decreased significantly.

Conclusion

Beclin 1, Survivin, HIF-1α ve CA9 may help to predict the effects of the applied treatment approach.

Predicting Response to Total Neoadjuvant Treatment (TNT) in Locally Advanced Rectal Cancer Based on Multiparametric Magnetic Resonance Imaging: A Retrospective Study

Purpose

To investigate the potential value of magnetic resonance imaging (MRI) in predicting response relevance to total neoadjuvant treatment (TNT) in locally advanced rectal cancer.

Methods

We analyzed MRI of 71 patients underwent TNT from 2015 to 2017 retrospectively. We categorized the response of TNT as CR (complete response) vs non-CR, and high vs moderate vs low sensitivity. Logistic regression analysis was used to identify the best predictors of response. Diagnostic performance was assessed using receiver operating characteristic curve analysis.

Results

Post-ICT (induction chemotherapy) ∆TL (tumor length), post-CRT (concurrent chemoradiotherapy) ∆LNN (the numbers of lymph node metastases), post-CCT (consolidation chemotherapy) ∆S_DWI (maximum cross-sectional area of tumor on diffusion-weighted imaging), post-CCT ADC_T (the mean apparent diffusion coefficient values of tumor) and post-CCT ∆LNV (volume of lymph node) were the best CR predictors. Post-ICT ∆TL, post-CRT EMVI (extramural vascular invasion) and post-CCT ∆S_T2 (S on T2-weight) were the best significant factors for high sensitivity.

Conclusion

Post-ICT ∆TL may be an early predictor of CR and high sensitivity to TNT. Dynamic analysis based on MRI between baseline and post-CCT could provide the most valuable prediction of CR. The grouping modality of CR vs non-CR may be more suitable for treatment response prediction than high vs moderate vs low sensitivity.

A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients

BACKGROUND

The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients.

METHODS

In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort.

RESULTS

The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT.

CONCLUSIONS

The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.

Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer

Background

We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature.

Methods

This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score.

Results

We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did.

Conclusions

The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02919-x.

Serum calcium improved systemic inflammation marker for predicting survival outcome in rectal cancer

Background

Systemic inflammation markers have shown prognostic values with variability in rectal cancer. Considering the association of serum calcium with inflammation, we aimed to examine whether it could improve systemic inflammation markers for survival prediction.

Methods

We enrolled 508 patients with stage I to III rectal cancer who underwent curative resection. The cohort was grouped by corrected serum calcium (cCa), platelet-to-lymphocyte ratio (PLR), and CaPLR (a score model combining cCa with PLR) for survival analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare models in survival prediction. The primary endpoint was disease-free survival (DFS).

Results

A total of 26.7% (136/508) patients reached recurrence after curative surgery. Both high cCa (HR 1.486; 95% CI, 1.018-2.171; P=0.040) and high PLR (HR 1.452; 95% CI, 1.059-1.991; P=0.021) were significantly associated with worse DFS. In model comparison, the AIC and LR were improved after cCa was added to PLR model in DFS prediction (AIC: 1,704.83 vs. 1,707.14 vs. 1,707.15; LR: 8.68 vs. 4.37 vs. 4.36; P=0.037). The CaPLR was developed for DFS prediction with adjusted HRs of 2.216 (95% CI, 1.256-3.909; P=0.006) and 1.679 (95% CI, 1.004-2.836; P=0.047) for high and intermediate score group respectively compared to low score group. A nomogram for predicting DFS was generated by using CaPLR and other clinical predictors, with a concordance index of 0.705 (95% CI, 0.620-0.789; P<0.001).

Conclusions

Serum calcium could improve systemic inflammation markers in survival prediction for patients with rectal cancer.

Nomogram for Predicting Survival in Advanced Gastric Cancer after Neoadjuvant Chemotherapy and Radical Surgery

BACKGROUND

Neoadjuvant chemotherapy (NAC) with subsequent radical surgery has become a popular treatment modality for advanced gastric cancer (AGC) worldwide. However, the survival benefit is still controversial, and prognostic factors remain undetermined.

AIM

To identify clinical parameters that are associated with the survival of AGC patients after NAC and radical surgery and to establish a nomogram integrating multiple factors to predict survival.

METHODS

We reviewed the medical profiles of 215 AGC patients who received NAC and radical resection, and clinical parameters concerning NAC, surgery, pathological findings, and adjuvant chemotherapy were analyzed using a Cox regression model to determine their impact on survival. Based on these factors, a nomogram was developed and validated.

RESULTS

The overall 1-year and 3-year survival rates were 85.8% and 55.6%, respectively. Younger age (<60 years old), increased examined lymph nodes (exLNs), successful R0 resection, the achievement of pathological complete response (pCR), and acceptance of adjuvant chemotherapy were positive predictors of survival. The C-index of the established nomogram was 0.785. The area under receiver operating curve (ROC) at 1/3 years of prediction was 0.694/0.736, respectively. The model showed an ideal calibration following internal bootstrap validation.

CONCLUSION

A nomogram predicting survival after NAC and surgery was established. Since this nomogram exhibited satisfactory and stable predictive power, it can be inferred that this is a practical tool for predicting AGC patient survival after NAC and radical surgery.

Improved Survival Outcome and Access to Cancer Screening from Hemorrhoid in Patients with Rectal Cancer

Background

The interventions for hemorrhoid increase access to rectal cancer screening and thus might reduce cancer death. We aimed to examine the impact of hemorrhoid on survival outcomes in rectal cancer.

Methods

We identified 510 patients with stage I to III rectal cancer from a prospectively collected database. Patients were divided into hemorrhoid and non-hemorrhoid group. The primary endpoints were disease-free survival (DFS) and overall survival (OS).

Results

Hemorrhoid group had significantly more stage I-II diseases in comparison to nonhemorrhoid group (71.1% vs. 55.9%, P = 0.049). The hemorrhoid group had significantly better DFS and OS compared to nonhemorrhoid group, the hazard ratios (HRs) of which were 0.39 (95% CI 0.17-0.88, P = 0.018) and 0.33 (95% CI 0.12-0.92, P = 0.034), respectively. Multivariate analysis revealed that hemorrhoid was independently associated with DFS [adjusted HR 0.43 (95% CI 0.17-0.95, P = 0.045)]. A nomogram for predicting DFS outcome was generated based on hemorrhoid history, with a concordance index of 0.71 (95% CI 0.66-0.75, P < 0.001).

Conclusions

There may exist a screening effect and survival benefit from hemorrhoid in rectal cancer, which supports the significance of rectal cancer screening in lowering its mortality.

Nomograms and risk score models for predicting survival in rectal cancer patients with neoadjuvant therapy

BACKGROUND

Colorectal cancer is a common digestive cancer worldwide. As a comprehensive treatment for locally advanced rectal cancer (LARC), neoadjuvant therapy (NT) has been increasingly used as the standard treatment for clinical stage II/III rectal cancer. However, few patients achieve a complete pathological response, and most patients require surgical resection and adjuvant therapy. Therefore, identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.

AIM

To establish effective prognostic nomograms and risk score prediction models to predict overall survival (OS) and disease-free survival (DFS) for LARC treated with NT.

METHODS

Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017. The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors, which were validated by the Cox regression method. Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves, and that of the two nomograms was conducted by calculating the concordance index (C-index) and calibration curves. The results were validated in a cohort of 65 patients from 2015 to 2017.

RESULTS

Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model: Vascular_tumors_bolt, cancer nodules, yN, body mass index, matchmouth distance from the edge, nerve aggression and postoperative carcinoembryonic antigen. The nomogram showed good predictive value for OS, with a C-index of 0.91 (95%CI: 0.85, 0.97) and good calibration. In the validation cohort, the C-index was 0.69 (95%CI: 0.53, 0.84). The risk factor prediction model showed good predictive value. The areas under the curve for 3- and 5-year survival were 0.811 and 0.782. The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77 (95%CI: 0.69, 0.85). In the validation cohort, the C-index was 0.71 (95%CI: 0.61, 0.81). The prediction model for DFS also had good predictive value, with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.

CONCLUSION

We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.

A nomogram model for screening the risk of diabetes in a large-scale Chinese population: an observational study from 345,718 participants

Our study is major to establish and validate a simple type||diabetes mellitus (T2DM) screening model for identifying high-risk individuals among Chinese adults. A total of 643,439 subjects who participated in the national health examination had been enrolled in this cross-sectional study. After excluding subjects with missing data or previous medical history, 345,718 adults was included in the final analysis. We used the least absolute shrinkage and selection operator models to optimize feature selection, and used multivariable logistic regression analysis to build a predicting model. The results showed that the major risk factors of T2DM were age, gender, no drinking or drinking/time > 25 g, no exercise, smoking, waist-to-height ratio, heart rate, systolic blood pressure, fatty liver and gallbladder disease. The area under ROC was 0.811 for development group and 0.814 for validation group, and the p values of the two calibration curves were 0.053 and 0.438, the improvement of net reclassification and integrated discrimination are significant in our model. Our results give a clue that the screening models we conducted may be useful for identifying Chinses adults at high risk for diabetes. Further studies are needed to evaluate the utility and feasibility of this model in various settings.

An inflammation index-based prediction of treatment response to neoadjuvant chemoradiotherapy for rectal mucinous adenocarcinoma

OBJECTIVE

This study aimed to evaluate the predictive value of hematological inflammation-based indexes in the treatment response to neoadjuvant chemoradiotherapy (NCRT) in rectal mucinous adenocarcinomas (MACs).

METHODS

Patients with rectal MACs undergoing NCRT and curative resection were included. Inflammation-based indexes such as systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and prognostic nutritional index (PNI) were calculated. Receiver operator characteristics analysis was used to determine the optimal cutoff points. Multivariable logistic analysis identified predictors of good response to NCRT. A nomogram was developed and validated internally.

RESULTS

A total of 100 patients met the inclusion criteria, with 32 patients developing good response (tumor regression grade, TRG 0 + 1) to NCRT. Lower pre-treatment SII, NLR, and PLR levels were associated with a higher probability of good response to NCRT (P = 0.025, P < 0.001, P = 0.003, respectively), and a higher pre-treatment PNI level was associated with a higher probability of good response to NCRT (P = 0.005). Logistic regression analysis demonstrated that tumor size (< 3 cm, OR = 5.489, P = 0.025), pre-treatment NLR level (< 3.05, OR = 4.025, P = 0.028), pre-treatment PLR level (< 145.98, OR = 4.337, P = 0.038), and pre-treatment PNI level (≥ 41.32, OR = 3.477, P = 0.039) were independent predictors of good response to NCRT. A nomogram was developed with a C-index of 0.827.

CONCLUSION

Hematological inflammation-based indexes, in terms of pre-treatment NLR, PLR, and PNI levels, can help in predicting the treatment response to NCRT for rectal MACs.

High platelet-to-lymphocyte ratio predicts improved survival outcome for perioperative NSAID use in patients with rectal cancer

PURPOSE

Nonsteroidal anti-inflammatory drugs (NSAIDs) have been shown to block tumor-associated inflammation in rectal cancer. However, the perioperative use of NSAIDs remains controversial. This study was designed to investigate whether the perioperative use of NSAIDs influences outcomes and to provide a predictive marker to identify patients who would benefit from NSAIDs.

METHODS

We enrolled 515 patients with stage I to III rectal cancer in this retrospective study. Patients were classified into the NSAID and non-NSAID groups according to their perioperative use of NSAIDs. The whole cohort was stratified by platelet-to-lymphocyte ratio (PLR). The primary endpoints were disease-free survival (DFS) and overall survival (OS).

RESULTS

The NSAID group had a 12.6% lower risk of recurrence than the non-NSAID group (P = 0.015), while the association with survival was nonsignificant. In the high-PLR subset, the NSAID group had a 17.3% lower risk of recurrence (P = 0.003) and a better DFS (P = 0.033) outcome than the non-NSAID group. Multivariate analysis confirmed this independent significant association with DFS (P = 0.023). In the low-PLR subset, the association of NSAID use with survival was nonsignificant.

CONCLUSION

Perioperative use of NSAIDs was associated with improved survival outcomes in rectal cancer patients with high PLR.

Nomograms for Prediction of Molecular Phenotypes in Colorectal Cancer

Background

Colorectal cancer (CRC) patients with different molecular phenotypes, including microsatellite instability (MSI), CpG island methylator phenotype (CIMP), and somatic mutations in BRAF and KRAS gene, vary in treatment response and prognosis. However, molecular phenotyping under adequate quality control in a community-based setting may be difficult. We aimed to build the nomograms based on easily accessible clinicopathological characteristics to predict molecular phenotypes.

Methods

Three hundred and six patients with pathologically confirmed stage I-IV CRC were included in the cohort. The assays for MSI, CIMP, and mutations in BRAF and KRAS gene were performed using resected tumor samples. The candidate predictors were identified from clinicopathological variables using multivariate Logistic regression analyses to construct the nomograms that could predict each molecular phenotype.

Results

The incidences of MSI, CIMP, BRAF mutation and KRAS mutation were 25.3% (72/285), 2.5% (7/270), 3.4% (10/293), and 34.8% (96/276) respectively. In the multivariate Logistic analysis, poor differentiation and high neutrophil/lymphocyte ratio (NLR) were independently associated with MSI; poor differentiation, high NLR and high carcinoembryonic antigen/tumor size ratio (CSR) were independently associated with CIMP; poor differentiation, lymphovascular invasion and high CSR were independently associated with BRAF mutation; poor differentiation, proximal tumor, mucinous tumor and high NLR were independently associated with KRAS mutation. Four nomograms for MSI, CIMP, BRAF mutation and KRAS mutation were developed based on these independent predictors, the C-indexes of which were 61.22% (95% CI: 60.28–62.16%), 95.57% (95% CI: 95.20–95.94%), 83.56% (95% CI: 81.54–85.58%), and 69.12% (95% CI: 68.30–69.94%) respectively.

Conclusion

We established four nomograms using easily accessible variables that could well predict the presence of MSI, CIMP, BRAF mutation and KRAS mutation in CRC patients.

Neutrophil-to-Lymphocyte Ratio in Rectal Cancer-Novel Biomarker of Tumor Immunogenicity During Radiotherapy or Confounding Variable?

The aim of this study was to investigate the predictive value of blood-derived makers of local and systemic inflammatory responses on early and long-term oncological outcomes. A retrospective analysis of patients with locally advanced rectal cancer treated with preoperative long-course 5-fluorouracil-based radiochemotherapy was performed. Differential blood counts before neoadjuvant treatment were extracted from the patients' electronic charts. Optimal cut-off values for neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) were determined. Potential clinical and hematological prognostic factors for disease-free survival (DFS) were studied using uni- and multivariate analysis. A total of 220 patients were included in the analysis. Median follow-up was 67 months. Five-year DFS and overall survival (OS) were 70% and 85%, respectively. NLR with a cut-off value of 4.06 was identified as optimal to predict DFS events. In multivariate analysis, only tumor volume (HR 0.33, 95% CI (0.14-0.83), p = 0.017) and NLR (HR 0.3, 95% CI (0.11-0.81), p = 0.017) remained significant predictors of DFS. Patients with a good histological response (Dworak 3 and 4) to radiotherapy also had a lower NLR than patients with less pronounced tumor regression (3.0 vs. 4.2, p = 0.015). A strong correlation between primary tumor volume and NLR was seen (Pearson's r = 0.64, p < 0.001). Moreover, patients with T4 tumors had a significantly higher NLR than patients with T1-T3 tumors (6.6 vs. 3.3, p < 0.001). An elevated pretherapeutic NLR was associated with higher T stage, inferior DFS, and poor pathological response to neoadjuvant radiochemotherapy. A strong correlation between NLR and primary tumor volume was seen. This association is important for the interpretation of study results and for the design of translational studies which are warranted.