Development and validation of a prognostic model to predict the prognosis of patients who underwent chemotherapy and resection of pancreatic adenocarcinoma: a large international population-based cohort study

High somatic mutations in circulating tumor DNA predict response of metastatic pancreatic ductal adenocarcinoma to first-line nab-paclitaxel plus S-1: prospective study

AIMS

We previously showed that the nab-paclitaxel plus S-1 (NPS) regimen had promising effects against metastatic pancreatic ducal adenocarcinoma (mPDAC), whose efficacy however could not be precisely predicted by routine biomarkers. This prospective study aimed to investigate the values of mutations in circulating tumor DNA (ctDNA) and their dynamic changes in predicting response of mPDAC to NPS chemotherapy.

METHODS

Paired tumor tissue and blood samples were prospectively collected from patients with mPDAC receiving first-line NPS chemotherapy, and underwent next-generation sequencing with genomic profiling of 425 genes for ctDNA. High mutation allelic frequency (MAF) was defined as ≥ 30% and ≥ 5% in tumor tissue and blood, respectively. Kappa statistics were used to assess agreement between mutant genes in tumor and ctDNA. Associations of mutations in ctDNA and their dynamic changes with tumor response, overall survival (OS), and progression-free survival (PFS) were assessed using the Kaplan-Meier method, multivariable-adjusted Cox proportional hazards regression, and longitudinal data analysis.

RESULTS

147 blood samples and 43 paired tumor specimens from 43 patients with mPDAC were sequenced. The most common driver genes with high MAF were KRAS (tumor, 35%; ctDNA, 37%) and TP53 (tumor, 37%; ctDNA, 33%). Mutation rates of KRAS and TP53 in ctDNA were significantly higher in patients with liver metastasis, with baseline CA19-9 ≥ 2000 U/mL, and/or without an early CA19-9 response. κ values for the 5 most commonly mutated genes between tumor and ctDNA ranged from 0.48 to 0.76. MAFs of the genes mostly decreased sequentially during subsequent measurements, which significantly correlated with objective response, with an increase indicating cancer progression. High mutations of KRAS and ARID1A in both tumor and ctDNA, and of TP53, CDKN2A, and SMAD4 in ctDNA but not in tumor were significantly associated with shorter survival. When predicting 6-month OS, AUCs for the 5 most commonly mutated genes in ctDNA ranged from 0.59 to 0.84, larger than for genes in tumor (0.56 to 0.71) and for clinicopathologic characteristics (0.51 to 0.68). Repeated measurements of mutations in ctDNA significantly differentiated survival and tumor response. Among the 31 patients with ≥ 2 ctDNA tests, longitudinal analysis of changes in gene MAF showed that ctDNA progression was 60 and 58 days ahead of radiologic and CA19-9 progression for 48% and 42% of the patients, respectively.

CONCLUSIONS

High mutations of multiple driving genes in ctDNA and their dynamic changes could effectively predict response of mPDAC to NPS chemotherapy, with promising reliable predictive performance superior to routine clinicopathologic parameters. Inspiringly, longitudinal ctDNA tracking could predict disease progression about 2 months ahead of radiologic or CA19-9 evaluations, with the potential to precisely devise individualized therapeutic strategies for mPDAC.

Dual-Energy Computed Tomography Parameters Combined With Inflammatory Indicators Predict Cervical Lymph Node Metastasis in Papillary Thyroid Cancer

BACKGROUND AND OBJECTIVE

Cervical lymph node metastasis (CLNM) is considered a marker of papillar Fethicy thyroid cancer (PTC) progression and has a potential impact on the prognosis of PTC. The purpose of this study was to screen for predictors of CLNM in PTC and to construct a predictive model to guide the surgical approach in patients with PTC.

METHODS

This is a retrospective study. Preoperative dual-energy computed tomography images of 114 patients with pathologically confirmed PTC between July 2019 and April 2023 were retrospectively analyzed. The dual-energy computed tomography parameters [iodine concentration (IC), normalized iodine concentration (NIC), the slope of energy spectrum curve (λHU)] of the venous stage cancer foci were measured and calculated. The independent influencing factors for predicting CLNM were determined by univariate and multivariate logistic regression analysis, and the prediction models were constructed. The clinical benefits of the model were evaluated using decision curves, calibration curves, and receiver operating characteristic curves.

RESULTS

The statistical results show that NIC, derived neutrophil-to-lymphocyte ratio (dNLR), prognostic nutritional index (PNI), gender, and tumor diameter were independent predictors of CLNM in PTC. The AUC of the nomogram was .898 (95% CI: .829-.966), and the calibration curve and decision curve showed that the prediction model had good predictive effect and clinical benefit, respectively.

CONCLUSION

The nomogram constructed based on dual-energy CT parameters and inflammatory prognostic indicators has high clinical value in predicting CLNM in PTC patients.

EBF2 Links KMT2D-Mediated H3K4me1 to Suppress Pancreatic Cancer Progression via Upregulating KLLN

Mono-methylation of histone H3 on Lys 4 (H3K4me1), which is catalyzed by histone-lysine N-methyltransferase 2D (KMT2D), serves as an important epigenetic regulator in transcriptional control. In this study, the authors identify early B-cell factor 2 (EBF2) as a binding protein of H3K4me1. Combining analyses of RNA-seq and ChIP-seq data, the authors further identify killin (KLLN) as a transcriptional target of KMT2D and EBF2 in pancreatic ductal adenocarcinoma (PDAC) cells. KMT2D-dependent H3K4me1 and EBF2 are predominantly over-lapped proximal to the transcription start site (TSS) of KLLN gene. Comprehensive functional assays show that KMT2D and EBF2 cooperatively inhibit PDAC cells proliferation, migration, and invasion through upregulating KLLN. Such inhibition on PDAC progression is also achieved through increasing H3K4me1 level by GSK-LSD1, a selective inhibitor of lysine-specific demethylase 1 (LSD1). Taken together, these findings reveal a new mechanism underlying PDAC progression and provide potential therapeutic targets for PDAC treatment.

The role of carotid artery stenosis in predicting stroke after coronary artery bypass grafting in a Chinese cohort study

Current guidelines give priority to surgical treatment of carotid artery stenosis (CAS) before coronary artery bypass grafting (CABG), especially in symptomatic patients. Carotid artery stenting is an alternative treatment for narrowing of the carotid arteries. This study sought to demonstrate the role of severe CAS in predicting stroke after CABG and assess the efficacy of carotid artery stenting in preventing postoperative stroke in a Chinese cohort. From 2015 to 2021, 1799 consecutive patients undergoing isolated CABG surgery were retrospectively recruited in a Chinese cohort. The predictive value of severe CAS in postoperative stroke and carotid stenting in preventing postoperative stroke was statistically analyzed. The incidence of postoperative stroke was 1.67%. The incidence of CAS with stenosis ≥ 50% and ≥ 70% was 19.2% and 6.9%. After propensity matching, the incidence of stroke was 8.0% in the severe CAS group and 0% in the non-severe CAS group. We successfully established an optimal predictive nomogram for predicting severe CAS in patients undergoing CABG. Carotid artery stenting was found ineffective in preventing postoperative stroke. The present study provides the incidence of CAS and postoperative stroke in a Chinese cohort, identifies severe CAS as an independent risk factor for postoperative stroke after CABG, constructs a nomogram predicting the incidence of severe CAS, and evaluates the effectiveness of carotid artery stenting in preventing postoperative stroke after CABG.

Development of a nomogram based on body composition analysis of quantitative computed tomography combined with clinical prognostic factors to predict disease-free survival after surgery and adjuvant chemotherapy in patients with gastric cancer

Background

Patients with gastric cancer (GC) have a high recurrence rate after surgery. To predict disease-free survival (DFS), we investigated the value of body composition changes (BCCs) measured by quantitative computed tomography (QCT) in assessing the prognosis of patients with GC undergoing resection combined with adjuvant chemotherapy and to construct a nomogram model in combination with clinical prognostic factors (CPFs).

Methods

A retrospective study of 60 patients with GC between February 2015 and June 2019 was conducted. Pre- and posttreatment CT images of patients was used to measure bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA), and the rate of BCC was calculated. CPFs such as maximum tumor diameter (MTD), human epidermal growth factor receptor-2 (HER2), and Ki-67 were derived from postoperative pathological findings. Independent prognostic factors affecting DFS in GC were screened via univariate and multivariate Cox regression analysis. The Kaplan-Meier method and log-rank test were used to plot survival curves and compare the curves between groups, respectively. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves to evaluate the efficacy of the nomogram.

Results

The results of multivariate Cox regression analysis showed that ΔBMD [hazard ratio (HR): 4.577; 95% confidence interval (CI): 1.483-14.132; P=0.008], ΔPMA (HR: 5.784; 95% CI: 1.251-26.740; P=0.025), HER2 (HR: 4.819; 95% CI: 2.201-10.549; P<0.001), and maximal tumor diameter (HR: 3.973; 95% CI: 1.893-8.337; P<0.001) were independent factors influencing DFS. ΔBMD, ΔSFA, ΔVFA, ΔTFA, and ΔPMA were -3.86%, -23.44%, -19.57%, -22.45%, and -5.94%, respectively. The prognostic model of BCCs combined with CPFs had the highest predictive performance. Decision curve analysis (DCA) indicated good clinical benefit for the prognostic nomogram. The concordance index of the prognostic nomogram was 0.814, and the area under the curve (AUC) of predicting 2- and 3-year DFS were 0.879 and 0.928, respectively. The calibration curve showed that the nomogram-predicted DFS aligned well with the actual DFS.

Conclusions

The prognostic nomogram combining BCCs and CPFs was able to reliably predict the DFS of patients with GC.

Associations of circulating immunomarkers with the efficacy of immunotherapy for primary hepatic carcinoma

BACKGROUND

Peripheral blood immunomarkers are associated with prognosis in patients with solid tumors receiving chemotherapy or immunotherapy. In this study, the associations of circulating neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR), as well as their dynamic changes were investigated in relation to the efficacy of immunotherapy in patients with primary liver cancer.

METHODS

Comparisons were made between NLR, MLR, and PLR among individuals exhibiting disease control (defined as the best response of partial response [PR] or stable disease [SD]) and those with progressive disease (PD). Additionally, disease control rate (DCR), overall survival (OS), and progression-free survival (PFS) were compared between individuals with different NLR, MLR, and PLR levels before initiating palliative immunotherapy. Furthermore, comparisons were made between patients with different alterations in the ratios at the second cycle of immunotherapy compared to baseline. These analyses were performed using univariate and multivariate approaches. A total of 119 Chinese patients with liver cancer who underwent immunotherapy were included in this study, which focused on hepatocellular carcinoma (HCC).

RESULTS

In cases with HCC (n = 104), the cutoffs of NLR, MLR, and PLR to differentiate treatment responders from nonresponders were 3.38, 0.28, and 227.18, respectively. Patients with the best response of PR or SD had significantly lower NLR and MLR. Patients with NLR <3.38 and those with MLR <0.28 significantly had longer OS and PFS than their counterparts, and those with PLR <227.18 had significantly longer PFS, both in overall patients and in various patient subgroups. Lower NLR, MLR, or PLR was associated with earlier BCLC stage, fewer metastatic sites, less frequent extrahepatic metastasis, or better performance status. For individuals who had an unfavorable baseline NLR ≥3.38, MLR ≥0.28, or a favorable baseline PLR <227.18 prior to first immunotherapy, a decrease in NLR, MLR, or PLR at Cycle 2 of immunotherapy was significantly associated with a higher DCR.

CONCLUSIONS

Among patients with HCC who received immunotherapy, lower NLR, and MLR at baseline in overall patients were significantly associated with better disease control and more favorable survival outcomes (both OS and PFS), and lower PLR was significantly associated with longer PFS. The findings of this research may offer useful hints foranoptimized selection of patients with liver cancer who may benefit more from immunotherapy.

Acute toxicities of intravenous, intraperitoneal, or intratumoral injection of natural killer cells in human pancreatic adenocarcinoma-bearing mice: Randomized study

AIMS

To investigate the possible acute toxicities and pathological changes associated with intravenous, intraperitoneal, or intratumoral injection of natural killer (NK) cells in mice subcutaneously bearing human pancreatic adenocarcinoma (PaC).

METHODS

100 NPG tumor-bearing mice (50/sex) were engrafted subcutaneously with human PaC BXPC-3 cells 9 days before administration. They were randomly divided into 10 groups with 5 males and 5 females in each group. Mice in Group 1 were given sodium chloride intravenously as vehicle control, and mice in Groups 2-4 human peripheral blood-derived NK cells intravenously at doses of 2 × 107, 1 × 108, and 5 × 108 cells/kg, respectively; mice in Groups 5-7 were injected with NK cells intraperitoneally at doses of 2 × 107, 1 × 108, and 5 × 108 cells/kg, respectively, and mice in Groups 8-10 with NK cells intratumorally at doses of 4 × 103, 2 × 104, and 1 × 105 cells/mm3, respectively. Each group was given a single dose; the mice were observed clinically, and body weight, food intake, blood biochemistry, and tumor volume were measured. On Day 15, the mice were euthanized for gross anatomy and histopathology.

RESULTS

On planned euthanasia, in Groups 2-4 no gross or microscopic pathological changes related to cells injection were found; in Groups 5-7 mice of both sexes showed a decrease in extramedullary hematopoiesis of spleen, and at the dose of 5 × 108 cells/kg, mice of both sexes showed an increase in the composition of spleen white pulp cells. In Groups 8-10, mice of both sexes at doses of 4 × 103 and 1 × 105 cells/mm3 and female mice at the dose of 2 × 104 cells/mm3 showed a decrease in extramedullary hematopoiesis of spleen, and female mice at a dose of 4 × 103 cells/mm3 and mice of both sexes at doses of ≥ 2 × 104 cells/mm3 showed an increase in the composition of spleen white pulp cells; perivascular/peribronchiolar inflammatory cell infiltration in lung and bronchus was observed in mice of both sexes at doses of ≥ 2 × 104 cells/mm3, and inflammatory cell infiltration in liver was observed in mice of both sexes at a dose of 1 × 105 cells/mm3. No other abnormal changes with toxicological significance in clinical observation, body weight, food intake, or blood biochemistry were observed in each group.

CONCLUSIONS

In our study intravenous injection appears the safest way to give NK cells to human PaC-bearing mice. Using intraperitoneal or intratumoral administration, spleen, liver, and lung were the most often affected organs, albeit with mostly mild pathological changes.

Gene signature developed for predicting early relapse and survival in early-stage pancreatic cancer

BACKGROUND

The aim of this study was to construct a predictive signature integrating tumour-mutation- and copy-number-variation-associated features using machine learning to precisely predict early relapse and survival in patients with resected stage I-II pancreatic ductal adenocarcinoma.

METHODS

Patients with microscopically confirmed stage I-II pancreatic ductal adenocarcinoma undergoing R0 resection at the Chinese PLA General Hospital between March 2015 and December 2016 were enrolled. Whole exosome sequencing was performed, and genes with different mutation or copy number variation statuses between patients with and without relapse within 1 year were identified using bioinformatics analysis. A support vector machine was used to evaluate the importance of the differential gene features and to develop a signature. Signature validation was performed in an independent cohort. The associations of the support vector machine signature and single gene features with disease-free survival and overall survival were assessed. Biological functions of integrated genes were further analysed.

RESULTS

Overall, 30 and 40 patients were included in the training and validation cohorts, respectively. Some 11 genes with differential patterns were first identified; using a support vector machine, four features (mutations of DNAH9, TP53, and TUBGCP6, and copy number variation of TMEM132E) were further selected and integrated to construct a predictive signature (the support vector machine classifier). In the training cohort, the 1-year disease-free survival rates were 88 per cent (95 per cent c.i. 73 to 100) and 7 per cent (95 per cent c.i. 1 to 47) in the low-support vector machine subgroup and the high-support vector machine subgroup respectively (P < 0.001). Multivariable analyses showed that high support vector machine was significantly and independently associated with both worse overall survival (HR 29.20 (95 per cent c.i. 4.48 to 190.21); P < 0.001) and disease-free survival (HR 72.04 (95 per cent c.i. 6.74 to 769.96); P < 0.001). The area under the curve of the support vector machine signature for 1-year disease-free survival (0.900) was significantly larger than the area under the curve values of the mutations of DNAH9 (0.733; P = 0.039), TP53 (0.767; P = 0.024), and TUBGCP6 (0.733; P = 0.023), the copy number variation of TMEM132E (0.700; P = 0.014), TNM stage (0.567; P = 0.002), and differentiation grade (0.633; P = 0.005), suggesting higher predictive accuracy for prognosis. The value of the signature was further validated in the validation cohort. The four genes included in the support vector machine signature (DNAH9, TUBGCP6, and TMEM132E were novel in pancreatic ductal adenocarcinoma) were significantly associated with the tumour immune microenvironment, G protein-coupled receptor binding and signalling, cell-cell adhesion, etc.

CONCLUSION

The newly constructed support vector machine signature precisely and powerfully predicted relapse and survival in patients with stage I-II pancreatic ductal adenocarcinoma after R0 resection.

Proposal of nomograms to predict clinical outcomes in patients with ampulla of Vater cancer based on the Korea-Japan collaborative study

BACKGROUND

In this study, we aimed to develop and validate a nomogram to predict overall survival (OS) and recurrence-free survival (RFS) in patients who underwent curative resection of ampulla of Vater (AOV) cancer. This is the first study for nomograms in AOV cancer patients using retrospective data based on an international multicenter study.

METHODS

A total of 2007 patients with AOV adenocarcinoma who received operative therapy between 2002 January and 2015 December in Korea and Japan were retrospectively assessed to develop a prediction model. Nomograms for 5-year OS and 3-year RFS were constructed by dividing the patients who received and who did not receive adjuvant therapy after surgery, respectively. Significant risk factors were identified by univariate and multivariate Cox analyses. Performance assessment of the four prediction models was conducted by the Harrell's concordance index (C-index) and calibration curves using bootstrapping.

RESULTS

A total of 2007 and 1873 patients were collected for nomogram construction to predict 5-year OS and 3-year RFS. We developed four types of nomograms, including models for 5-year OS and 3-year RFS in patients who did not receive postoperative adjuvant therapy, and 5-year OS and 3-year RFS in patients who received postoperative adjuvant therapy. The C-indices of these nomograms were 0.795 (95% confidence interval [CI]: 0.766-0.823), 0.712 (95% CI: 0.674-0.750), 0.804 (95% CI: 0.7778-0.829), and 0.703 (95% CI: 0.669-0.737), respectively.

CONCLUSIONS

This predictive model could help clinicians to choose optimal treatment and precisely predict prognosis in AOV cancer patients.

Continuous care needs in patients with cancer receiving chemotherapy during the recent omicron wave of COVID-19 in Shanghai: A qualitative study

Aims

This study aimed to investigate the care needs, to clarify the factors affecting the quality of homecare, and to provide reference for constructing a homecare system for patients with cancer receiving chemotherapy during the recent omicron wave of COVID-19 in Shanghai.

Methods

From March to May 2022 when the omicron wave emerged in Shanghai, 50 consecutive patients who received chemotherapy at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, were enrolled, and underwent face-to-face or telephone-based semi-structured interviews regarding continuous care needs. Some of their homecare-givers, caring nurses, and physicians were also interviewed. The Colaizzi method was used for data analysis.

Results

Fifty patients, 4 homecare-givers, 4 nurses, and 4 physicians were interviewed. Three themes and six subthemes emerged from analysis of the interviews: The first theme was "Disease management needs," including needs for knowledge of managing adverse events associated with chemotherapy, and needs for treatment-related information. Patients expressed most concern about not being able to go to the hospital for blood review and disease evaluation in time due to the outbreak. With the COVID-19 pandemic being ongoing, factors such as pandemic panic, inconvenient medical treatment, and worry about hospital cross-infection might reduce disease management for patients with cancer. The second theme was "Medical needs," including needs for mobile healthcare and needs for medical resources. All interviewees emphasized the importance of mobile healthcare during the COVID-19 pandemic, as access to hospitals was difficult. The third theme was "Spiritual needs," including demands for psychological counseling and intervention, and needs for spiritual care. Patients and homecare-givers commonly lacked a feeling of security and needed communication, encouragement, and reassurance that medical care could be delivered to them, and patients reported that they very much wanted psychological advice.

Conclusion

For patients with cancer receiving chemotherapy during the COVID-19 pandemic, continuous care is greatly needed. Medical personnel should strengthen the healthcare education for patients and their caregivers during hospitalization, and further improve the patients' information intake rate through Internet-based digital healthcare methods during homecare, to further meet the information needs of patients after discharge from hospital.

Establishment of a prognostic prediction system based on tumor microenvironment of pancreatic cancer

BACKGROUND

Pancreatic cancer (PC) is an inflammatory tumor. Tumor microenvironment (TME) plays an important role in the development of PC. This study aims to explore hub genes of TME and establish a prognostic prediction system for PC.

METHODS

High throughput RNA-sequencing and clinical data of PC were downloaded from The Cancer Genome Atlas and International Cancer Genome Consortium database, respectively. PC patients were divided into high- and low-score group by using stromal, immune scores system based on ESTIMATE. Differentially expressed genes between high- and low-score patients were screened and survival-related differentially expressed genes were identified as candidate genes by univariate Cox regression analysis. Final variables for establishment of the prognostic prediction system were determined by LASSO analysis and multivariate Cox regression analysis. The predictive power of the prognostic system was evaluated by internal and external validation.

RESULTS

A total of 210 candidate genes were identified by stromal, immune scores system, and survival analyses. Finally, the prognostic risk score system was constructed by the following genes: FAM57B, HTRA3, CXCL10, GABRP, SPRR1B, FAM83A, and LY6D. In process of internal validation, Harrell concordance index (C-index) of this prognostic risk score system was 0.73, and the area under the receiver operating characteristic curve value of 1-year, 2-year, and 3-year overall survival period was 0.67, 0.76 and 0.86, respectively. In the external validation set, the survival prediction C-index was 0.71, and the area under the curve was 0.81, 0.72, and 0.78 at 1-year, 2-year, and 3-year, respectively.

CONCLUSION

This prognostic risk score system based on TME demonstrated a good predictive capacity to the prognosis of PC. It may provide information for the treatment strategy and follow-up for patients with PC.

Brain metastasis from gastric adenocarcinoma: A large comprehensive population-based cohort study on risk factors and prognosis

Aims

Although brain metastasis from gastric adenocarcinoma (GaC) is rare, it may significantly affect survival and quality of life. The aim of this large, comprehensive, population-based cohort investigation was to investigate factors that were associated with brain metastasis from GaC and to explore the prognostic factors and time-dependent cumulative mortalities among cases with GaC and brain involvement.

Methods

Population-based information on cases with GaC diagnosed from 2010 to 2016 was obtained from a large-scale database. Factors that were associated with brain metastasis were investigated utilizing multivariable logistic regression. Time-dependent tumor-specific mortalities of cases with GaC and brain involvement were then computed utilizing the cumulative incidence functions (CIFs), and mortalities were compared between subgroups utilizing Gray's test. Factors that were associated with death were further evaluated utilizing multivariable Fine-Gray subdistribution hazard regression.

Results

Together, 28,736 eligible cases were included, which comprised 231 (1%) cases with brain metastasis and 10,801 (38%) with metastasis to other sites, encompassing a follow-up of 39,168 person-years. Brain metastasis occurred more often among younger patients (within overall cancers), in cases with stomach cardia tumors, within cases with signet-ring cell carcinoma (within overall cancers), and within cases with positive lymph nodes (within overall tumors); it was less often detected among black people. Brain involvement was associated with more lung and bone metastases. The median survival time of cases having brain metastasis was only 3 months; the 6- and 12-month tumor-specific cumulative mortalities were 57% and 71%, respectively. Among cases with GaC and brain metastasis, those with gastric cardia cancers (when receiving radiotherapy), those undergoing resection, and those receiving chemotherapy had lower mortality risks, while younger patients (when receiving chemotherapy or radiotherapy) and people with positive lymph nodes (when receiving radiotherapy) had higher death hazards.

Conclusion

Among patients with GaC, brain metastasis was correlated with several clinical and pathological variables, including ethnicity, age, cancer histology, location, lymph node involvement, and metastases to other sites. Cases having brain metastasis had poor survival that was correlated with age, cancer location, lymph node metastasis, and management. These findings offer vital clues for individualized patient care and future mechanistic explorations.

Development and validation of a prognostic nomogram for patients with stage II colon mucinous adenocarcinoma

PURPOSE

Mucinous histology is generally considered as a risk factor of prognosis in stage II colon cancer, but there is no appropriate model for prognostic evaluation and treatment decision in patients with stage II colon mucinous adenocarcinoma (C-MAC) Thus, it is urgent to develop a comprehensive, individualized evaluation tool to reflect the heterogeneity of stage II C-MAC.

METHODS

Patients with stage II C-MAC who underwent surgical treatment in the Surveillance, Epidemiology, and End Results Program were enrolled and randomly divided into training cohort (70%) and internal validation cohort (30%). Prognostic predictors which were determined by univariate and multivariate analysis in the training cohort were included in the nomogram. The calibration curves, decision curve analysis, X-tile analysis, and Kaplan-Meier curve of the nomogram were validated in the internal validation cohort.

RESULTS

Three thousand seven hundred sixty-two patients of stage II C-MAC were enrolled. The age, pathological T (pT) stage, tumor number, serum carcinoembryonic antigen (CEA), and perineural invasion (PNI) were independent predictors of overall survival (OS), which were used to establish a nomogram. Calibration curves of the nomogram indicated good consistency between nomogram prediction and actual survival for 1-, 3- and 5-year OS. Besides, patients with stage II C-MAC could be divided into high-, middle-, and low-risk subgroups by the nomogram. Further subgroup analysis indicated that patients in the high-risk group could have a survival benefit from chemotherapy after surgical treatment.

CONCLUSIONS

We established the first nomogram to accurately predict the survival of stage II C-MAC patients who underwent surgical treatment. In addition, the nomogram identified low-, middle-, and high-risk subgroups of patients and found chemotherapy might improve survival in the high-risk subgroup of stage II C-MAC patients.

Development and validation of the Massachusetts General Hospital/Memorial Sloan Kettering nomogram to predict overall survival of resected patients with pancreatic ductal adenocarcinoma treated with neoadjuvant therapy

BACKGROUND

Prognostication in patients undergoing resection for pancreatic ductal adenocarcinoma following neoadjuvant therapy remains challenging. In this study, we aimed to develop and validate a nomogram for the prediction of overall survival of these patients.

METHODS

Patients who underwent neoadjuvant therapy followed by surgical resection at the Massachusetts General Hospital were analyzed (training cohort). Patients from Memorial Sloan Kettering were included as a validation cohort. A nomogram to predict overall survival was designed, trained, and subjected to internal (bootstrap) validation.

RESULTS

A total of 325 patients were identified from Massachusetts General Hospital. Multivariable Cox regression analysis demonstrated that age (hazard ratio 1.828, 95% confidence interval 1.251-2.246; P = .007), serum carbohydrate antigen 19-9 ≥ 37 U/mL (HR 1.602, 95% confidence interval 1.187-3.258; P = .015), tumor size (hazard ratio 2.278, 95% confidence interval 1.405-4.368; P = .003), nodal status (hazard ratio 1.309, 95% confidence interval 1.108-2.439; P = .032), and R1 tumor resection (hazard ratio 1.481, 95% confidence interval 1.049-2.091; P = .026) were independent factors associated with overall survival. A nomogram that incorporated these significant prognostic factors was established. The calibration plots demonstrated high concordance between predictive nomogram values and actual overall survival for 1-year, 3-year, and 5-year overall survival. The model demonstrated excellent discriminatory power in both the Massachusetts General Hospital and Memorial Sloan Kettering cohorts, with adjusted Harrel's concordance index values of 0.729 and 0.712, respectively.

CONCLUSION

In this report, we established and validated a novel nomogram for predicting the survival of patients who underwent neoadjuvant therapy followed by pancreatectomy. This model allows clinicians to better estimate the survival of these specific patients.

Characteristics and in-hospital outcomes of elderly patients with cancer in a top-ranked hospital in China, 2016-2020: Real-world study

BACKGROUND

Cancer is mostly a disease of aging, and older patients with cancer are generally frailer. This study aimed to describe the characteristics and in-hospital outcomes and explore factors associated with duration, cost, and mortality during first hospitalization, in older patients with cancer admitted to a top-ranked hospital in China.

METHODS

Data on patients with solid cancer ≥65 years consecutively hospitalized in 2016-2020 were retrieved from the electronic medical records of Ruijin Hospital in Shanghai, China. Baseline characteristics, duration, cost, and mortality during hospitalization were described. Factors associated with duration, cost, and mortality during first hospitalization were explored using multivariable-adjusted logistic regression.

RESULTS

20,650 eligible patients with male proportion of 59% and median age of 70 years were analyzed. 45% of the patients underwent resection in our hospital. Upon first admission, 49% of patients had hypertension, 19% diabetes, 22% weight loss, and 28% risks of malnutrition. The median duration and cost of first hospitalization were 9 days and 32,000 RMB, respectively. 118 (0.6%) and 228 (1.1%) deaths occurred during first and any hospitalization, respectively. For first hospitalization, longer duration and higher cost were positively associated with older ages, male gender, emergency admission, certain tumor locations and histology, histories of diabetes, cirrhosis, and anticoagulant intake, higher body mass index, weight loss, reduced food intake, risk of falling, and worse self-care ability; in-hospital mortality was positively associated with age ≥85 years, emergency admission, certain cancer types, histories of hypertension and psychotropic intake, reduced food intake, and worse self-care ability.

CONCLUSIONS

This study identified certain baseline patient and tumor characteristics, medical and medication histories, changes of weight and food intake, diet, and self-care ability which were independently associated with in-hospital outcomes among older patients with cancer admitted to our hospital and which should be paid special attention to. While the factors might not be easily modifiable, our study can help identify patients at higher risks of inferior in-hospital outcomes.

Prognostic models to predict survival in patients with pancreatic cancer: a systematic review

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has poor survival. Current treatments offer little likelihood of cure or long-term survival. This systematic review evaluates prognostic models predicting overall survival in patients diagnosed with PDAC.

METHODS

We conducted a comprehensive search of eight electronic databases from their date of inception through to December 2019. Studies that published models predicting survival in patients with PDAC were identified.

RESULTS

3297 studies were identified; 187 full-text articles were retrieved and 54 studies of 49 unique prognostic models were included. Of these, 28 (57.1%) were conducted in patients with advanced disease, 17 (34.7%) with resectable disease, and four (8.2%) in all patients. 34 (69.4%) models were validated, and 35 (71.4%) reported model discrimination, with only five models reporting values >0.70 in both derivation and validation cohorts. Many (n = 27) had a moderate to high risk of bias and most (n = 33) were developed using retrospective data. No variables were unanimously found to be predictive of survival when included in more than one study.

CONCLUSION

Most prognostic models were developed using retrospective data and performed poorly. Future research should validate instruments performing well locally in international cohorts and investigate other potential predictors of survival.

S-1 Maintenance Therapy After First-Line Treatment With Nab-Paclitaxel Plus S-1 for Advanced Pancreatic Adenocarcinoma: A Real-World Study

Background

In our previous phase II study, nab-paclitaxel plus S-1 (NPS) showed encouraging objective response rate (ORR) as first-line treatment for advanced pancreatic adenocarcinoma (APAC). This study aimed to evaluate the effectiveness and safety of S-1 maintenance after NPS in APAC and to explore factors predicting survival benefits when using S-1 maintenance.

Methods

Between 2014 and 2018 a total of 182 patients with APAC, who were primarily treated with NPS, were included. For patients without progression or with treatment discontinuation due to any reasons within 4 months during NPS treatment, S-1 monotherapy was administrable as maintenance therapy at the physicians’ discretion based on the patients’ preference and performance status. Efficacy and safety of S-1 maintenance were investigated.

Results

In 123 patients without progression within 4 months during NPS treatment, 74 received S-1 maintenance and had median progression-free survival of 9.6 months and median overall survival of 16.7 months. Multivariable analysis showed that in patients receiving S-1 maintenance after first-line NPS therapy, an Eastern Cooperative Oncology Group (ECOG) Performance Status score of 0, non-metastatic disease, and complete or partial response as best response to NPS chemotherapy were independently associated with better survival. The most common all-grade hematological and non-hematological adverse events were neutropenia (82.4%) and peripheral neurotoxicity (66.2%), respectively, and the most common ≥Grade 3 hematological and non-hematological adverse events were neutropenia (40.5%) and peripheral neurotoxicity (6.8%), respectively in patients who received S-1 maintenance.

Conclusions

Our real-world study showed that S-1 maintenance after tumor response or stable disease induced by first-line NPS treatment was effective and well-tolerated for some patients with APAC, which offers a promising alternative treatment strategy with encouraging survival for APAC.

Combining Intravoxel Incoherent Motion Diffusion Weighted Imaging and Texture Analysis for a Nomogram to Predict Early Treatment Response to Concurrent Chemoradiotherapy in Cervical Cancer Patients

This study aimed to predict early treatment response to concurrent chemoradiotherapy (CCRT) by combining intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) with texture analysis (TA) for cervical cancer patients and to develop a nomogram for estimating the risk of residual tumor. Ninty-three cervical cancer patients underwent conventional MRI and IVIM-DWI before CCRT. We conducted TA using T2WI. The patients were allocated to partial response (PR) and complete response (CR) groups on the basis of posttreatment MRI. Multivariate logistic regression analysis on IVIM-DWI parameters and texture features was employed to filter the independent predictors and construct the predictive nomogram. Its discrimination and calibration performances were estimated. Multivariate analysis on the IVIM-DWI parameters showed that D and f were independent predictors (OR = 4.029 and 0.889, resp.; p < 0.05). However, the multivariate analysis on the texture features indicated that GLCM-correlation, GLRLM-LRE, and GLSZM-ZE were independent predictors (OR = 43.789, 9.774, and 23.738, resp.;p < 0.05). The combination of IVIM-DWI parameters and texture features exhibited the highest predictive performance (AUC = 0.975). The nomogram to identify the patients with high-risk residual tumors exhibited an acceptable predictive performance and stability with a C-index of 0.953. Decision curve analysis demonstrated the clinical use of the nomogram. The results demonstrate that D, f, GLCM-correlation, GLRLM-LRE, and GLSZM-ZE were independent predictors for cervical cancer. The nomogram combining IVIM-DWI parameters and texture features makes it possible to identify cervical cancer patients at a high risk of residual tumor after CCRT.

Nomogram of Combining CT-Based Body Composition Analyses and Prognostic Inflammation Score: Prediction of Survival in Advanced Epithelial Ovarian Cancer Patients

PURPOSE

To investigate the value of body composition changes measured by quantitative computer tomography (QCT) in evaluating the prognosis of advanced epithelial ovarian cancer (AEOC) patients who underwent primary debulking surgery (PDS) and adjuvant platinum-based chemotherapy, and constructed a nomogram model for predicting survival in combination with prognostic inflammation score (PIS).

METHOD

Fifty-seven patients with AEOC between 2012 and 2016 were retrospectively enrolled. Pre- and post-treatment CT images were used to analyze the body composition biomarkers. The subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), cross-sectional area of paraspinal skeletal muscle area (PMA), skeletal muscle density (SMD), body mineral density (BMD) were measured from two sets of CT images.

RESULTS

In multivariate analyses, VFA gain, PMA loss, BMD loss, and PIS were independent risk factors of overall survival (OS) (HR = 3.7, 3.0, 2.8, 1.9, respectively, all p < 0.05). Receiver operating characteristic (ROC) curves showed that the prognostic model combining body composition changes (BCC) and PIS had the highest predictive performance (area under the curve = 0.890). The concordance index (C-index) of the prognostic nomogram was 0.779 (95% CI, 0.673-0.886). Decision curve analysis (DCA) demonstrated the prognostic nomogram had a great distinguishing performance.

CONCLUSION

CT-based body composition analyses and PIS were associated with poor OS for AEOC patients who underwent PDS and adjuvant platinum-based chemotherapy. The prognostic nomogram with a combination of BCC and PIS was dependable in predicting survival for AEOC patients during treatment.

Body Composition Changes in Hepatocellular Carcinoma: Prediction of Survival to Transcatheter Arterial Chemoembolization in Combination With Clinical Prognostic Factors

Treatment-related toxicities and decreased levels of patient performance during cancer therapy might contribute to body composition changes (BCC) and thereby impact outcomes. This study investigated the association between BCC during transcatheter arterial chemoembolization (TACE) and outcome in patients with hepatocellular carcinoma (HCC), and developed a nomogram for predicting survival in combination with clinical prognostic factors (CPF). Pretreatment and posttreatment computed tomography (CT) images of 75 patients with HCC who were treated between 2015 and 2018 were analyzed. The bone mineral density (BMD), cross-sectional area of paraspinal muscles (CSA_muscle), subcutaneous fat area (SFA), and visceral fat area (VFA) were measured from two sets of CT images. Count the changes in body composition during treatment and sort out the CPF of patients. Using cox regression models, CSA_muscle change, SFA change, VFA change, child-push class, and portal vein thrombosis were independent prognostic factors for overall survival (OS) (HR=5.932, 2.384, 3.140, 1.744, 1.794, respectively. P < 0.05). Receiver operating characteristic curves (ROCs) showed the prediction model combination of BCC and CPF exhibited the highest predictive performance (AUC=0.937). Independent prognostic factors were all contained into the prognostic nomogram, the concordance index (C-index) of prognostic nomogram was 0.787 (95% CI, 0.675−0.887). Decision curve analysis (DCA) demonstrated that the prognostic nomogram was clinically useful. Nomogram-based risk classification systems were also constructed to facilitate risk stratification in HCC for optimization of clinical management. In conclusion, we identified CSA_muscle change, SFA change, VFA change, Child-Pugh class, and portal vein thrombosis were independent prognostic factors for HCC. The prognostic nomogram with a combination of BCC and CPF that can be applied in the individualized prediction of survival in patients with HCC after TACE.

Development and Validation of a Simple-to-Use Nomogram to Predict Early Death in Metastatic Pancreatic Adenocarcinoma

Background

Pancreatic adenocarcinoma (PCa) is a highly aggressive malignancy with high risk of early death (survival time ≤3 months). The present study aimed to identify associated risk factors and develop a simple-to-use nomogram to predict early death in metastatic PCa patients.

Methods

Patients diagnosed with metastatic PCa between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were collected for model construction and internal validation. An independent data set was obtained from China for external validation. Independent risk variables contributed to early death were identified by logistic regression models, which were then used to construct a nomogram. Internal and external validation was performed to evaluate the nomogram using calibration curves and the receiver operating characteristic curves.

Results

A total of 19,464 patients in the SEER cohort and 67 patients in the Chinese cohort were included. Patients from the SEER database were randomly divided into the training cohort (n = 13,040) and internal validation cohort (n = 6,424). Patients in the Chinese cohort were selected for the external validation cohort. Overall, 10,484 patients experienced early death in the SEER cohort and 35 in the Chinese cohort. A reliable nomogram was constructed on the basis of 11 significant risk factors. Internal validation and external validation of the nomogram showed high accuracy in predicting early death. Decision curve analysis demonstrated that this predictive nomogram had excellent and potential clinical applicability.

Conclusion

The nomogram provided a simple-to-use tool to distinguish early death in patients with metastatic PCa, assisting clinicians in implementing individualized treatment regimens.

SOURCE-PANC: A Prediction Model for Patients With Metastatic Pancreatic Ductal Adenocarcinoma Based on Nationwide Population-Based Data

BACKGROUND

A prediction model for overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (PDAC) including patient and treatment characteristics is currently not available, but it could be valuable for supporting clinicians in patient communication about expectations and prognosis. We aimed to develop a prediction model for OS in metastatic PDAC, called SOURCE-PANC, based on nationwide population-based data.

MATERIALS AND METHODS

Data on patients diagnosed with synchronous metastatic PDAC in 2015 through 2018 were retrieved from the Netherlands Cancer Registry. A multivariate Cox regression model was created to predict OS for various treatment strategies. Available patient, tumor, and treatment characteristics were used to compose the model. Treatment strategies were categorized as systemic treatment (subdivided into FOLFIRINOX, gemcitabine/nab-paclitaxel, and gemcitabine monotherapy), biliary drainage, and best supportive care only. Validation was performed according to a temporal internal-external cross-validation scheme. The predictive quality was assessed with the C-index and calibration.

RESULTS

Data for 4,739 patients were included in the model. Sixteen predictors were included: age, sex, performance status, laboratory values (albumin, bilirubin, CA19-9, lactate dehydrogenase), clinical tumor and nodal stage, tumor sublocation, presence of distant lymph node metastases, liver or peritoneal metastases, number of metastatic sites, and treatment strategy. The model demonstrated a C-index of 0.72 in the internal-external cross-validation and showed good calibration, with the intercept and slope 95% confidence intervals including the ideal values of 0 and 1, respectively.

CONCLUSIONS

A population-based prediction model for OS was developed for patients with metastatic PDAC and showed good performance. The predictors that were included in the model comprised both baseline patient and tumor characteristics and type of treatment. SOURCE-PANC will be incorporated in an electronic decision support tool to support shared decision-making in clinical practice.

Combinatorial Treatment of Tinzaparin and Chemotherapy Can Induce a Significant Antitumor Effect in Pancreatic Cancer

Pancreatic Cancer (PC) is recognized as a highly thrombogenic tumor; thus, low-molecular-weight heparin (LMWH) such as tinzaparin is routinely used for PC patients. On the basis of combinatorial therapy approaches to treat highly malignant and refractory cancers such as PC, we hypothesized that tinzaparin can augment the effectiveness of traditional chemotherapeutic drugs and induce efficient antitumor activity. PANC-1 and MIAPaCa-2 were incubated alone or in combination with tinzaparin, nab-paclitaxel and gemcitabine. In vivo evaluation of these compounds was performed in a NOD/SCID mouse using a model injected with PANC-1. Tinzaparin enhances the anti-tumor effects of nab-paclitaxel and gemcitabine in mtKRAS PC cell lines via apoptosis in in vitro experiments. The triple combination power acts through the induction of apoptosis, reduction of the proliferative potential and angiogenesis; hence, contributing to a decrease in tumor volume observed in vivo. The triple regimen provided an extra 24.3% tumor reduction compared to the double combination (gemcitabine plus nab-paclitaxel). Combinatorial strategies can create novel therapeutic approaches for the treatment of patients with PC, achieving a better clinical outcome and prolonged survival. Further prospective randomized research is needed and the investigation of various concentrations of tinzaparin above 150 UI/Kg, would potentially provide a valuable synergistic effect to the conventional therapeutic compounds.

Development and External Validation of Survival Prediction Model for Pancreatic Cancer Using Two Nationwide Database: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP)

Background/Aims

Several prediction models for evaluating the prognosis of nonmetastatic resected pancreatic ductal adenocarcinoma (PDAC) have been developed, and their performances were reported to be superior to that of the 8th edition of the American Joint Committee on Cancer (AJCC) staging system. We developed a prediction model to evaluate the prognosis of resected PDAC and externally validated it with data from a nationwide Korean database.

Methods

Data from the Surveillance, Epidemiology and End Results (SEER) database were utilized for model development, and data from the Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP) database were used for external validation. Potential candidate variables for model development were age, sex, histologic differentiation, tumor location, adjuvant chemotherapy, and the AJCC 8th staging system T and N stages. For external validation, the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) were evaluated.

Results

Between 2004 and 2016, data from 9,624 patients were utilized for model development, and data from 3,282 patients were used for external validation. In the multivariate Cox proportional hazard model, age, sex, tumor location, T and N stages, histologic differentiation, and adjuvant chemotherapy were independent prognostic factors for resected PDAC. After an exhaustive search and 10-fold cross validation, the best model was finally developed, which included all prognostic variables. The C-index, 1-year, 2-year, 3-year, and 5-year time-dependent AUCs were 0.628, 0.650, 0.665, 0.675, and 0.686, respectively.

Conclusions

The survival prediction model for resected PDAC could provide quantitative survival probabilities with reliable performance. External validation studies with other nationwide databases are needed to evaluate the performance of this model.

Construction and Validation of Novel Nomograms for Predicting Prognosis of Pancreatic Ductal Adenocarcinoma After Surgery According to Different Primary Cancer Locations

Background/Aims

Pancreatic ductal adenocarcinoma (PDAC) can occur in different parts of the pancreas. This study aimed to identify clinicopathological characteristics independently correlated with the prognosis of PDAC of the pancreatic head/uncinate (PHC) or body-tail (PBTC), and to develop novel nomograms for predicting cancer-specific survival (CSS) according to different primary cancer locations.

Methods

1160 PDAC patients were retrospectively enrolled and assigned to training and test sets with each set divided into PHC and PBTC groups. Comparative analysis of clinicopathologic characteristics, survival analysis, and multivariate analysis were performed. Independent factors were identified and used for constructing nomograms. The performance of the nomograms was validated in the test set.

Results

Primary tumor location was an independent risk factor for prognosis of PDAC after surgery. Specially, gender, fasting blood glucose, and preoperative cancer antigen 19-9 were significantly associated with prognosis of PHC, whereas age, body mass index, and lymph nodes were significantly correlated with the prognosis of PBTC. A significant difference in prognosis was found between PHC and PBTC in stage Ia and stage III. Three nomograms were established for predicting the prognosis for PDAC, PHC, and PBTC. Notably, these nomograms were calibrated modestly (c-indexes of 0.690 for PDAC, 0.669 for PHC, and 0.704 for PBTC), presented better accuracy and reliability than the 8^th AJCC staging system, and achieved clinical validity.

Conclusions

PHC and PBTC share the differential clinical-pathological characteristics and survival. The nomograms show good performance for predicting prognosis in PHC and PBTC. Therefore, these nomograms hold potential as novel approaches for predicting survival of PHC and PBTC patients after surgery.

Prognostic Nomograms for Nonelderly Adults with Gastric Signet Ring Cell Carcinoma

Background

Nomograms were established to predict the survival for gastric signet ring cell carcinoma (GSRC) in young and middle-aged adults. Material and Methods. Eligible patients with GSRC from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and then divided into a training and a testing cohort in proportion. Independent prognostic factors were picked by univariate and multivariate Cox regression analysis to set up nomograms. The predictive effect and clinical value of nomograms were evaluated by the concordance index (C-index), calibration curves, and receiver operating characteristic curve (ROC).

Results

A total of 1686 GSRC patients were subsumed into this case for analysis, including a training (n = 1180) and a testing cohort (n = 506). Independent risk factors related to overall survival (OS) and cancer-specific survival (CSS) comprised of race, TNM stage, tumor size, number of positive lymph nodes (PLNE), and chemotherapy. For OS, the C-indexes of the training and testing cohorts were 0.737 and 0.752, while for CSS, C-indexes were, respectively, 0.749 and 0.751. These revealed that nomograms accurately predicted OS and CSS. Calibration curves and ROC demonstrated the apparent superiority of nomograms.

Conclusion

We built a well-understood and comprehensive prognostic assessment model for GSRC, which provided an individualized survival prediction in the form of a quantitative score that can be considered for clinical practice.

Similarity-driven multi-view embeddings from high-dimensional biomedical data

Diverse, high-dimensional modalities collected in large cohorts present new opportunities for the formulation and testing of integrative scientific hypotheses. Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces. SiMLR contributes an objective function for identifying joint signal, regularization based on sparse matrices representing prior within-modality relationships and an implementation that permits application to joint reduction of large data matrices. We demonstrate that SiMLR outperforms closely related methods on supervised learning problems in simulation data, a multi-omics cancer survival prediction dataset and multiple modality neuroimaging datasets. Taken together, this collection of results shows that SiMLR may be applied to joint signal estimation from disparate modalities and may yield practically useful results in a variety of application domains.

Nomogram for predicting overall survival in children with neuroblastoma based on SEER database

Purpose

This study was performed to establish and validate a nomogram for predicting the overall survival in children with neuroblastoma.

Methods

The latest clinical data of neuroblastoma in Surveillance, Epidemiology, and End Results (SEER) database was extracted from 2000 to 2016. The cases included were randomly divided into training and validation cohorts. The survival curves were drawn with a Kaplan-Meier estimator to investigate the influences of certain single factors on overall survival. Also, least absolute shrinkage and selection operator regression was applied to further select the prognostic variables for neuroblastoma. Additionally, receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the accuracy of the nomogram.

Results

In total, 1,262 patients were collected and 8 independent prognostic factors were achieved, including patients' age, sex, race, tumor grade, radiotherapy, chemotherapy, tumor site, and tumor size. Then we constructed a nomogram by using the data of the training cohort with 886 cases. Subsequently, the nomogram was validated internally and externally with 886 and 376 cases, respectively. The internal validation revealed that the area under the curves (AUC) of ROC curves of 1-, 3-, and 5-year overall survival were 0.69, 0.78, and 0.81, respectively. Accordingly, the external validation also showed that the AUC of 1-, 3-, and 5-year overall survival were all ≥0.69. Both methods of validation demonstrated that the predictive calibration curves were consistent with standard curves.

Conclusion

The nomogram possess the potential to be a new tool in predicting the survival rate of neuroblastoma patients.

Development and validation of a novel nomogram for predicting the prognosis of patients with resected pancreatic adenocarcinoma

The survival prediction for patients with resected pancreatic adenocarcinoma by using the Tumor-Node-Metastasis (TNM) staging system remains limited. A nomogram is a efficient tool that can be used to predict the outcome of patients with various types of malignancy. The present study aimed to develop and validate a nomogram for patients with resected pancreatic adenocarcinoma. A total of 368 patients (258 in the training set and 110 in the validation set) who underwent pancreatic adenocarcinoma resection at the China National Cancer Center between January 2008 and October 2018 were included in the present study. The nomogram was established according to the results from Cox multivariate analysis, which was validated by discrimination and calibration. The area under the receiver operating characteristic curve (AUC) was determined to assess the accuracy of survival predictions. The results from multivariate analysis in the training set demonstrated that blood transfusion, T-stage, N-stage, tumor grade, capsule invasion, carbohydrate antigen 199, neutrophil percentage and adjuvant therapy were independent prognostic factors for overall survival (OS; all P<0.05). Subsequently, a nomogram predicting the 1-year, 3-year and 5-year OS rates, with favorable calibration, was established based on the independent prognostic factors. The concordance indices of the nomogram were higher compared with the TNM staging system in both training and validation sets. Furthermore, a clear risk stratification system based on the nomogram was used to classify patients into the three following groups: Low-risk group (≤168), moderate-risk group (168–255) and high-risk group (>255). The risk stratification system demonstrated an improved ability in predicting the 1-year, 3-year and 5-year OS rates compared with the TNM system (AUC, 0.758, 0.709 and 0.672 vs. AUC, 0.614, 0.604 and 0.568; all P<0.05). The present study developed and validated a nomogram for patients with resected pancreatic adenocarcinoma by including additional independent prognostic factors, including tumor marker, immune index, surgical information, pathological data and adjuvant therapy. Taken together, the results from the present study indicated an improved performance of the nomogram in predicting the prognosis of patients with resected pancreatic adenocarcinoma compared with the TNM staging system.

Development and validation of novel nomograms for predicting the survival of patients after surgical resection of pancreatic ductal adenocarcinoma

Background/Aims

Pancreatic ductal adenocarcinoma (PDAC) is associated with high mortality, even after surgical resection. The existing predictive models for survival have limitations. This study aimed to develop better nomograms for predicting overall survival (OS) and cancer‐specific survival (CSS) in PDAC patients after surgery.

Methods

A total of 6323 PDAC patients were retrospectively recruited from the Surveillance, Epidemiology, and End Results (SEER) database and randomly allocated into training, validation, and test cohorts. Multivariate Cox regression analysis was conducted to identify significant independent factors for OS and CSS, which were used for construction of nomograms. The performance was evaluated, validated, and compared with that of the 8th edition AJCC staging system.

Results

Ten independent factors were significantly correlated with OS and CSS. The 1‐, 3‐, and 5‐year OS rates were 40%, 20%, and 15%, and 1‐, 3‐, and 5‐year CSS rates were 45%, 24%, and 19%, respectively. The nomograms were calibrated well, with c‐indexes of 0.640 for OS and 0.643 for CSS, respectively. Notably, relative to the 8th edition AJCC staging system, the nomograms were able to stratify each AJCC stage into three prognostic subgroups for more robust risk stratification. Furthermore, the nomograms achieved significant clinical validity, exhibiting wide threshold probabilities and high net benefit. Performance assessment also showed high predictive accuracy and reliability.

Conclusions

The predictive ability and reliability of the established nomograms have been validated, and therefore, these nomograms hold potential as novel approaches to predicting survival and assessing survival risks for PDAC patients after surgery.

Conditional Survival After Resection for Pancreatic Cancer: A Population-Based Study and Prediction Model

Background

Conditional survival is the survival probability after already surviving a predefined time period. This may be informative during follow-up, especially when adjusted for tumor characteristics. Such prediction models for patients with resected pancreatic cancer are lacking and therefore conditional survival was assessed and a nomogram predicting 5-year survival at a predefined period after resection of pancreatic cancer was developed.

Methods

This population-based study included patients with resected pancreatic ductal adenocarcinoma from the Netherlands Cancer Registry (2005–2016). Conditional survival was calculated as the median, and the probability of surviving up to 8 years in patients who already survived 0–5 years after resection was calculated using the Kaplan–Meier method. A prediction model was constructed.

Results

Overall, 3082 patients were included, with a median age of 67 years. Median overall survival was 18 months (95% confidence interval 17–18 months), with a 5-year survival of 15%. The 1-year conditional survival (i.e. probability of surviving the next year) increased from 55 to 74 to 86% at 1, 3, and 5 years after surgery, respectively, while the median overall survival increased from 15 to 40 to 64 months at 1, 3, and 5 years after surgery, respectively. The prediction model demonstrated that the probability of achieving 5-year survival at 1 year after surgery varied from 1 to 58% depending on patient and tumor characteristics.

Conclusions

This population-based study showed that 1-year conditional survival was 55% 1 year after resection and 74% 3 years after resection in patients with pancreatic cancer. The prediction model is available via www.pancreascalculator.com to inform patients and caregivers.

Electronic supplementary material

The online version of this article (10.1245/s10434-020-08235-w) contains supplementary material, which is available to authorized users.

Bladder cancer survival nomogram: Development and validation of a prediction tool, using the SEER and TCGA databases

Bladder cancer (BC) is a common malignancy associated with high morbidity and mortality, however, accurate and convenient risk assessment tools applicable to BC patients are currently lacking. Previous studies using nomograms to evaluate bladder cancer (BC) survival have been based on small samples. Using a large dataset, this study aimed to construct more precise clinical nomograms to effectively predict bladder cancer survival.Data on patients with pathologically-confirmed bladder cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Additional BC patient data for an external validation cohort were extracted from the Cancer Genome Atlas (TCGA) database. Clinical parameters that constituted potential risk factors were reviewed and analyzed using univariate and multivariate Cox proportional hazards regression. A nomogram was constructed with parameters that significantly correlated with the overall survival (OS). Prognostic performance of a nomogram was assessed using the concordance index (c-index), area under the receiver operating characteristic curve (AUC), and a calibration curve. The model was then tested with data from an internal and external validation cohort. Patients' survival was analyzed and compared with the Kaplan-Meier (KM) method.Multivariate Cox regression showed that age, sex, race, stage_T1, stage_T2a, stage_T2b, stage_T3a, stage_Ta, stage_Tis, stage_N, stage_M were independent predictors of BC survival. A nomogram was constructed based on these factors. The c-index of the nomogram was 0.7916 (95% confidence interval CI, 0.79-0.80). The calibration curve showed excellent agreement between the predicted and observed values. The c-index for the internal validation cohort was 0.7917 (95% CI 0.79-0.80), which was higher than for the training cohort, suggesting robustness of the model. For the training cohort, the AUC for the 3- and the 5-year survival was 0.82 and 0.813, respectively. The c-index for the TNM-based model was superior to that for the AJCC-TNM classification.The models presented in this study might be suitable for clinical use, supporting clinicians in their individualized assessment of expected survival in BC patients. They might also be used as a layered tool for clinical research.

Development and validation of a prognostic nomogram for HIV/AIDS patients who underwent antiretroviral therapy: Data from a China population-based cohort

BACKGROUND

Accurate forecast of the death risk is crucial to the administration of people living with HIV/AIDS (PLHIV). We aimed to establish and validate an effective prognosis nomogram in PLHIV receiving antiretroviral therapy (ART).

METHODS

All the data were obtained from 2006 to 2018 in the Wenzhou area from China AIDS prevention and control information system. Factors included in the nomogram were determined by univariate and multiple Cox proportional hazard analysis based on the training set. The receiver operating characteristic (ROC) and calibration curves were used to assess its predictive accuracy and discriminative ability. Its clinical utility was also evaluated using decision curve analysis (DCA), X-tile analysis and Kaplan-Meier curve, respectively in an independent validation set.

FINDINGS

Independent prognostic factors including haemoglobin, viral load and CD4+ T-cell count were determined and contained in the nomogram. Good agreement between the prediction by nomogram and actual observation could be detected in the calibration curve for mortality, especially in the first year. In the training cohort, AUC (95% CI) and C-index (95% CI) were 0.93 (0.90, 0.96) and 0.90 (0.85, 0.96), respectively. In the validation set, the nomogram still revealed excellent discriminations [AUC (95% CI): 0.95 (0.91, 1.00)] and good calibration [C-index (95% CI): 0.92 (0.82-1.00)]. Moreover, DCA also demonstrated that the nomogram was clinical beneficial. Additionally, participants could be classified into three distinct (low, middle and high) risk groups by the nomogram.

INTERPRETATION

The nomogram presents accurate and favourable prognostic prediction for PLHIV who underwent ART.

FUNDING

This work was supported by Zhejiang Basic Public Welfare Research Project (LGF19H260011), Wenzhou Basic Public Welfare Research Project (Y20180201), the Initial Scientific Research Fund (KYQD170301), the Major Project of the Eye Hospital Wenzhou the Major Project of the Eye Hospital Wenzhou Medical University (YNZD201602). Part of this work was also funded by National Natural Science Foundation of China (81670777) and Science and Technology Innovation Activity Plan and New Talents Plan for College Students in Zhejiang Province (2019R413073). The funders had no roles in study design, data collection, data analysis, interpretation and writing of the report.

A PD-L2-based immune marker signature helps to predict survival in resected pancreatic ductal adenocarcinoma

BACKGROUND

Programmed cell death protein 1 (PD-1) is a key immune checkpoint that regulates peripheral tolerance and protects against autoimmunity. Programmed death ligand-2 (PD-L2) is a less studied ligand to PD-1 and has yet to be fully explored, especially in pancreatic ductal adenocarcinoma (PDAC).

METHODS

In this study, we performed immunohistochemistry to detect the PD-L2, CD3, CD8, transforming growth factor-β2 (TGF-β2) and FOXP3 levels in paraffin sections from 305 patients with resected PDAC as a training set. Expression levels of intratumoral and stromal immune markers were compared in relation to survival using Kaplan-Meier curves, random survival forest model and survival tree analysis. A multivariable Cox proportional-hazards model of associated markers was used to calculate the risk scores.

RESULTS

PD-L2 was expressed in 71.5% of PDAC samples and showed strong correlations with CD3+, CD8+ T cells and FOXP3+ regulatory T cell densities. High levels of intratumoral PD-L2 and FOXP3 were related to poor survival; only stromal FOXP3 overexpression was associated with worse prognosis. Four patterns generated from survival tree analysis demonstrated that PD-L2lowstromalFOXP3low patients had the longest survival, while PD-L2highintratumoralCD3low patients had the shortest survival (P <  0.001). The area under the curve was 0.631(95% confidence interval (CI): 0.447-0.826) for the immune marker-based signature and 0.549 (95% CI: 0.323-0.829; P <  0.001) for the clinical parameter-based signature, which was consistent with the results in the validation set including 150 patients (P <  0.001). A higher risk score indicated shorter survival and could serve as an independent prognostic factor. PD-L2 was also showed associated with TGF-β2 and other immune molecules based on bioinformatics analysis.

CONCLUSIONS

Our work highlighted PD-L2 as a promising immunotherapeutic target with prognostic value combined with complex tumor infiltrating cells in PDAC.

Modulated Electro-Hyperthermia as Palliative Treatment for Pancreatic Cancer: A Retrospective Observational Study on 106 Patients

Background: Pancreatic adenocarcinoma has a poor prognosis, resulting in a <10% survival rate at 5 years. Modulated electro-hyperthermia (mEHT) has been increasingly used for pancreatic cancer palliative care and therapy. Objective: To monitor the efficacy and safety of mEHT for the treatment of advanced pancreatic cancer. Methods: We collected data retrospectively on 106 patients affected by stage III-IV pancreatic adenocarcinoma. They were divided into 2 groups: patients who did not receive mEHT (no-mEHT) and patients who were treated with mEHT. We performed mEHT applying a power of 60 to 150 W for 40 to 90 minutes. The mEHT treatment was associated with chemotherapy and/or radiotherapy for 33 (84.6%) patients, whereas 6 (15.4%) patients received mEHT alone. The patients of the no-mEHT group received chemotherapy and/or radiotherapy in 55.2% of cases. Results: Median age of the sample was 65.3 years (range = 31-80 years). After 3 months of therapy, the mEHT group had partial response in 22/34 patients (64.7%), stable disease in 10/34 patients (29.4%), and progressive disease in 2/34 patients (8.3%). The no-mEHT group had partial response in 3/36 patients (8.3%), stable disease in 10/36 patients (27.8%), and progressive disease in 23/36 patients (34.3%). The median overall survival of the mEHT group was 18.0 months (range = 1.5-68.0 months) and 10.9 months (range = 0.4-55.4 months) for the non-mEHT group. Conclusions: mEHT may improve tumor response and survival of pancreatic cancer patients.