Predictor Of Disease-free Survival Research Articles

Comparative analysis of metabolic characteristics and prognostic stratification of HER2-low and HER2-zero breast cancer using 18F-FDG PET/CT imaging

BackgroundRecent advancements in novel anti-human epidermal growth factor receptor 2 (HER2) antibody-drug conjugates (ADCs) have highlighted the emerging HER2-low breast cancer subtype with promising therapeutic efficacy. This study aimed to comparatively analyze the metabolic characteristics and prognostic stratification of HER2-low and HER2-zero breast cancer using baseline fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging.MethodsConsecutive patients with newly diagnosed breast cancer who underwent 18F-FDG PET/CT prior to therapy in our hospital were retrospectively reviewed. The relationship between metabolic parameters (maximum standardized uptake value (SUVmax), tumor-to-liver SUV ratio (TLR), total lesion glycolysis (TLG), and metabolic tumor volume (MTV)) in primary lesions and HER2 expression was analyzed. The survival analyses were performed to identify the prognostic factors for disease-free survival (DFS) in patients with HER2-negative (HER2-low versus -zero).ResultsIn total, 258 patients (mean age: 54 ± 12 years) were included. In hormone receptor (HR)-positive subgroup, SUVmax and TLR were significantly higher in HER2-low than in HER2-zero (P = 0.045 and 0.03, respectively). But in HR-negative subgroup, there was no significant metabolic difference between HER2-low and HER2-zero (All P > 0.05). The four metabolic parameters were significant predictors of DFS in HER2-negative patients (All P < 0.01), but there was no significant difference in DFS between HER2-low and -zero, regardless of tumor metabolism. Moreover, in HER2-zero patients, the DFS of patients with high metabolism was significantly shorter than that of patients with low metabolism (PSUVmax = 0.002, PMTV = 0.03, PTLG= 0.005, PTLR < 0.001, respectively), but without a similar finding in HER2-low patients.ConclusionOur study demonstrated the HR-positive HER2-low breast cancer exhibited a particularity in glucose metabolic profile. Additionally, HER2-zero patients with elevated metabolism were associated with inferior prognosis and warranted careful attention in clinical evaluations.

Automatic segmentation-based multi-modal radiomics analysis of US and MRI for predicting disease-free survival of breast cancer: a multicenter study

BackgroundSeveral studies have confirmed the potential value of applying radiomics to predict prognosis of breast cancer. However, the tumor segmentation in these studies depended on delineation or annotation of breast cancer by radiologist, which is often laborious, tedious, and vulnerable to inter- and intra-observer variability. Automatic segmentation is expected to overcome this difficulty. Herein, we aim to investigate the value of automatic segmentation-based multi-modal radiomics signature and magnetic resonance imaging (MRI) features in predicting disease-free survival (DFS) of patients diagnosed with invasive breast cancer.MethodsThis retrospective multicenter study included a total of 643 female patients with invasive breast cancer who underwent preoperative ultrasound (US) and MRI for prognostic analysis. Data (n = 480) from center 1 were divided into training and internal testing sets, while data (n = 163) from centers 2 and 3 were analyzed as the external testing set. We developed automatic segmentation frameworks for tumor segmentation by deep learning. Then, Least absolute shrinkage and selection operator Cox regression was used to select features to construct radiomics signature, and corresponding radiomics score (Rad-score) was calculated. Finally, six models for predicting DFS were constructed by using Cox regression and assessed in terms of discrimination, calibration, and clinical usefulness.ResultsThe multi-modal radiomics signature combining intra- and peri-tumoral radiomics signatures of US and MRI achieved a higher C-index in the internal (0.734) and external (0.708) testing sets than most other radiomics signatures in predicting DFS, and successfully stratified patients into low- and high-risk groups. The multi-modal clinical imaging model combining the multi-modal Rad-score and clinical traditional MRI model-score resulted in a higher C-index (0.795) than other models in the external testing set, and it had a better calibration and higher clinical benefit.ConclusionsThis study demonstrates that the multi-modal radiomics signature derived from automatic segmentations of US and MRI is a promising risk stratification biomarker for breast cancer, and highlights that the appropriate combination of multi-modal radiomics signature, clinical characteristics, and MRI feature can improve performance of individualized DFS prediction, which might assist in guiding decision-making related to breast cancer.

Osteosarcopenia in patients with cancer: A systematic review and meta-analysis.

Osteosarcopenia is frequent, and the relative risk of fracture is higher among patients with sarcopenia. It is a strong predictor of poor outcomes in older adults undergoing cancer treatment, suggesting that osteosarcopenia is important in an aging society. This study aimed to evaluate the overall survival (OS) and disease-free survival (DFS) of patients with cancer with and without osteosarcopenia. Five electronic databases-Embase, PubMed, Web of Science, Scopus, and CINAHL-were searched for relevant articles published before February 2024. Studies that met the criteria were used to evaluate the OS and DFS of patients with cancer with and without osteosarcopenia. From the 603 initially identified articles, 8 involving 1608 participants were included in the meta-analysis. We observed that patients with cancer diagnosed with osteopenia, sarcopenia, or osteosarcopenia had worse DFS than those without these conditions. Specifically, osteopenia (pooled hazard ratio [HR] = 1.70, P = .01) and osteosarcopenia (pooled HR = 2.17, P = .0001) emerged as independent predictors of DFS. However, sarcopenia was significantly associated with DFS. The quality of the included studies was generally good, and no publication bias was detected among them for either OS or DFS. These meta-analysis results suggest that osteopenia and osteosarcopenia are associated with worse DFS among patients with cancer. The use of different case definitions appeared to be a major source of heterogeneity among studies. Further studies are warranted to confirm our findings, especially those regarding OS and DFS.

Three-dimensional deep learning model complements existing models for preoperative disease-free survival prediction in localized clear cell renal cell carcinoma: a multicenter retrospective cohort study.

Current prognostic models have limited predictive abilities for the growing number of localized (stage I-III) ccRCCs. It is, therefore, crucial to explore novel preoperative recurrence prediction models to accurately stratify patients and optimize clinical decisions. The purpose of this study was to develop and externally validate a computed tomography (CT)-based deep learning (DL) model for presurgical disease-free survival (DFS) prediction. Patients with localized ccRCC were retrospectively enrolled from six independent medical centers. Three-dimensional (3D) tumor regions from CT images were utilized as input to architect a ResNet 50 model, which outputted DL computed risk score (DLCR) of each patient for DFS prediction later. The predictive performance of DLCR was assessed and compared to the radiomics model (Rad-Score), the clinical model the authors built and two existing prognostic models (UISS and Leibovich). The complementary value of DLCR to the UISS, Leibovich, as well as Rad-Score were evaluated by stratified analysis. Seven hundred seven patients with localized ccRCC were finally enrolled for models' training and validating. The DLCR the authors established can perfectly stratify patients into low-risks, intermediate-risks, and high-risks, and outperformed the Rad-Score, clinical model, UISS and Leibovich score in DFS prediction, with a C-index of 0.754 (0.689-0.821) in the external testing set. Furthermore, the DLCR presented excellent risk stratification capacity in subgroups defined by almost all clinic-pathological features. Moreover, patients classified as low-risk by the UISS/Leibovich score/Rad-Score but as intermediate - or high-risk by DLCR were significantly more likely to experience ccRCC recurrence than those stratified as intermediate- or high-risk by UISS/Leibovich score/Rad-Score but as low-risk by DLCR (all Log-rank P- values<0.05). Our DL model, derived from preoperative CT, is superior to radiomics and current models in precisely DFS predicting of localized ccRCC, and can provide complementary values to them, which may assist more informed clinical decisions and adjuvant therapies adoptions.

Overall survival and disease-free survival prediction in Chinese women with breast cancer aged 70 years or older by using nomograms.

By analyzing the existing data of this study, a prediction tool for the overall breast cancer survival and disease-free survival (DFS) of elderly women was established. Clinicopathologic data were collected from elderly women with BC who were admitted to the Tianjin Medical University Cancer Institute and Hospital from August 2014 to December 2017. Independent prognostic factors for BC in elderly patients were confirmed using the Cox proportional hazards model. Nomograms were developed with these factors for predicting the 3- and 5-year overall survival (OS) as well as DFS. The nomograms' discrimination ability and calibration were assessed through the area under the curve (AUC), concordance index (C-index), decision curve analysis (DCA), and calibration plots. We enroled 889 elderly patients with BC, and the results showed that the 3-year OS rate was 93.4% (95%CI = 91.8%-95.1%), the 3-year DFS rate was 87.8% (95%CI = 85.7%-90.0%), the 5-year OS rate was 85.6% (95%CI = 83.3%-87.9%), and the 5-year DFS rate was 80.1%(95%CI = 77.5%-82.8%). The corrected C-indices of the OS and DFS nomograms were 0.799 and 0.667, respectively (95%CI = 0.767-0.830 and 0.632-0.702, respectively). Relatively high AUC values were shown by the nomograms for estimating OS and DFS. The DCA revealed that the constructed nomograms had net benefits for clinical application. The calibration curves demonstrated an excellent correspondence between the data predicted by the nomograms and the actual survival data. Survival curves indicated that risk stratification could differentiate OS and DFS. This study developed novel and practical nomograms for individual prediction of DFS and OS in elderly BC patients. These nomograms can predict 3- and 5-year OS as well as DFS in the elderly BC patient population, thereby enabling personalized risk assessment and risk-based therapy.

Sinonasal adenoid cystic carcinoma: preoperative apparent diffusion coefficient histogram analysis in prediction of prognosis and Ki-67 proliferation status.

To investigate the value of preoperative apparent diffusion coefficient (ADC) histogram analysis in predicting the prognosis of patients with sinonasal adenoid cystic carcinoma (ACC) and the correlation between ADC histogram parameters and Ki-67 labeling index (LI). The study enrolled 66 patients with sinonasal ACC who were surgically resected and confirmed by histopathology. The disease-free survival (DFS) was evaluated with clinical-pathologic and radiologic characteristics using the Cox proportion hazard model. Spearman correlation analysis was used to evaluate the correlation between ADC histogram parameters and Ki-67 LI. The predictive performance of ADC histogram parameters for Ki-67 LI was assessed using the receiver operating characteristic (ROC) curve. Multivariable analysis showed Ki-67 LI (hazard ratio: 9.279; 95% confidence interval 1.099-78.338; P = 0.041) and ADCskewness (hazard ratio: 5.942; 95% confidence interval 1.832-19.268; P = 0.003) were significant independent predictors of DFS. The combination of these two variables achieved the predictive ability with a C-index of 0.717 (95% confidence interval 0.607-0.826). ADCmean and all ADC percentiles (10th, 50th, and 90th) significantly and inversely correlated with Ki-67 LI of ACC (Correlation coefficients = -0.574 to -0.591, Ps < 0.001). Among the ADC histogram parameters, the ADC50th showed superior performance for the differentiation of the high from low Ki-67 LI groups with an area under the curve (AUC) of 0.834 and an accuracy of 80.30%. ADC histogram analysis had predictive value for DFS and Ki-67 LI, which may be a valuable biomarker for prognosis and proliferation status for ACC in clinical practice.

A predictive model based on radiomics, clinical features, and pathologic indicators for disease-free survival after liver transplantation for hepatocellular carcinoma: a 7-year retrospective study.

Disease-free survival (DFS) is an essential indicator for evaluating the prognosis of liver transplantation (LT) in hepatocellular carcinoma (HCC) patients. Despite progress in the prediction of DFS by radiomics, only preoperative clinical features have been combined in most studies. The aim of this study was to construct a nomogram model (NM) using preoperative clinical features, radiomics, and postoperative pathological indicators for more effective prediction of DFS. This was a retrospective study of a single-center cohort comprising 139 HCC patients. Using the whole cohort, we constructed and assessed a clinical model (CM) based on alpha-fetoprotein (AFP) and alkaline phosphatase (ALP), a pathological model (PM) based on Ki-67 and tumor number, a radiomics model (RM) based on the radiomics score (Rad-score), and an NM based on the above five independent predictors. Significant correlations between the NM and DFS were observed in the training and validation cohorts. Among the four prediction models, the C-index of the NM was the highest [(training/validation cohort) CM: 0.664/0.676, PM: 0.737/0.691, RM: 0.706/0.697, NM: 0.817/0.760], and the areas under the receiver operating characteristic curves (AUCs) of the NM prediction of 1-year, 2-year, and 3-year DFS were also the highest [(training/validation cohort) 1-year, 2-year, and 3-year CM: 0.726/0.726, 0.685/0.744, 0.645/0.686, PM: 0.789/0.780, 0.801/0.748, 0.841/0.735, RM: 0.769/0.752, 0.717/0.805, 0.748/0.765, NM: 0.882/0.854, 0.867/0.849, 0.882/0.801]. The NM also exhibited the highest net clinical benefit. Based on radiomics, clinical features, and pathological indicators, the NM could be used to effectively predict DFS after LT in HCC patients, guiding the follow-up and complementary treatment.

Evaluation of prognostic risk factors of triple-negative breast cancer with 18F-FDG PET/CT parameters, clinical pathological features and biochemical indicators.

Breast cancer is a heterogeneous disease comprising various molecular subtypes, including Luminal A, Luminal B, human epidermal growth factor receptor-2 (HER2) positive, and triple negative types, each with distinct biological characteristics and behaviors. Triple negative breast cancer (TNBC) remains a particularly challenging subtype worldwide. Our study aims to evaluate whether Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (18F-FDG PET/CT) parameters, clinical pathological features, and biochemical indicators serve as prognostic risk factors for TNBC. Additionally, we explore correlations between biochemical indicators and 18F-FDG PET/CT parameters. We conducted a retrospective analysis of 95 TNBC patients who underwent preoperative 18F-FDG PET/CT examinations at Tianjin Medical University Cancer Institute and Hospital from 2013 to 2018. Collected data included 18F-FDG PET/CT parameters, clinical and pathological features, and biochemical indicators. We used Kaplan-Meier survival analysis and multivariate Cox regression analysis to evaluate associations between 18F-FDG PET/CT parameters/biochemical indicators and disease free survival (DFS)/overall survival (OS). The log-rank test determined significant differences in survival curves, and the Spearman correlation coefficient analyzed correlations between quantitative variables. Visualization and analysis were performed using R packages. Among 95 TNBC patients, mean standardized uptake value (SUVmean) was significantly correlated with DFS. Fasting blood glucose (FBG), α- L-fucosylase (AFU) and Creatine kinase (CK) were independent predictors of DFS, while Precursor albumin (PALB) and CK were independent predictors of OS. FBG showed correlations with SUVpeak and SUVmean, and CK was correlated with peak standardized uptake value (SUVpeak). Our results indicated that 18F-FDG PET/CT parameters and biochemical indicators may constitute a new prognostic model for TNBC patients post-surgery. We found that SUVmean, FBG, AFU and CK are predictive factors for DFS in TNBC patients post-surgery, while PALB and CK are predictive factors for OS, which prompts us to pay more attention to these indicators in clinical practice. Also 18F-FDG PET/CT parameters and biochemical indicators have potential utility in constituting a new prognostic model for TNBC patients post-surgery.

Short-term outcomes of intravesical gemcitabine for non-muscle-invasive bladder cancer after recent approval for use in Korea.

In high-risk non-muscle-invasive bladder cancer (NMIBC), intravesical Bacillus Calmette-Guérin (BCG) is the standard adjuvant therapy post-transurethral resection of bladder tumor (TURBT). Intravesical gemcitabine, used as an alternative or second-line therapy amid BCG shortages, lacks outcome studies in the Korean population. Patients who received weekly intravesical gemcitabine for 6 weeks after TURBT from 2019 to 2022 were retrospectively investigated. Based on the American Urological Association risk classification, patients with high- or very high-risk NMIBC who refused cystectomy were included. Maintenance treatment was performed depending on their risk. Recurrence was defined as histologic confirmation on subsequent cystoscopic biopsies or TURBT. Disease free survival (DFS) was evaluated by the Kaplan-Meier method. The study included 60 patients, comprising 45 high-risk (group 1) patients with a median age of 76 years and 15 very high-risk (group 2) patients with a median age of 68 years. Among them, 28 patients had previously received intravesical BCG. Over a median follow-up of 22 months, recurrence occurred in 31 patients in group 1 and 11 in group 2. The DFS rates of the high-risk group and the very high-risk group were 57.8% versus 40% at 1 year, 20.7% versus 21.3% at 2 years and 20.7% versus 21.3% at 3 years, respectively (p=0.831). Tis stage (p=0.042) and prostatic urethra invasion (p=0.028) were significant predictors of DFS. Cancer-specific mortality rates were 2.2% in group 1 and 6.7% in group 2 (p=0.441). Similar DFS outcome between high-risk and very high-risk patients were observed based on short-term results in Korea. This finding is crucial for clinical practice; however, studies analyzing more patients and long-term outcomes are needed.

Treatment Outcomes and Toxicity Profiles with PORTEC-3 Trial Regimen in South Asian Cohort of High-Risk Endometrial Cancer Patients: A Single-Center Ambispective Analysis

Objectives Adjuvant chemoradiation followed by chemotherapy is the current standard of care in high-risk endometrial cancer after the PORTEC-3 trial. There is a lack of data on this treatment regimen in the South Asian patient cohort. The present study aims to assess toxicity profiles and outcomes in this cohort of patients. Materials and Methods High-risk endometrial cancer patients planned for adjuvant chemoradiation followed by chemotherapy were included. Toxicity was graded using the Radiation Therapy Oncology Group and Common Terminology Criteria for Adverse Events criteria. Disease-free survival (DFS) and overall survival (OS) were estimated using the Kaplan–Meier method. Survival curves were compared using the log-rank test. Cox regression analysis was done to find out the predictors of DFS. Results This study included 58 patients treated from October 2016 to August 2022. Median age was 61 years (interquartile range [IQR] 56–66), with Fédération Internationale de Gynécologie et d'Obstétrique Stages I = 26 (44.8%), II = 5 (8.6%), and III = 27 (46.6%). p53 positivity was seen in 38 (65.5%) patients. Intensity-modulated radiotherapy was used in 44 (79.3%) patients. There was no treatment discontinuation during chemoradiation. Acute Grade 2 and above toxicity during chemoradiation were diarrhea in 10 (17.2%) and hematological in 2 (3.4%). For the planned adjuvant chemotherapy in 55 patients, 51 (92.7%) completed four cycles. Grade 2 or above neuropathy was seen in 11 (20%), with 5 (9%) having persisting neuropathy at 1-year follow-up. At a median follow-up of 31 months, 15 (25.8%) patients recurred; distant = 13 and isolated para-aortic = 2. The median time to recurrence was 16 months (IQR 12–22), with 80% (12 out of 15) of recurrence within the first 2 years of follow-up. The actuarial 5-year DFS and OS were 63.8 and 76.5%, respectively. In univariate analysis, p53 positivity and lymphovascular space invasion were predictors for DFS, with p-values 0.031 and 0.027, respectively. There was no significant predictor identified in multivariate analysis. Conclusion There is good tolerance and compliance to adjuvant chemoradiation and chemotherapy in this South Asian cohort of patients with high-risk endometrial cancer, with no toxicity-related treatment breaks during radiation. The majority of the recurrences were seen at distant sites and within the first 2 years of follow-up. These findings are in line with the outcomes of the PORTEC-3 trial.

A nomogram based on inflammation and nutritional biomarkers for predicting the survival of breast cancer patients.

We aim to develop a new prognostic model that incorporates inflammation, nutritional parameters and clinical-pathological features to predict overall survival (OS) and disease free survival (DFS) of breast cancer (BC) patients. The study included clinicopathological and follow-up data from a total of 2857 BC patients between 2013 and 2021. Data were randomly divided into two cohorts: training (n=2001) and validation (n=856) cohorts. A nomogram was established based on the results of a multivariate Cox regression analysis from the training cohorts. The predictive accuracy and discriminative ability of the nomogram were evaluated by the concordance index (C-index) and calibration curve. Furthermore, decision curve analysis (DCA) was performed to assess the clinical value of the nomogram. A nomogram was developed for BC, incorporating lymphocyte, platelet count, hemoglobin levels, albumin-to-globulin ratio, prealbumin level and other key variables: subtype and TNM staging. In the prediction of OS and DFS, the concordance index (C-index) of the nomogram is statistically greater than the C-index values obtained using TNM staging alone. Moreover, the time-dependent AUC, exceeding the threshold of 0.7, demonstrated the nomogram's satisfactory discriminative performance over different periods. DCA revealed that the nomogram offered a greater overall net benefit than the TNM staging system. The nomogram incorporating inflammation, nutritional and clinicopathological variables exhibited excellent discrimination. This nomogram is a promising instrument for predicting outcomes and defining personalized treatment strategies for patients with BC.

Preoperative Vertebral Fracture: A Prognostic Factor in Stage I-III Colorectal Cancer.

This study evaluated the prognostic impact of vertebral fractures (VFs) on the survival of patients with colorectal cancer (CRC). We included 299 patients with stage I-III CRC who had undergone elective surgery. The patients were divided into the VF group (n=94) and non-VF group (n=205). VFs were assessed using sagittal computed tomography image reconstruction (Th11-L5) performed preoperatively. Disease-free survival (DFS) and overall survival (OS) rates were analyzed. The VF group had lower 5-year DFS and OS rates compared to the non-VF group (both, p<0.001). The independent predictors of DFS were carbohydrate antigen 19-9 (CA19-9) ≥37.0 ng/ml, T3/T4 disease, stage III CRC, osteopenia, and VF; for OS, CA19-9 ≥37.0 ng/ml, stage III, osteopenia, and VF. VF, compared with osteopenia, was a more significant prognostic factor for DFS and OS in patients with stage I+ II CRC (both, p<0.001). Preoperative VF was associated with worse DFS and OS following CRC resection.

Salvage radiotherapy for locally recurrent cervical and endometrial carcinoma: clinical outcomes and toxicities

BackgroundThe management of locally recurrent gynecological carcinoma remains a challenge due to the limited availability of data. This study aims to share our institutional experience in using definitive radiotherapy (RT) for the treatment of locally recurrent cervical and endometrial carcinoma.MethodsThe study retrospectively reviewed 20 patients in our hospital completing salvage 3D image-based HDR brachytherapy, with or without EBRT, for locally recurrent cervical and endometrial carcinoma after surgery. The Kaplan–Meier method was applied to estimate the disease-free survival (DFS) and overall survival (OS). The toxicities were assessed by CTCAEv5.ResultsDuring a median observation period of 21 months, the study reported a tumor objective response rate of 95%. The 3-year DFS and OS rates were 89.4% and 90.9%, respectively. The EBRT combined with brachytherapy achieved a median cumulative dose of 88 Gy to CTV D90. 14 patients received concurrent and/or systemic chemotherapy. Two patients suffered locoregional recurrence after salvage treatment, one of whom only received salvage brachytherapy for prior RT history. The analysis identified significant predictors for DFS, including tumor histology and FIGO stage. 5 patients observed acute grade 1–2 rectal (15%) or genitourinary (10%) toxicities. Late toxicities including grade 1–2 rectal bleeding (10%) and grade 2 pelvic fracture (5%) were seen in 3 patients.Conclusions3D image-guided brachytherapy combined with EBRT shows effective tumor control and acceptable toxicity profile for women with locally recurrent gynecologic cancer. The success in managing vaginal recurrence is notably influenced by histologic subtype and FIGO staging.

Does chronic kidney disease affect the short-term outcomes and prognosis of colorectal cancer surgery? A propensity score matching analysis.

The aim of this study was to analyze the effect of chronic kidney disease (CKD) on the short-term outcomes and prognosis of colorectal cancer (CRC) patients who underwent primary surgery. CRC patients who underwent radical surgery were included from Jan 2011 to Jan 2020 in a single hospital. The short-term outcomes and prognosis were compared between the CKD group and the Non-CKD group using propensity score matching (PSM) analysis. A total of 4056 patients undergoing CRC surgery were included, including 723 patients in the CKD group and 3333 patients in the Non-CKD group. After 1:1 PSM, there were 666 patients in each group, respectively. No significant difference was found in baseline characteristics between the two groups. (p>0.05). After PSM, the CKD group had a longer postoperative hospital stay (P=0.009) and a higher incidence of overall complications (p=0.050). Cox analysis was performed on matched patients to find predictors of overall survival (OS) and disease-free survival (DFS). We found that age (p<0.01, HR=1.045, 95% CI=1.028-1.062), tumor stage (p<0.01, HR=1.931, 95% CI=1.564-2.385) and overall complications (p<0.01, HR=1.858, 95% CI=1.423-2.425) were independent predictors of OS. Age (p<0.01, HR=1.034, 95% CI=1.020-1.049), tumor stage (p<0.01, HR=1.852, 95% CI=1.537-2.231), and overall complications (p<0.01, HR=1.651, 95% CI=1.295-2.10) were independent predictors of DFS. However, CKD was not an independent predictor of OS or DFS (OS: p=0.619, HR=1.070, 95% CI=0.820-1.396; DFS: p=0.472, HR=1.092, 95% CI=0.859-1.389). CKD prolonged postoperative hospital stay; however, CKD might not affect major postoperative complications, OS or DFS of CRC.

Machine learning evaluation of immune infiltrate through digital tumour score allows prediction of survival outcome in a pooled analysis of three international stage III colon cancer cohorts

T-cell immune infiltrates are robust prognostic variables in localised colon cancer. Evaluation of prognosis using artificial intelligence is an emerging field. We evaluated whether machine learning analysis improved prediction of patient outcome in comparison with analysis of T cell infiltrate only or in association with clinical variables. We used data from two phase III clinical trials (Prodige-13 and PETACC08) and one retrospective Italian cohort (HARMONY). Cohorts were split into training (N=692), internal validation (N=297) and external validation (N=672) sets. Tumour slides were stained with CD3mAb. CD3 Machine Learning (CD3ML) score was computed using graphical parameters within the tumour tiles obtained from CD3 slides. CD3 infiltrates in tumour core and invasive margin were automatically detected. Associations of CD3 infiltrates and CD3ML with 5-year Disease-Free Survival (DFS) were examined using univariate and multivariable survival models by Cox regression. CD3 density both in the invasive margin and the tumour core were significantly associated with DFS in the different sets. Similarly, CD3ML score was significantly associated with DFS in all sets. CD3 assessment did not provide added value on top of CD3ML assessment (Likelihood Ratio Test (LRT), p=0.13). In contrast, CD3ML improved prediction of DFS when combined with a clinical risk stage (LRT, p=0.001). Stratified by clinical risk score (High or Low), patients with low CD3ML score had better DFS. In all tested sets, machine learning analysis of tumour cells improved prediction of prognosis compared to clinical parameters. Adding tumour-infiltrating lymphocytes assessment did not improve prognostic determination. This research received no external funding.

Integrating IASLC grading and radiomics for predicting postoperative outcomes in stage IA invasive lung adenocarcinoma.

The International Association for the Study of Lung Cancer (IASLC) Pathology Committee introduced a histologic grading system for invasive lung adenocarcinoma (LUAD) in 2020. The IASLC grading system, hinging on the evaluation of predominant and high-grade histologic patterns, has proven to be practical and prognostic for invasive LUAD. However, there are still limitations in evaluating the prognosis of stage IA LUAD. Radiomics may serve as a valuable complement. To establish a model that integrates IASLC grading and radiomics, aimed at predicting the prognosis of stage IA LUAD. We conducted a retrospective analysis of 628 patients diagnosed with stage IA LUAD who underwent surgical resection between January 2015 and December 2018 at our institution. The patients were randomly divided into the training set (n=439) and testing set (n=189) at a ratio of 7:3. Overall survival (OS) and disease-free survival (DFS) were taken as the end points. Radiomics features were obtained by PyRadiomics. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO). The prediction models for OS and DFS were developed using multivariate Cox regression analysis, and the models were visualized through nomogram plots. The model's performance was evaluated using area under the curves (AUC), concordance index (C-index), calibration curves, and survival decision curve analysis (DCA). In total, nine radiomics features were selected for the OS prediction model, and 15 radiomics features were selected for the DFS prediction model. Patients with high radiomics scores were associated with a worse prognosis (p<0.001). We built separate prediction models using radiomics or IASLC alone, as well as a combined prediction model. In the prediction of OS, we observed that the combined model (C-index: 0.812±0.024, 3 years AUC: 0.692, 5 years AUC: 0.792) achieved superior predictive performance than the radiomics (C-index: 0.743±0.038, 3 years AUC: 0.633, 5 years AUC: 0.768) and IASLC grading (C-index: 0.765±0.042, 3 years AUC: 0.658, 5 years AUC: 0.743) models alone. Similar results were obtained in the models for DFS. The combination of radiomics and IASLC pathological grading proves to be an effective approach for predicting the prognosis of stage IA LUAD. This has substantial clinical relevance in guiding treatment decisions for early-stage LUAD.

Selection of postoperative adjuvant therapy for patients with stage IB lung adenocarcinoma: analysis of 653 cases

To explore the optimal postoperative adjuvant regimens for patients with stage IB lung adenocarcinoma. We respectively analyzed the data of 653 patients undergoing surgery for stage IB lung adenocarcinoma in our hospital from January, 2013 to December, 2021. The 5-year disease-free survival (DFS) and overall survival (OS) rates were compared among the patients receiving postoperative adjuvant therapy with epidermal growth factor-tyrosine kinase inhibitors (EGFR-TKIs group, n=111), chemotherapy (CT group, n=108) and clinical observation (CO group, n=434). In TKIs, CT, and CO groups, the 5-year DFS rates were 92.8%, 80.7%, and 81.7%, respectively, significantly higher in TKIs group than in CO group (P < 0.01). The 3-year OS rates of the 3 groups were 96.8%, 97.1%, and 91.7%, respectively. Subgroup analysis showed that in TKIs, CT, and CO groups, the 5-year DFS rates of patients with with T3-4 cmN0M0 were 92.6%, 84.0%, and 81.4%, respectively, significantly higher in TKIs group than in CO group (P < 0.05); the 5-year DFS rates of T2ViscPlN0M0 patients were 95.1%, 71.4%, and 83.5%, respectively. Multivariate COX regression analysis showed that age (P < 0.05; HR=0.631, 95% CI: 0.401-0.993), solid nodules (P < 0.01; HR=7.620, 95% CI: 3.037-19.121), micropapillary or solid component (P < 0.05; HR= 1.776, 95% CI: 1.010-3.122), lymphovascular invasion (P < 0.05; HR=2.981, 95% CI: 1.198-7.419), and adjuvant therapy (P < 0.01) were independent predictors of DFS. The most common adverse effects included rashes, paronychia, and diarrhea for TKIs and hematological suppression and gastrointestinal reactions for chemotherapy, and TKIs were associated with a higher incidence of grade 3 or above adverse effects (44.4% vs 9.0%). Adjuvant therapy with TKIs helps improve DFS in patients with stage IB (T3-4cmN0M0) lung adenocarcinoma but not in patients with T2ViscPlN0M0. Adjuvant chemotherapy does not improve DFS or OS in patients with stage IB lung adenocarcinoma.

A multi-parametric prognostic model based on clinicopathologic features: vessels encapsulating tumor clusters and hepatic plates predict overall survival in hepatocellular carcinoma patients

BackgroundVessels encapsulating tumor clusters (VETC) is a newly described vascular pattern that is distinct from microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Despite its importance, the current pathological diagnosis report does not include information on VETC and hepatic plates (HP). We aimed to evaluate the prognostic value of integrating VETC and HP (VETC-HP model) in the assessment of HCC.MethodsA total of 1255 HCC patients who underwent radical surgery were classified into training (879 patients) and validation (376 patients) cohorts. Additionally, 37 patients treated with lenvatinib were studied, included 31 patients in high-risk group and 6 patients in low-risk group. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to establish a prognostic model for the training set. Harrell’s concordance index (C-index), time-dependent receiver operating characteristics curve (tdROC), and decision curve analysis were utilized to evaluate our model's performance by comparing it to traditional tumor node metastasis (TNM) staging for individualized prognosis.ResultsA prognostic model, VETC-HP model, based on risk scores for overall survival (OS) was established. The VETC-HP model demonstrated robust performance, with area under the curve (AUC) values of 0.832 and 0.780 for predicting 3- and 5-year OS in the training cohort, and 0.805 and 0.750 in the validation cohort, respectively. The model showed superior prediction accuracy and discrimination power compared to TNM staging, with C-index values of 0.753 and 0.672 for OS and disease-free survival (DFS) in the training cohort, and 0.728 and 0.615 in the validation cohort, respectively, compared to 0.626 and 0.573 for TNM staging in the training cohort, and 0.629 and 0.511 in the validation cohort. Thus, VETC-HP model had higher C-index than TNM stage system(p < 0.01).Furthermore, in the high-risk group, lenvatinib alone appeared to offer less clinical benefit but better disease-free survival time.ConclusionsThe VETC-HP model enhances DFS and OS prediction in HCC compared to traditional TNM staging systems. This model enables personalized temporal survival estimation, potentially improving clinical decision-making in surveillance management and treatment strategies.

The association between referral by specialists in oral diagnosis on survival rates of patients with oral cancer: A retrospective cohort study.

To assess the influence of diagnosis and referral provided by specialists in oral diagnosis on disease-free survival and overall survival of patients with oral cancer. A cohort of 282 patients with oral cancer treated at a regional cancer hospital from 1998 to 2016 was analyzed retrospectively. The referral register of the patients was analyzed and assigned to two groups: (1) those referred by oral diagnosis specialists (n = 129), or (2) those referred by nonspecialized professionals (n = 153). The cancer treatment evolution was assessed from the patients' records, and the outcome was registered concerning cancer recurrence and death. Sociodemographic and clinicopathological variables were explored as predictors of disease-free survival and overall survival. Group 1 exhibited lower T stages and a reduced incidence of regional and distant metastases. Surgery was performed in 75.2% of cases in Group 1, while in Group 2, the rate was 60.8%. Advanced T stages and regional metastases reduced the feasibility of surgery. Higher TNM stages and tumor recurrence were associated with decreased disease-free survival, while surgical intervention was a protective factor. Higher TNM stage had a negative impact on the overall survival. Specialized oral diagnosis did not directly impact disease-free survival and overall survival and did not influence the indication of surgery in oral cancer; however, it was associated with the diagnosis of early tumors and better prognosis.

Machine learning-based radiomics for predicting outcomes in cervical cancer patients undergoing concurrent chemoradiotherapy

PurposesTo investigate the value of machine learning-based radiomics for predicting disease-free survival (DFS) and overall survival (OS) undergoing concurrent chemoradiotherapy (CCRT) for patients with locally advanced cervical cancer (LACC). Materials and methodsIn this multicentre study, 700 patients with IB2-IVA cervical cancer who underwent CCRT with ongoing follow-up were retrospectively analyzed. Three-dimensional radiomics features of primary lesions and its surrounding 5 mm region in T2WI sequences were collected. Six machine learning methods were used to construct the optimal radiomics model for accurate prediction of DFS and OS after CCRT in LACC patients. Eventually, TCGA and GEO databases were used to explore the mechanisms of radiomics in predicting the progression and survival of cervical cancer. This study adhered CLEAR for reporting and its quality was assessed using RQS and METRICS. ResultsIn the prediction of DFS, the RSF model combined tumor and peritumor radiomics demonstrated the best predictive efficacy, with the AUC for predicting 1-year, 3-year, and 5-year DFS in the training, validation, and test sets of 0.986, 0.989, 0.990, and 0.884, 0.838, 0.823, and 0.829, 0.809, 0.841, respectively. In the prediction of OS, the GBM model best performer, with AUC of 0.999, 0.995, 0.978, and 0.981, 0.975, 0.837, and 0.904, 0.860, 0.905. Differential genes in TCGA and GEO suggest that the prediction of radiomics model may be associated with KDELR2 and HK2. ConclusionMachine learning-based radiomics models help to predict DFS and OS after CCRT in LACC patients, and the combination of tumor and peritumor information has higher predictive efficacy, which can provide a reliable basis for therapeutic decision-making in cervical cancer patients.