The Relationship between Primary Gross Tumor Volume and Tumor Response of Locally Advanced Rectal Cancer: pGTV as a More Accurate Tumor Size Indicator

Radiomics from Mesorectal Blood Vessels and Lymph Nodes: A Novel Prognostic Predictor for Rectal Cancer with Neoadjuvant Therapy

The objective of our study is to investigate the predictive value of various combinations of radiomic features from intratumoral and different peritumoral regions of interest (ROIs) for achieving a good pathological response (pGR) following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study was conducted using data from LARC patients who underwent nCRT between 2013 and 2021. Patients were divided into training and validation cohorts at a ratio of 4:1. Intratumoral ROIs (ROI_ITU) were segmented on T2-weighted imaging, while peritumoral ROIs were segmented using two methods: ROI_{PTU_2mm}, ROI_{PTU_4mm}, and ROI_{PTU_6mm}, obtained by dilating the boundary of ROI_ITU by 2 mm, 4 mm, and 6 mm, respectively; and ROI_{MR_F} and ROI_{MR_BVLN}, obtained by separating the fat and blood vessels + lymph nodes in the mesorectum. After feature extraction and selection, 12 logistic regression models were established using radiomics features derived from different ROIs or ROI combinations, and five-fold cross-validation was performed. The average area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the models. The study included 209 patients, consisting of 118 pGR and 91 non-pGR patients. The model that integrated ROI_ITU and ROI_{MR_BVLN} features demonstrated the highest predictive ability, with an AUC (95% confidence interval) of 0.936 (0.904-0.972) in the training cohort and 0.859 (0.745-0.974) in the validation cohort. This model outperformed models that utilized ROI_ITU alone (AUC = 0.779), ROI_{MR_BVLN} alone (AUC = 0.758), and other models. The radscore derived from the optimal model can predict the treatment response and prognosis after nCRT. Our findings validated that the integration of intratumoral and peritumoral radiomic features, especially those associated with mesorectal blood vessels and lymph nodes, serves as a potent predictor of pGR to nCRT in patients with LARC. Pending further corroboration in future research, these insights could provide novel imaging markers for refining therapeutic strategies.

Tumor Volume Predicts for Pathologic Complete Response in Rectal Cancer Patients Treated With Neoadjuvant Chemoradiation

OBJECTIVES

Nonoperative management (NOM) of locally advanced rectal cancer is an emerging approach allowing patients to preserve their anal sphincter. Identifying clinical factors associated with pathologic complete response (pCR) is essential for physicians and patients considering NOM.

MATERIALS AND METHODS

In total, 412 locally advanced rectal cancer patients were included in this retrospective analysis. Tumor volumes were derived from pretreatment MRI. Clinical parameters such as tumor volume, stage, and location were analyzed by univariate and multivariate analysis, against pCR. A receiver operator characteristic curve was generated to identify a tumor volume cut-off with the highest clinically relevant Youden index for predicting pCR.

RESULTS

Seventy-five of 412 patients (18%) achieved pCR. A tumor volume threshold of 37.3 cm 3 was identified as predictive for pCR. On regression analysis, a tumor volume >37.3 cm 3 was associated with a greater than 78% probability of not achieving pCR. On multivariate analysis, a GTV <37.3 cm 3 [odds ratio (OR)=3.7, P <0.0001] was significantly associated with an increased pCR rate, whereas tumor length > 4.85 cm was associated with pCR on univariate (OR=3.03, P <0.01) but not on multivariate analysis (OR=1.45, P =0.261). Other clinical parameters did not impact pCR rates.

CONCLUSIONS

A tumor volume threshold of 37.3 cm 3 was identified as predictive for pCR in locally advanced rectal cancer patients receiving neoadjuvant chemoradiation. Tumors above this volume threshold corresponded to a greater than 78% probability of not achieving pCR. This information will be helpful at diagnosis for clinicians who are considering potential candidates for NOM.

Machine Learning of Dose-Volume Histogram Parameters Predicting Overall Survival in Patients with Cervical Cancer Treated with Definitive Radiotherapy

Purpose

To analyze the effects of dosimetric parameters and clinical characteristics on overall survival (OS) by machine learning algorithms.

Methods and Materials

128 patients with cervical cancer were treated with definitive pelvic radiotherapy with or without chemotherapy followed by image-guided brachytherapy. The elastic-net models with integrating DVH parameters and baseline clinical factors, only DVH parameters and only baseline clinical factors were constructed in 5-folds cross-validations for 100 iteration bootstrapping, and then were compared using concordance index (C-index) criteria. Finally, the selected important factors were used to build multivariable Cox-pH models for OS and also shown in nomograms for clinical usage.

Results

The median OS occurred was 25.78 months with 25 (19.53%) deaths. The elastic-net models integrating clinical and DVH factors had the best prediction performances (C-index 0.76 in the train set and C-index 0.74 in the test set). Three important factors were selected, including baseline hemoglobin level as the protective factor, primary tumor volume (GTV_P) volume, and body V5 as the risk factors. The final multivariable Cox-pH models were constructed using these important factors and had prediction performance (C-index: 0.78, 95%CI: 0.73–0.81).

Conclusions

This is the first attempt to establish elastic-net models to study the contributions of DVH parameters for predicting OS in patients with cervical cancer. These results can facilitate individualized tailoring of radiation treatment in cervical cancer patients.

Pre-Treatment Computed Tomography Radiomics for Predicting the Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Retrospective Study

Background and Purpose

Computerized tomography (CT) scans are commonly performed to assist in diagnosis and treatment of locally advanced rectal cancer (LARC). This study assessed the usefulness of pretreatment CT-based radiomics for predicting pathological complete response (pCR) of LARC to neoadjuvant chemoradiotherapy (nCRT).

Materials and Methods

Patients with LARC who underwent nCRT followed by total mesorectal excision surgery from July 2010 to December 2018 were enrolled in this retrospective study. A total of 340 radiomic features were extracted from pretreatment contrast-enhanced CT images. The most relevant features to pCR were selected using the least absolute shrinkage and selection operator (LASSO) method and a radiomic signature was generated. Predictive models were built with radiomic features and clinico-pathological variables. Model performance was assessed with decision curve analysis and was validated in an independent cohort.

Results

The pCR was achieved in 44 of the 216 consecutive patients (20.4%) in this study. The model with the best performance used both radiomics and clinical variables including radiomic signatures, distance to anal verge, lymphocyte-to-monocyte ratio, and carcinoembryonic antigen. This combined model discriminated between patients with and without pCR with an area under the curve of 0.926 and 0.872 in the training and the validation cohorts, respectively. The combined model also showed better performance than models built with radiomic or clinical variables alone.

Conclusion

Our combined predictive model was robust in differentiating patients with and without response to nCRT.

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

BACKGROUND

The purpose of this study was to investigate and validate multiparametric magnetic resonance imaging (MRI)-based machine learning classifiers for early identification of poor responders after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).

METHODS

Patients with LARC who underwent nCRT were included in this retrospective study (207 patients). After preprocessing of multiparametric MRI, radiomics features were extracted and four feature selection methods were used to select robust features. The selected features were used to build five machine learning classifiers, and 20 (four feature selection methods × five machine learning classifiers) predictive models for the screening of poor responders were constructed. The predictive models were evaluated according to the area under the curve (AUC), F1 score, accuracy, sensitivity, and specificity.

RESULTS

Eighty percent of all predictive models constructed achieved an AUC of more than 0.70. A predictive model using a support vector machine classifier with the minimum redundancy maximum relevance (mRMR) selection method followed by the least absolute shrinkage and selection operator (LASSO) selection method showed superior prediction performance, with an AUC of 0.923, an F1 score of 88.14%, and accuracy of 91.03%. The predictive performance of the constructed models was not improved by ComBat compensation.

CONCLUSIONS

In rectal cancer patients who underwent neoadjuvant chemoradiotherapy, machine learning classifiers with radiomics features extracted from multiparametric MRI were able to accurately discriminate poor responders from good responders. The techniques should provide additional information to guide patient-tailored treatment.

The Survival Effect of Radiotherapy on Stage II/III Rectal Cancer in Different Age Groups: Formulating Radiotherapy Decision-Making Based on Age

Introduction

Total mesorectal excision (TME), chemotherapy (CT), and radiotherapy (RT) are usually integrated into the comprehensive treatment of stage II/III rectal cancer (RC). Neoadjuvant radiotherapy (nRT) has become the standard treatment for stage II/III RC patients to help reduce the size of a tumor or kill cancer cells that have spread. Adjuvant RT is delivered after the resection to destroy remaining cancer cells and used mainly in stage II/III RC patients who have not received preoperative radiotherapy, such as those who suffered from a bowel obstruction before surgery. It is controversial whether radiotherapy can improve the survival of stage II/III RC patients. An increasing number of studies have reported that rectal cancer exhibited mismatched biology, epidemiology, and therapeutic response to current treatment strategy in different age groups. It is necessary to investigate whether radiotherapy exhibits disparate effects in different age groups of patients with stage II/III RC.

Methods

Data from the Surveillance, Epidemiology, and End Results (SEER) Program was extracted to identify stage II/III RC diagnosed in the periods of 2004-2016. The statistical methods included Pearson's chi-square test, log-rank test, Cox regression model, and propensity score matching.

Results

Neoadjuvant radiotherapy (nRT) cannot improve the prognosis, and postoperative RT may even reduce the survival time for early onset stage II/III RC. Postoperative RT was not able to improve the overall survival (OS), while nRT may provide limited survival improvement for middle-aged stage II/III RC patients. In addition, radiotherapy can significantly improve the prognosis for elderly stage II/III RC.

Conclusions

This study indicated the inconsistent survival effect of radiotherapy on stage II/III rectal cancer patients in different age groups. Hence, we formulated a novel flow chart of radiotherapy decision-making based on age in stage II/III RC patients.

Accurate nomograms with excellent clinical value for locally advanced rectal cancer

Background

Rectal cancer accounts for approximately 30–50% of colorectal cancer. Despite its widespread use and convenience, the American Joint Committee on Cancer (AJCC) staging system for predicting survival is prone to inaccuracy, even including a survival paradox for locally advanced rectal cancer (LARC). An accurate risk stratification of LARC is essential for proper treatment selection and prognostic evaluation. Therefore, we aimed to create prognostic nomograms for LARC capable of assessing overall survival (OS) and cancer-specific survival (CSS) precisely and intuitively.

Methods

The Surveillance, Epidemiology, and End Results (SEER) database was accessed. All of the significant variables in the multivariate analysis were integrated to build the nomograms.

Results

Data for a total of 23,055 patients with LARC were collected from the SEER database in this study. Based on the multivariate Cox regression analysis, both OS and CSS were significantly associated with 13 variables: age, marital status, race, pathological grade, histological type, T stage, N stage, surgery, radiotherapy, chemotherapy, regional nodes examined (RNE), tumor size, and carcinoembryonic antigen (CEA). These were included in the construction of nomograms for OS and CSS. Time-dependent receiver operating characteristic (ROC) curves, decision curve analysis (DCA), concordance index, and calibration curves demonstrated the discriminative superiority of the nomograms.

Conclusions

The nomograms, which effectively solve the issue of the survival paradox in the AJCC staging system regarding LARC, may act as excellent tools for integrating clinical characteristics and to guiding therapeutic choices for LARC patients.

Predictive and Prognostic Factors of Synchronous Colorectal Lung-Limited Metastasis

Aim

This study is aimed at investigating predictive and prognostic factors of synchronous colorectal lung-limited metastasis (SCLLM) based on The Surveillance, Epidemiology, and End Results (SEER) database.

Methods

A multivariate logistic regression model was constructed to identify independent predictors of SCLLM. A multivariate Cox proportional hazards regression model was used to distinguish independent prognostic factors.

Results

This study enrolled 168,007 colorectal cancer (CRC) patients without metastatic diseases and 1,298 cases with SCLLM. Eight features, involving race, tumor location, pathological grade, histological type, T stage, N stage, and tumor size as well as CEA, could be used as the independent predictors. As the nomogram shown, the T4 stage contributed the most to SCLLM, followed by the N2 stage, elevated CEA, and rectal cancer. A multivariate regression analysis discriminated 9 independent prognostic factors, including age, race, marital status, pathological grade, T stage, colectomy/proctectomy, chemotherapy, CEA, and TD. The prognostic nomogram illustrated that nonresection/NOS played as the poorest prognostic factor, followed by nonchemotherapy, ≥75-year old and T4 stage. The cumulative survival curves revealed the influence of each prognostic factor on survival after controlling the other variables.

Conclusions

This study identified independent predictors and prognostic factors for SCLLM based on a large database of the United States. The predictors and prognostic factors can provide supporting evidence for the prevention and treatment of SCLLM.

Plant homeodomain finger protein 23 inhibits autophagy and promotes apoptosis of chondrocytes in osteoarthritis

BACKGROUND

Plant homeodomain finger protein 23 (PHF23) is a novel autophagy inhibitor gene that has been few studied with respect to orthopedics. This study was to investigate the expression of PHF23 in articular cartilage and synovial tissue, and analyze the relationship between PHF23 and chondrocyte autophagy in osteoarthritis (OA).

METHODS

Immunohistochemical staining and western blot were applied to show the expression of PHF23 in cartilage of different outbridge grades and synovial tissue of patient with OA and healthy control. The normal human chondrocyte pre-treated with rapamycin or 3-methyladenine, treated with interleukin-1β (IL-1β). IL-1β induced expression level of PHF23 and autophagy-related proteins light chain 3B-I (LC3B-I), LC3B-II, and P62, were examined by Western blot. A PHF23 gene knock-down model was constructed with small interfering RNA. Western blot was performed to detect the efficiency of PHF23 and the impact of PHF23 knockout on IL-1β-induced expression of autophagy-related and apoptotic-related proteins in chondrocyte.

RESULTS

The expression of PHF23 was significantly increased in the high-grade cartilage and synovial tissue of patients with OA. The IL-1β-induced expression of PHF23 was gradually enhanced with time. The level of LC3B-II, P62 changed with time. After knockdown of PHF23, the level of autophagy-related proteins increased and apoptotic-related proteins decreased in IL-1β-induced OA-like chondrocytes.

CONCLUSIONS

The expression of PHF23 increased in human OA cartilage and synovium, and was induced by IL-1β through inflammatory stress. PHF23 can suppress autophagy of chondrocytes, and accelerate apoptosis.

Establishment and Verification of Synchronous Metastatic Nomogram for Gastrointestinal Stromal Tumors (GISTs): A Population-Based Analysis

Aim

Assess the risk of synchronous metastasis and establish a nomogram in patients with GISTs.

Methods

Surveillance, Epidemiology and End Results database (2004-2014) was accessed. With the logistic regression model as the basis, a nomogram was constructed.

Results

7,256 target patients were contained in our study. The nomogram discrimination for mGIST prediction revealed that tumor size contributed most to synchronous metastasis, followed by lymph nodes, extension, pathologic grade, tumor location, and mitotic count. C-index values of predictions were 0.821 (95% CI, 0.805-0.836) and 0.815 (95% CI, 0.800-0.831), and Brier score were 0.109 and 0.112 in training and validation group, respectively. The value of area under the ROCs were 0.813 (p < 0.001) in the primary cohort and 0.819 (p < 0.001) in the validation cohort. Through the calibration curves (as seen in the figures), nomogram prediction proved to have excellent agreement with actual metastatic diseases.

Conclusion

A new nomogram was created that can evaluate synchronous metastatic diseases in patients with GISTs.

Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer

BACKGROUND

Total mesorectal excision following neoadjuvant chemoradiotherapy (nCRT) is recommended in the latest treatment of locally advanced rectal cancer (LARC).

OBJECTIVE

To predict whether patients with LARC can achieve pathologic complete response (pCR), comparing MRI-based radiomics between before and after neoadjuvant radiotherapy (nRT) was performed.

METHODS

One hundred and sixty-five MRI-based radiomics features in axial T2-weighted images were obtained quantitatively from Imaging Biomarker Explorer Software. The specific features of conventional and developing radiomics were selected with the analysis of least absolute shrinkage and selection operator logistic regression, of which the predictive performance was analyzed with receiver operating curve and calibration curve, and applied to an independent cohort.

RESULTS

One hundred and thirty-one target patients were enrolled in the present study. A radiomics signature founded on seven radiomics features was generated in the primary cohort. A remarkable difference about Rad-score between pCR and non-pCR group occurred in both of primary (P < .001) or validation cohorts (P < .001). The value of area under the curves was 0.92 (95% CI, 0.86-0.99) and 0.87 (95% CI, 0.74-1.00) in the primary and validation cohorts, respectively. The Rad-score (OR = 23.581; P < .001) from multivariate logistic regression analysis was significant as an independent factor of pCR.

CONCLUSION

Our predictive model based on radiomics features was an independent predictor for pCR in LARC and could be a candidate in clinical practice.

Optimized Parameters of Diffusion-Weighted MRI for Prediction of the Response to Neoadjuvant Chemoradiotherapy for Locally Advanced Rectal Cancer

Aim

To identify the optimal diffusion-weighted MRI-derived parameters for predicting the response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

Methods

This prospective study enrolled 92 patients who underwent neoadjuvant chemoradiotherapy. Diffusion-weighted MRI sequences with two b-value combinations of b (0, 800) and b (0, 1000) were acquired before the start of neoadjuvant chemoradiotherapy and surgery. The pathological tumor regression grade was obtained according to the Mandard criteria, recommended by the seventh edition of the American Joint Committee on Cancer, to act as the reference standard. Pathological good responders (pathological tumor regression grade 1-2) were compared with poor responders (pathological tumor regression grade 3–5).

Results

The good responder group contained 37 (40.2%) patients and the poor responder group 55 (59.8%) patients. Both before and after neoadjuvant chemoradiotherapy, the mean ADC value for b = 1000 was significantly higher than that for b = 800. In the two patient groups, the post-ADC value and ΔADC for b = 800 were significantly lower than those for b = 1000, but percentages of ADC increase for b = 800 and b = 1000 showed no significant difference.

Conclusions

The percentage of ADC increase, as an optimized predictor unaffected by different b-values, may have a significant role in differentiating those patients with a good response to N-CRT from those with a poor response.