Enhancing rectal cancer liver metastasis prediction:Magnetic resonance imaging-based radiomics,bias mitigation,and regulatory considerations

In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.

Metachronous metastasis of renal cell carcinoma to bilateral testis

Unusual site metastasis as a presenting complaint of renal cell carcinoma(RCC)has been reported previously in the literature.RCC is a tumor with notoriously unpredictable behavior.The authors report an unusual case of metachronous bilateral testicular metastasis in a patient who operated for RCC.The case highlights the unique behavior of RCC with an unusual site of metastasis.A 72-year-old patient presented with bilateral scrotal swelling of 1-month duration.There was a history of left radical nephrectomy for RCC 4 years prior.He underwent a bilateral high inguinal orchidectomy and diagnosis of chromophobe RCC was made on histopathological examination.

Development and validation of a nomogram for predicting metachronous peritoneal metastasis in colorectal cancer:A retrospective study

BACKGROUND Peritoneal metastasis(PM)after primary surgery for colorectal cancer(CRC)has the worst prognosis.Prediction and early detection of metachronous PM(m-PM)have an important role in improving postoperative prognosis of CRC.However,commonly used imaging methods have limited sensitivity to detect PM early.We aimed to establish a nomogram model to evaluate the individual probability of m-PM to facilitate early interventions for high-risk patients.AIM To establish and validate a nomogram model for predicting the occurrence of m-PM in CRC within 3 years after surgery.METHODS We used the clinical data of 878 patients at the Second Hospital of Jilin University,between January 1,2014 and January 31,2019.The patients were randomly divided into training and validation cohorts at a ratio of 2:1.The least absolute shrinkage and selection operator(LASSO)regression was performed to identify the variables with nonzero coefficients to predict the risk of m-PM.Multivariate logistic regression was used to verify the selected variables and to develop the predictive nomogram model.Harrell’s concordance index,receiver operating characteristic curve,Brier score,and decision curve analysis(DCA)were used to evaluate discrimination,distinctiveness,validity,and clinical utility of this nomogram model.The model was verified internally using bootstrapping method and verified externally using validation cohort.RESULTS LASSO regression analysis identified six potential risk factors with nonzero coefficients.Multivariate logistic regression confirmed the risk factors to be independent.Based on the results of two regression analyses,a nomogram model was established.The nomogram included six predictors:Tumor site,histological type,pathological T stage,carbohydrate antigen 125,v-raf murine sarcoma viral oncogene homolog B mutation and microsatellite instability status.The model achieved good predictive accuracy on both the training and validation datasets.The C-index,area under the curve,and Brier scores were 0.796,0.796[95%confidence interval(CI)0.735-0.856],and 0.081 for the training cohort and 0.782,0.782(95%CI 0.690-0.874),and 0.089 for the validation cohort,respectively.DCA showed that when the threshold probability was between 0.01 and 0.90,using this model to predict m-PM achieved a net clinical benefit.CONCLUSION We have established and validated a nomogram model to predict m-PM in patients undergoing curative surgery,which shows good discrimination and high accuracy.

An extremely rare case of pancreatic metastasis of esophageal squamous cell carcinoma

We report a rare case of a 68-year-old male with metachronous pancreatic metastasis that was resected2 years after salvage esophagectomy for local recurrence of esophageal squamous cell carcinoma(ESCC).Two years and 8 mo ago,he had undergone definitive chemoradiotherapy for the lower thoracic ESCC and achieved a complete response.Chemoradiotherapy used the protocol of the Japan Clinical Oncology Group trial 9906.Approximately 8 mo later,he developed a local recurrence of the ESCC and underwent thoracoscopic salvage esophagectomy followed by reconstruction with a conduit colon graft via a subcutaneous route.Recently,a tumor of the pancreatic body was found on routine follow-up computed tomography(CT).The tumor diameter was 15 mm on CT,and the maximum standardized uptake value of the lesion was 5.49at 18F-2-fluoro-2-deoxy-D-glucose positron-emission tomography,strongly suggesting pancreatic cancer.In addition,all tumor markers were within the reference intervals.Therefore,distal pancreatectomy was performed with the resultant histological diagnosis being confirmed as pancreatic metastasis of the ESCC.He was treated with adjuvant chemotherapy,and there has been no evidence of recurrence 9 mo after the surgery.Resection of pancreatic metastasis offers a good prognosis and should be considered for solitary ESCC metastasis.

Metachronous bladder metastasis from papillary renal cell carcinoma

Renal cell carcinoma(RCC)is well known for its metastatic potential and predilection for unusual sites of metastasis.Metastasis to the bladder is rare and has been reported predominantly from clear cell RCC.We report a case of a 72-year-old male presenting with a bladder tumor which on histopathological evaluation was found to be a metastasis from papillary RCC,7 years after radical nephrectomy.This case highlights the need to maintain a high index of suspicion to diagnose bladder metastasis in a patient with a history of RCC presenting with a bladder lesion.