T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma

BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.

Myxofibrosarcoma of the scalp with difficult preoperative diagnosis:A case report and review of the literature

BACKGROUND A myxofibrosarcoma(MFS)is a malignant fibroblastic tumor that tends to occur in the lower and upper extremities.The reported incidence of head and neck MFSs is extremely rare.We report a 46-year-old male with“a neoplasm in the scalp”who was hospitalized and diagnosed with an MFS(highly malignant with massive necrotic lesions)based on histologic and immunohistochemistry evaluations.The magnetic resonance imaging manifestations did not demonstrate the“tail sign”mentioned in several studies,which resulted in a great challenge to establish an imaging diagnosis.The treatment plan is closely associated with the anatomic location and histologic grade,and more importantly,aggressive surgery and adjuvant radiotherapy may be helpful.Hence,we report the case and share some valuable information about the disease.CASE SUMMARY A 46-year-old male with“a neoplasm in the scalp for 6 mo”was hospitalized.Initially,the tumor was about the size of a soybean,without algesia or ulceration.The patient ignored the growth,did not seek treatment,and thus,did not receive treatment.Recently,the tumor increased to the size of an egg;there was no bleeding or algesia.His family history was unremarkable.No abnormalities were found upon laboratory testing,including routine hematologic,biochemistry,and tumor markers.Computed tomography showed an ovoid mass(6.25 cm×3.29 cm×3.09 cm in size)in the left frontal scalp with low density intermingled with equidense strips in adjacent areas of the scalp.Magnetic resonance imaging revealed a lesion with an irregular surface and an approximate size of 3.55 cm×6.34 cm in the left frontal region,with clear boundaries and visible separation.Adjacent areas of the skull were damaged and the dura mater was involved.Contrast enhancement showed an uneven enhancement pattern.Surgery was performed and postoperative adjuvant radiotherapy was administered to avoid recurrence or metastasis.The post-operative pathologic diagnosis confirmed an MFS.A repeat computed tomography scan showed no local recurrence or distant metastasis 19 mo post-operatively.CONCLUSION The case reported herein of MFS was demonstrated in an extremely rare location on the scalp and had atypical magnetic resonance imaging findings,which serves as a reminder to radiologists of the possibility of this diagnosis to assist in clinical treatment.Given the special anatomic location and the high malignant potential of this rare tumor,combined surgical and adjuvant radiotherapy should be considered to avoid local recurrence and distant metastasis.The significance of regular follow-up is strongly recommended to improve the long-term survival rate.