Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer.

Objective: To compare computed tomography (CT)- and magnetic resonance imaging (MRI)-based multiparametric radiomics models and validate a multi-modality, multiparametric clinical-radiomics nomogram for individual preoperative prediction of lymph node metastasis (LNM) in rectal cancer (RC) patients. Methods: 234 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 164) and testing (n = 70) cohorts. The radiomics features of the primary tumor were extracted from the non-contrast enhanced computed tomography (NCE-CT), the enhanced computed tomography (CE-CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. Three kinds of models were constructed based on training cohort, including the Clinical model (based on the clinical features), the radiomics models (based on NCE-CT, CE-CT, T2WI, CE-T1WI, CT, MRI, CT combing with MRI) and the clinical-radiomics models (based on CT or MRI radiomics model combing with clinical data) and Clinical-IMG model (based on CT and MRI radiomics model combing with clinical data). The performances of the 11 models were evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the training and validation cohort. Differences in the AUCs among the 11 models were compared using DeLong's test. Finally, the optimal model (Clinical-IMG model) was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis (DCA). Results: The MRI radiomics model in the validation cohort significantly outperformed than CT radiomics model (AUC, 0.785 vs. 0.721, p<0.05). The Clinical-IMG nomogram had the highest prediction efficiency than all other predictive models (p<0.05), of which the AUC was 0.947, the sensitivity was 0.870 and the specificity was 0.884. Conclusion: MRI radiomics model performed better than both CT radiomics model and Clinical model in predicting LNM of RC. The clinical-radiomics nomogram that combines the radiomics features obtained from both CT and MRI along with preoperative clinical characteristics exhibits the best diagnostic performance.

Comprehensive next-generation sequencing reveals double primary colorectal carcinoma missed by diagnostic imaging: A case report.

BACKGROUND: Multiple primary colorectal carcinoma (MPCC) is a rare clinical disease, which is challenging to differentiate from metastatic disease using histopathological methods. Next-generation sequencing (NGS) has been employed to identify multiple primary cancers. CASE SUMMARY: This study a rare case of a 63-year-old male patient diagnosed with MPCC by targeted NGS, which was initially missed by radiological evaluation. The patient was found to have two tumors located on the surface of the colorectum which had distinct genomic alterations. Based on wild-type KRAS detected in the unresected tumor, the patient benefited from the epidermal growth factor receptor (EGFR) inhibitor cetuximab treatment, but developed novel mutations including KIF5B-RET fusion, which provides a possible resistance mechanism to anti-EGFR therapy. CONCLUSION: Our case highlights the necessity of using genetic testing for primary tumor diagnosis and the application of serial plasma circulating tumor DNA profiling for dynamic disease monitoring.

Predicting pathological response to neoadjuvant or conversion chemoimmunotherapy in stage IB-III non-small cell lung cancer patients using radiomic features.

BACKGROUND: To develop a radiomics model based on chest computed tomography (CT) for the prediction of a pathological complete response (pCR) after neoadjuvant or conversion chemoimmunotherapy (CIT) in patients with non-small cell lung cancer (NSCLC). METHODS: Patients with stage IB-III NSCLC who received neoadjuvant or conversion CIT between September 2019 and July 2021 at Hunan Cancer Hospital, Xiangya Hospital, and Union Hospital were retrospectively collected. The least absolute shrinkage and selection operator (LASSO) were used to screen features. Then, model 1 (five radiomics features before CIT), model 2 (four radiomics features after CIT and before surgery) and model 3 were constructed for the prediction of pCR. Model 3 included all nine features of model 1 and 2 and was later named the neoadjuvant chemoimmunotherapy-related pathological response prediction model (NACIP). RESULTS: This study included 110 patients: 77 in the training set and 33 in the validation set. Thirty-nine (35.5%) patients achieved a pCR. Model 1 showed area under the curve (AUC) = 0.65, 64% accuracy, 71% specificity, and 50% sensitivity, while model 2 displayed AUC = 0.81, 73% accuracy, 62% specificity, and 92% sensitivity. In comparison, NACIP yielded a good predictive value, with an AUC of 0.85, 81% accuracy, 81% specificity, and 83% sensitivity in the validation set. CONCLUSION: NACIP may be a potential model for the early prediction of pCR in patients with NSCLC treated with neoadjuvant/conversion CIT.

Eschar Dermabrasion in Deep Dermal Partial-Thickness Burn: A Case Report.

ABSTRACT: Although dermabrasion is widely used to treat various skin diseases and for scar repair, relatively few reports have described its use with burn wounds. As a blunt debridement, eschar dermabrasion has unique advantages. For patients with deep burns, the boundary between active tissue and inactive tissue is unclear. With eschar dermabrasion, necrotic tissue can be removed to the greatest extent with minimal damage. Early use can help patients skip the scab-dissolving period, decrease local and systemic inflammation, reduce postoperative scarring, and significantly reduce the difficulty of early wound care. As a result, the patient's hospitalization costs and pain during treatment are both reduced, and thanks to less scarring, the patient is more likely to engage in social activities and has an improved quality of life.

Case Report: Lung adenocarcinoma associated with germline ERCC2 frameshift mutation.

Family history is an established risk factor for lung cancer. Previous studies have found that germline genetic alterations, such as those in EGFR, BRCA1, BRCA2, CHEK2, CDKN2A, HER2, MET, NBN, PARK2, RET, TERT, TP53, and YAP1, are associated with an increased risk of developing lung cancer. The study reports the first of a lung adenocarcinoma proband with germline ERCC2 frameshift mutation c.1849dup (p. A617Gfs*32). Her family cancer history review demonstrated that her two healthy sisters, a brother with lung cancer, and three healthy cousins were positive for ERCC2 frameshift mutation, which might contribute to increased cancer risk. Our study highlights the necessity of performing comprehensive genomic profiling in discovering rare genetic alterations, early cancer screening, and monitoring for patients with family cancer history.

MRI-Based Radiomic Models Outperform Radiologists in Predicting Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

RATIONALE AND OBJECTIVES: The 15%-27% of patients with locally advanced rectal cancer (LARC) achieved pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) and could avoid proctectomy. We aimed to investigate the effectiveness of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for LARC patients treated with nCRT and to compare these radiomic models with radiologists' visual assessment. MATERIALS AND METHODS: A total of 126 patients with LARC who received nCRT before surgery were included and randomly divided into a training set (n = 84) and a validation set (n = 42). 250 radiomic features were extracted from T2-weighted images from pre- and post-nCRT MRI. Pearson correlation analysis and AONVA or Relief were used to identify radiomic descriptors associated with pCR. Five machine-learning classifiers were compared to construct radiomic models. The radiomic nomogram was built via multivariate logistic regression analysis. Two senior radiologists independently rated tumor regression grades and compared with radiomic models. Area under the curve (AUC) of the models and pooled observers were compared by using the DeLong test. RESULTS: The optimal pre-, post-, and delta-radiomic models yielded an AUC of 0.717 (95% CI: 0.639-0.795), 0.805 (95%CI: 0.736-0.874), and 0.724 (95%CI: 0.648-0.800), respectively. The radiomic nomogram based on pre-nCRT cN stage, pre-nCRT radscore, and post-nCRT radscore achieved an AUC of 0.852 (95%CI: 0.774-0.930), which was higher than the single radiomic models and pooled readers (all p < 0.05). CONCLUSIONS: The radiomic nomogram is an effective and invasive tool to predict pCR in LARC patients after nCRT, which outperforms radiologists.

Prediction of therapeutic response of unresectable hepatocellular carcinoma to hepatic arterial infusion chemotherapy based on pretherapeutic MRI radiomics and Albumin-Bilirubin score.

PURPOSE: To construct and validate a combined nomogram model based on magnetic resonance imaging (MRI) radiomics and Albumin-Bilirubin (ALBI) score to predict therapeutic response in unresectable hepatocellular carcinoma (HCC) patients treated with hepatic arterial infusion chemotherapy (HAIC). METHODS: The retrospective study was conducted on 112 unresectable HCC patients who underwent pretherapeutic MRI examinations. Patients were randomly divided into training (n = 79) and validation cohorts (n = 33). A total of 396 radiomics features were extracted from the volume of interest of the primary lesion by the Artificial Kit software. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify optimal radiomic features. After feature selection, three models, including the clinical, radiomics, and combined models, were developed to predict the non-response of unresectable HCC to HAIC treatment. The performance of these models was evaluated by the receiver operating characteristic curve. According to the most efficient model, a nomogram was established, and the performance of which was also assessed by calibration curve and decision curve analysis. Kaplan-Meier curve and log-rank test were performed to evaluate the Progression-free survival (PFS). RESULTS: Using the LASSO regression, we ultimately selected three radiomics features from T2-weighted images to construct the radiomics score (Radscore). Only the ALBI score was an independent factor associated with non-response in the clinical model (P = 0.033). The combined model, which included the ALBI score and Radscore, achieved better performance in the prediction of non-response, with an AUC of 0.79 (95% CI 0.68-0.90) and 0.75 (95% CI 0.58-0.92) in the training and validation cohorts, respectively. The nomogram based on the combined model also had good discrimination and calibration (P = 0.519 for the training cohort and P = 0.389 for the validation cohort). The Kaplan-Meier analysis also demonstrate that the high-score patients had significantly shorter PFS than the low-score patients (P = 0.031) in the combined model, with median PFS 6.0 vs 9.0 months. CONCLUSION: The nomogram based on the combined model consisting of MRI radiomics and ALBI score could be used as a biomarker to predict the therapeutic response of unresectable HCC after HAIC.

Comprehensive next-generation sequencing reveals low-grade fibromyxoid sarcoma of the vulva missed by morphological diagnosis: a case report.

Low-grade fibromyxoid sarcoma (LGFMS) is a rare soft tissue tumor composed of bland spindled cells in a variably fibrous to myxoid stroma. Its occurrence in the vulva region is rare, and thus, it may not be always taken into account in the differential diagnosis. Here, we describe a 34-year-old woman presented with a right vulvar mass and underwent complete surgical excision. The final pathologic diagnosis revealed LGFMS of the vulva based on the morphological, immunophenotypic, and molecular genetic features. The patient has not experienced a local or metastatic recurrence after 9-month follow-up. Despite being rare, LGFMS of the vulva should be considered when making a diagnosis of vulvar lesions. We also report that the genetic testing by next-generation sequencing (NGS) represents a very useful tool for the differential diagnosis of LGFMS from its mimics. Moreover, we have reviewed the literature on LGFMS of the vulva and summarized the characteristics of the patients, providing assistance for the diagnosis of such patients. Most vulvovaginal LGFMS can be fully removed through surgery. However, ongoing monitoring over the long term is essential as local and/or distant spread can occur decades after the initial diagnosis.

ZNF582 promoter methylation predicts cervical cancer radiosensitivity and ZNF582 protein overexpression reduces radiosensitivity by cell cycle arrest in S phase.

This study aimed to investigate the relationship between ZNF582 promoter methylation (ZNF582m) level and radiosensitivity of cervical cancer and its biological basis. This was a prospective multicenter clinical study, comprising two independent cohorts of locally advanced cervical cancer patients. Exfoliated cervical cells were collected at 0, 24, 30, 36, 48, and 64 Gy to test ZNF582m levels. Radiotherapy response was evaluated according to RECIST Version. RT-PCR and WT were used to detect the mRNA and protein expression levels; MTT and flow cytometry were used to detect the cell viability and cell cycle, respectively. While clone formation and subcutaneous tumorigenesis in nude mice were used to detect the growth of HeLa cells with/without ZNF582 overexpression. In the first cohort, 22 cases achieved complete remission (CR) or partial response (PR), and the other 28 cases exhibited stable disease (SD). Radiotherapy reduced ZNF582m levels among all patients. Initial lever of ZNF582m was significantly higher in the Responder (CR + PR) group than in the SD group. Also, patients with higher initial lever ZNF582m were more sensitive towards radiotherapy than ZNF582m-low patients. The second cohort confirmed the above results. The amplitude of ZNF582m levels were related to the radiotherapeutic response; some patients of ZNF582m-low showed a transient increase in ZNF582m, and present greater radiosensitivity than other ZNF582m-low patients. In vitro, ZNF582 protein overexpression promoted cell cycle arrest in S phase. These results suggested that higher ZNF582m levels predicted greater radiosensitivity in clinical cervical cancer cases. Overexpressed ZNF582 conferred radioresistance by cell cycle arrest in vitro.

Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer.

OBJECTIVE: To establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning. METHODS: The clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristic (ROC) curve analysis after dimensionality reduction. Three kinds of models were constructed based on training set, including the clinical model (the optimal radiomics signature combining with the clinical features), the magnetic resonance imaging model (the optimal radiomics signature combining with the mrEMVI status) and the integrated model (the optimal radiomics signature combining with both the clinical features and the mrEMVI status). Finally, the optimal model was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis curves. RESULTS: The radiomics signature constructed based on T2WI showed the best performance, with an AUC value of 0.717, a sensitivity of 0.742 and a specificity of 0.621. The radiomics nomogram had the highest prediction efficiency, of which the AUC was 0.863, the sensitivity was 0.774 and the specificity was 0.801. CONCLUSION: The radiomics nomogram had the highest efficiency in predicting EMVI. This may help patients choose the best treatment strategy and may strengthen personalized treatment methods to further optimize the treatment effect.

Predicting the pathological response to chemoradiotherapy of non-mucinous rectal cancer using pretreatment texture features based on intravoxel incoherent motion diffusion-weighted imaging.

OBJECTIVES: To investigate the performance of the mean parametric values and texture features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) on identifying pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). METHODS: Pretreatment IVIM-DWI was performed on 41 LARC patients receiving nCRT in this prospective study. The values of IVIM-DWI parameters (apparent diffusion coefficient, ADC; pure diffusion coefficient, D; pseudo-diffusion coefficient, D* and perfusion fraction, f), the first-order, and gray-level co-occurrence matrix (GLCM) texture features were compared between the pCR (n = 9) and non-pathological responder (non-pCR, n = 32) groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine the efficiency for identifying pCR. RESULTS: The values of IVIM-DWI parameters and first-order texture features did not show significant differences between the pCR and non-pCR groups. The pCR group had lower Contrast and DifVarnc values extracted from the ADC, D, and D* maps, respectively, as well as lower CorrelatD value. Higher CorrelatD*, Correlatf, SumAvergADC, and SumAvergD values were observed in the pCR group. The area under the ROC curve (AUC) values for the individual predictors in univariate analysis ranged from 0.698 to 0.837, with sensitivities from 43.75% to 87.50% and specificities from 66.67 to 100.00%. In multivariate analysis, CorrelatD* (P < 0.001), DifVarncADC (P = 0.024), and DifVarncD (P < 0.001) were the independent predictors to pCR, with an AUC of 0.986, a sensitivity of 93.75%, and a specificity of 100.00%. CONCLUSION: Pretreatment GLCM analysis based on IVIM-DWI may be a potential approach to identify the pathological response of LARC.

Prognostic Value of the Pretreatment Primary Lesion Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma.

RATIONALE AND OBJECTIVES: Early identifying the long-term outcome of chemoradiotherapy is helpful for personalized treatment in nasopharyngeal carcinoma (NPC). This study aimed to investigate the prognostic significance of pretreatment quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for NPC. MATERIALS AND METHODS: The relationships between the prognosis and pretreatment quantitative DCE-MRI (Ktrans, Kep, Ve, and fpv) values of the primary tumors were analyzed in 134 NPC patients who received chemoradiotherapy. Kaplan-Meier analysis was performed to calculate the local-regional relapse-free survival (LRRFS), local relapse-free survival (LRFS), regional relapse-free survival, distant metastasis-free survival (DMFS), progression-free survival, and overall survival rates. Cox proportional hazards model was used to explore the independent predictors for prognosis. RESULTS: The local-failure group had significantly higher Ve (p = 0.033) and fpv values (p = 0.005) than the non-local-failure group. The Ve-high group showed significantly lower LRRFS (p = 0.015) , LRFS (p = 0.013) , DMFS (p = 0.027) and progression-free survival (p = 0.035) rates than the Ve-low group. The fpv-high group exhibited significantly lower LRRFS (p = 0.004) and LRFS (p = 0.005) rates than the fpv-low group. Ve was the independent predictor for LRRFS (p = 0.008), LRFS (p = 0.007), DMFS (p = 0.041), and overall survival (p = 0.022). fpv was the independent indicator for LRRFS (p = 0.003) and LRFS (p = 0.001). CONCLUSION: Baseline quantitative DCE-MRI may be valuable in predicting the prognosis for NPC.

Predicting chemoradiotherapy response of nasopharyngeal carcinoma using texture features based on intravoxel incoherent motion diffusion-weighted imaging.

The aim of the study was to investigative the utility of gray-level co-occurrence matrix (GLCM) texture analysis based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the early response to chemoradiotherapy for nasopharyngeal carcinoma (NPC).Baseline IVIM-DWI was performed on 81 patients with NPC receiving chemoradiotherapy in a prospective nested case-control study. The patients were categorized into the residue (n = 11) and nonresidue (n = 70) groups, according to whether there was local residual lesion or not at the end of chemoradiotherapy. The pretreatment tumor volume and the values of IVIM-DWI parameters (apparent diffusion coefficient [ADC], D, D, and f) and GLCM features based on IVIM-DWI were compared between the 2 groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine significant indicator of treatment response.The nonresidue group had lower tumor volume, ADC, D, CorrelatADC, CorrelatD, InvDfMomADC, InvDfMomD and InvDfMomD values, together with higher ContrastD, Contrastf, SumAvergADC, SumAvergD, and SumAvergD values, than the residue group (all P < .05). Based on ROC curve in univariate analysis, the area under the curve (AUC) values for individual GLCM features in the prediction of the treatment response ranged from 0.635 to 0.879, with sensitivities from 54.55% to 100.00% and specificities from 52.86% to 85.71%. Multivariate logistic regression analysis demonstrated D (P = .026), InvDfMomADC (P = .033) and SumAvergD (P = .015) as the independent predictors for identifying NPC without residue, with an AUC value of 0.977, a sensitivity of 90.91% and a specificity of 95.71%.Pretreatment GLCM features based on IVIM-DWI, especially on the diffusion-related maps, may have the potential to predict the early response to chemoradiotherapy for NPC.