Magnetic Resonance Imaging Predictors Research Articles

Role of magnetic resonance imaging in prediction of outcome in traumatic diffuse axonal injury: A single-centre observational study

Introduction: Incidence of diffuse axonal injury has been estimated at 40-50% of hospitalizations. Recently, much interest has been directed towards the potential of newer imaging sequences of magnetic resonance imaging to investigate diffuse axonal injury (DAI) and to prognosticate the outcome. In this study, we correlated the magnetic resonance imaging grades of diffuse axonal injury with clinical outcome in terms of Glasgow Outcome Scale (GOS).
 Methods and Materials: A hospital based observational study was carried out at Upendra Devkota Memorial National Institute of Neurological and Allied Sciences, Kathmandu in 69 patients of diffuse axonal injury between November 2017 to November 2018. Data was collected on patient and trauma characteristics, as well as neurological assessment and MRI findings. Outcome was assessed as favourable and unfavourable GOS for various MRI grades of diffuse axonal injury.
 Results: There were 21.74%, 42.03% and 36.23% of cases with grade I, II and III diffuse axonal injury respectively. There were 0 (0%), 2 (11.8%) and 15 (88.2%) cases of MRI grade I, II and III diffuse axonal injury in favourable GOS group and 15 (28.8%), 27 (51.9%) and 10 (19.2%) cases of MRI grade I, II and III diffuse axonal injury in unfavourable GOS group (p=0.00).
 Conclusion: This study showed that there was a significantly higher chance of unfavourable outcome with increasing MRI grades of diffuse axonal injury.

Comparing data-driven and hypothesis-driven MRI-based predictors of cognitive impairment in individuals from the Atherosclerosis Risk in Communities (ARIC) study.

A data-driven index of dementia risk based on magnetic resonance imaging (MRI), the Alzheimer's Disease Pattern Similarity (AD-PS) score, was estimated for participants in the Atherosclerosis Risk in Communities (ARIC) study. AD-PS scores were generated for 839 cognitively non-impaired individuals with a mean follow-up of 4.86 years. The scores and a hypothesis-driven volumetric measure based on several brain regions susceptible to AD were compared as predictors of incident cognitive impairment in different settings. Logistic regression analyses suggest the data-driven AD-PS scores to be more predictive of incident cognitive impairment than its counterpart. Both biomarkers were more predictive of incident cognitive impairment in participants who were White, female, and apolipoprotein E gene (APOE) ε4 carriers. Random forest analyses including predictors from different domains ranked the AD-PS scores as the most relevant MRI predictor of cognitive impairment. Overall, the AD-PS scores were the stronger MRI-derived predictors of incident cognitive impairment in cognitively non-impaired individuals.

Characterisation of isocitrate dehydrogenase gene mutant WHO grade 2 and 3 gliomas: MRI predictors of 1p/19q co-deletion and tumour grade

To identify imaging predictors of molecular subtype and tumour grade in patients with isocitrate dehydrogenase (IDH) gene mutant (IDHmut) World Health Organization (WHO) grade 2 or 3 gliomas. Patients with histologically confirmed WHO grade 2 or 3 IDHmut gliomas between 2016 and 2019 were included in the study. Magnetic resonance imaging (MRI) images were evaluated for the presence or absence of potential imaging predictors of tumour subtype, such as T2/fluid attenuated inversion recovery (FLAIR) signal match, and these factors were examined using regression analysis. On perfusion imaging, the maximum relative cerebral blood volume (rCBVmax) was evaluated as a potential predictor of tumour grade. The performance of two experienced neuroradiologists in correctly predicting tumour type on MRI was evaluated. Eighty-five patients were included in the study. The presence of T2/FLAIR signal match >50% of tumour volume (p<0.01) and intratumoural susceptibility (p=0.02) were independent predictors of 1p/19q co-deletion. Mean rCBV max was significantly higher in WHO grade 3 astrocytomas (p=0.04) than WHO grade 2 astrocytomas. The consensus prediction of 1p/19q co-deletion status by two neuroradiologists of tumour was 95% sensitive and 86% specific. The presence of matched T2/FLAIR signal could be used to identify tumour subtype when biopsy is inconclusive or genetic analysis is unavailable. rCBVmax predicted astrocytoma grade. Experienced neuroradiologists predict tumour subtype with good sensitivity and specificity.

Predicting the surgical reparability of large-to-massive rotator cuff tears by B-mode ultrasonography: a cross-sectional study.

PurposeThis study aimed to compare the ability of B-mode ultrasonography and magnetic resonance imaging (MRI) to predict the repairability of large-to-massive rotator cuff tears (RCTs).MethodsThis cross-sectional study included participants with large-to-massive RCTs who underwent arthroscopic repair. B-mode ultrasonography and MRI were conducted prior to arthroscopic repair. B-mode ultrasonography was used to evaluate the echogenicity of the rotator cuff muscle using the Heckmatt scale. Intra-rater and inter-rater reliabilities were examined for two independent physicians. MRI was used to evaluate the degrees of tendon retraction, fatty infiltration of rotator cuff muscles, and muscle atrophy. Finally, two experienced orthopedic surgeons performed surgery and decided whether the torn stump could be completely repaired intraoperatively.ResultsFifty participants were included, and 32 complete repairs and 18 partial repairs were performed. B-mode ultrasonography showed good intra-rater reliability and inter-rater reliability for assessment of the muscle echogenicity of the supraspinatus and infraspinatus muscles. The correlation coefficients between B-mode ultrasound findings and MRI findings showed medium to large effect sizes (r=0.4-0.8). The Goutallier classification of the infraspinatus muscles was the MRI predictor with the best discriminative power for surgical reparability (area under the curve [AUC], 0.89; 95% confidence interval [CI], 0.81 to 0.98), while the Heckmatt scale for infraspinatus muscles was the most accurate ultrasound predictor (AUC, 0.85; 95% CI, 0.74 to 0.96). No significant differences in AUCs among the MRI and ultrasound predictors were found.ConclusionB-mode ultrasonography was a reliable examination tool and had a similar ability to predict surgical reparability to that of MRI among patients with large-to-massive RCTs.

Magnetic Resonance Imaging Predictors of Chondral Lesions in Patients With Femoroacetabular Impingement: An Analysis of 545 Cases

A large prospective cohort was used (1) to evaluate the overall ability of magnetic resonance imaging (MRI) to detect Outerbridge grade III and IV cartilage defects found during surgery and (2) to identify the specific MRI findings most associated with these cartilage defects so that the practicing hip arthroscopist can better predict cartilage injury before surgery. All patients undergoing hip arthroscopy between February 2015 and May 2017 at 1 institution were enrolled in a prospective cohort. Intra-articular findings were documented at the time of surgery. MRI reports were retrospectively reviewed for radiologist-reported articular cartilage, osseous, or synovial abnormalities. Sensitivity and specificity of MRI findings were calculated; multivariate logistic regression analysis determined which findings were associated with high-grade chondral damage at the time of arthroscopy and used to create an online risk calculator, https://orthop.washington.edu/hiprisk/. Of 598 patients who underwent hip arthroscopy, 550 had MRI reports available for review (92%). Grade III and IV cartilage injuries were reported on arthroscopy in 70 patients (13%) of average age 33 ± 13 years. On univariate analyses, individual MRI findings were not sensitive in detection of articular cartilage injury (mean 22%; range, 1.4%-46%), but positive findings were highly specific (mean 90%,; range, 76%-99%). Multivariate analysis revealed that older age (odds ratio [OR] 1.09 [1.06-1.11], P < .001) and osseous findings such as subchondral cyst or edema (OR 4.77 [2.51-9.05], P < .001) were most predictive of grade III and IV defects (P < .001). MRI was a specific but not sensitive tool in diagnosing articular cartilage injury. Surgeons should be aware that osseous findings such as cysts or edema are highly predictive of full-thickness cartilage loss in FAI. Level III, development of diagnostic criteria (consecutive patients with consistently applied reference standard, no blinding).

Intravoxel Incoherent Motion Magnetic Resonance Imaging for Prediction of Induction Chemotherapy Response in Locally Advanced Hypopharyngeal Carcinoma: Comparison With Model-Free Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

Multiparametric intravoxel incoherent motion (IVIM) provides diffusion and perfusion information for the treatment prediction of cancer. However, the superiority of IVIM over dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in locally advanced hypopharyngeal carcinoma (LAHC) remains unclear. To compare the diagnostic performance of IVIM and model-free DCE in assessing induction chemotherapy (IC) response in patients with LAHC. Prospective. Forty-two patients with LAHC. 3.0 T MRI, including IVIM (12 b values, 0-800 seconds/mm2 ) with a single-shot echo planar imaging sequence and DCE-MRI with a volumetric interpolated breath-hold examination sequence. IVIM MRI is a commercially available sequence and software for calculation and analysis from vendor. The IVIM-derived parameters (diffusion coefficient [D], pseudodiffusion coefficient [D*], and perfusion fraction [f]) and DCE-derived model-free parameters (Wash-in, time to maximum enhancement [Tmax], maximum enhancement [Emax], area under enhancement curve [AUC] over 60 seconds [AUC60 ], and whole area under enhancement curve [AUCw ]) were measured. At the end of IC, patients with complete or partial response were classified as responders according to the Response Evaluation Criteria in Solid Tumors. The differences of parameters between responders and nonresponders were assessed using Mann-Whitney U tests. The performance of parameters for predicting IC response was evaluated by the receiver operating characteristic curves. Twenty-three (54.8%) patients were classified as responders. Compared with nonresponders, the perfusion parameters D*, f, f × D*, and AUCw were significantly higher whereas Wash-in was lower in responders (all P-values <0.05). The f × D* outperformed other parameters, with an AUC of 0.84 (95% confidence interval [CI]: 0.69-0.93), sensitivity of 79.0% (95% CI: 54.4-93.9), and specificity of 82.6% (95% CI: 61.2-95.0). The IVIM MRI technique may noninvasively help predict the IC response before treatment in patients with LAHC. 2 TECHNICAL EFFICACY: Stage 2.

Role of magnetic resonance imaging in prediction of chondrosarcoma of bone grade

Purpose. Determining the diagnostic value of magnetic resonance imaging in the accurate definition of chondrosarcoma of bone grade at the pre-surgery examination. Material and methods. We analyzed examination data (magnetic resonance imaging with no contrast enhancement) of 70 patients with chondrosarcoma (35 patients with low-grade chondrosarcoma and 35 patients with high grade chondrosarcoma). Informative weighted coefficients were determined separately for ‘learning’ and ‘examination’ samples. On the basis of weighted coefficients, the decisive rule was created for differentiation between low-grade and high-grade chondrosarcoma. Results. The sensitivity of the method was 87.0%, specificity was 95.6%, total correct classification was 91.03%. Conclusion. Magnetic resonance imaging is a highly informative method for prediction of chondrosarcoma grade at the pre-surgery examination.

T2 -weighted magnetic resonance imaging texture as predictor of low back pain: A texture analysis-based classification pipeline to symptomatic and asymptomatic cases.

Low back pain is a very common symptom and the leading cause of disability throughout the world. Several degenerative imaging findings seen on magnetic resonance imaging are associated with low back pain but none of them is specific for the presence of low back pain as abnormal findings are prevalent among asymptomatic subjects as well. The purpose of this population-based study was to investigate if more specific magnetic resonance imaging predictors of low back pain could be found via texture analysis and machine learning. We used this methodology to classify T2 -weighted magnetic resonance images from the Northern Finland Birth Cohort 1966 data to symptomatic and asymptomatic groups. Lumbar spine magnetic resonance imaging was performed using a fast spin-echo sequence at 1.5 T. Texture analysis pipeline consisting of textural feature extraction, principal component analysis, and logistic regression classifier was applied to the data to classify them into symptomatic (clinically relevant pain with frequency ≥30 days and intensity ≥6/10) and asymptomatic (frequency ≤7 days, intensity ≤3/10, and no previous pain episodes in the follow-up period) groups. Best classification results were observed applying texture analysis to the two lowest intervertebral discs (L4-L5 and L5-S1), with accuracy of 83%, specificity of 83%, sensitivity of 82%, negative predictive value of 94%, precision of 56%, and receiver operating characteristic area-under-curve of 0.91. To conclude, textural features from T2 -weighted magnetic resonance images can be applied in low back pain classification.

Feasibility of Deep Learning Algorithms for Reporting in Routine Spine Magnetic Resonance Imaging.

Artificial intelligence is gaining traction in automated medical imaging analysis. Development of more accurate magnetic resonance imaging (MRI) predictors of successful clinical outcomes is necessary to better define indications for surgery, improve clinical outcomes with targeted minimally invasive and endoscopic procedures, and realize cost savings by avoiding more invasive spine care. To demonstrate the ability for deep learning neural network models to identify features in MRI DICOM datasets that represent varying intensities or severities of common spinal pathologies and injuries and to demonstrate the feasibility of generating automated verbal MRI reports comparable to those produced by reading radiologists. A 3-dimensional (3D) anatomical model of the lumbar spine was fitted to each of the patient's MRIs by a team of technicians. MRI T1, T2, sagittal, axial, and transverse reconstruction image series were used to train segmentation models by the intersection of the 3D model through these image sequences. Class definitions were extracted from the radiologist report for the central canal: (0) no disc bulge/protrusion/canal stenosis, (1) disc bulge without canal stenosis, (2) disc bulge resulting in canal stenosis, and (3) disc herniation/protrusion/extrusion resulting in canal stenosis. Both the left and right neural foramina were assessed with either (0) neural foraminal stenosis absent, or (1) neural foramina stenosis present. Reporting criteria for the pathologies at each disc level and, when available, the grading of severity were extracted, and a natural language processing model was used to generate a verbal and written report. These data were then used to train a set of very deep convolutional neural network models, optimizing for minimal binary cross-entropy for each classification. The initial prediction validation of the implemented deep learning algorithm was done on 20% of the dataset, which was not used for artificial intelligence training. Of the 17,800 total disc locations for which MRI images and radiology reports were available, 14,720 were used to train the model, and 3560 were used to validate against. The convergence of validation accuracy achieved with the deep learning algorithm for the foraminal stenosis detector was 81% (sensitivity = 72.4.4%, specificity = 83.1%) after 25 complete iterations through the entire training dataset (epoch). The accuracy was 86.2% (sensitivity = 91.1%, specificity = 82.5%) for the central stenosis detector and 85.2% (sensitivity = 81.8%, specificity = 87.4%) for the disc herniation detector. Deep learning algorithms may be used for routine reporting in spine MRI. There was a minimal disparity among accuracy, sensitivity, and specificity, indicating that the data were not overfitted to the training set. We concluded that variability in the training data tends to reduce overfitting and overtraining as the deep neural network models learn to focus on the common pathologies. Future studies should demonstrate the accuracy of deep neural network models and the predictive value of favorable clinical outcomes with intervention and surgery. 3. Feasibility, clinical teaching, and evaluation study.

Reliability Analysis of Deep Learning Algorithms for Reporting of Routine Lumbar MRI Scans.

Artificial intelligence could provide more accurate magnetic resonance imaging (MRI) predictors of successful clinical outcomes in targeted spine care. To analyze the level of agreement between lumbar MRI reports created by a deep learning neural network (RadBot) and the radiologists' MRI reading. The compressive pathology definitions were extracted from the radiologist lumbar MRI reports from 65 patients with a total of 383 levels for the central canal: (0) no disc bulge/protrusion/canal stenosis, (1) disc bulge without canal stenosis, (2) disc bulge resulting in canal stenosis, and (3) disc herniation/protrusion/extrusion resulting in canal stenosis. For both, neural foramina were assessed with either (0) neural foraminal stenosis absent or (1) neural foramina stenosis present. Reporting criteria for the pathologies at each disc level and, when available, the grading of severity were extracted, and the Natural Language Processing model was used to generate a verbal and written report. The RadBot report was analyzed similarly as the MRI report by the radiologist. MRI reports were investigated by dichotomizing the data into 2 categories: normal and stenosis. The quality of the RadBot test was assessed by determining its sensitivity, specificity, and positive and negative predictive value as well as its reliability with the calculation of the Cronbach alpha and Cohen kappa using the radiologist MRI report as a gold standard. The authors found a RadBot sensitivity of 73.3%, a specificity of 88.4%, a positive predictive value of 80.3%, and a negative predictive value of 83.7%. The reliability analysis revealed the Cronbach alpha as 0.772. The highest individual values of the Cronbach alpha were 0.629 and 0.681 when compared to the MRI report by the radiologist, rending values of 0.566 and 0.688, respectively. Analysis of interobserver reliability rendered an overall kappa for the RadBot of 0.627. Analysis of receiver operating characteristics (ROC) showed a value of 0.808 for the area under the ROC curve. Deep learning algorithms, when used for routine reporting in lumbar spine MRI, showed excellent quality as a diagnostic test that can distinguish the presence of neural element compression (stenosis) at a statistically significant level (P < .0001) from a random event distribution. This research should be extended to validated and directly visualized pain generators to improve the accuracy and prognostic value of the routine lumbar MRI scan for favorable clinical outcomes with intervention and surgery. 3. Validity, clinical teaching, and evaluation study.

Cervical spondylotic myelopathy: Association of MRI changes and outcome predictors after surgical intervention

Introduction: Cervical spondylotic myelopathy (CSM) is the most common cause of spinal cord dysfunction in older individuals. Controversy remains in terms of the optimal timing and indications for surgical intervention. In this context, it would be of benefit to define clinical and magnetic resonance imaging (MRI) predictors of outcome after intervention for CSM. Material and Methods: This was a prospective and observational study conducted in the Department of Radio diagnosis, Subbaiah Institute of Medical Sciences, Shimoga. All patients with functional disability secondary to cervical degenerative myelopathy and radiculopathy underwent surgery for decompression of the spinal cord with or without spinal stabilization from January 2020 to September 2020 were studied. A diagnosis of CSM required radiological confirmation (MRI) and at least one or more “upper motor neuron” findings (spasticity, hyper-reflexia, clonus and positive Babinski sign). Result: Among group A patients, 7 patients (77.7%) have improved postoperatively when compared according to Nurick grading. 9 patients (60%) have improved postoperatively. Among group C patients, only 2 patient (33.3%) have improved and majority (66.6%) remained in same grade as per Nurick grading. Conclusions: MR techniques play an indispensable role in the management of CSM patients and have evolved primarily from a diagnostic modality to a method that can potentially predict patient outcome following surgical intervention. Functional MRI can help to assess the neurological functional recovery after decompression surgery for CSM

Magnetic resonance imaging predictors of outcome after surgical intervention for cervical spondylotic myelopathy

Background: Cervical spondylotic myelopathy (CSM) is a commonly seen spinal cord disease. There are no well-defined indications and optimal timing for surgical intervention. Therefore, defining predictors for outcome after surgical intervention will have great advantage in taking decisions for interventions.Methods: A consecutive series of all patients having signs and symptoms of cervical spondylotic myelopathy admitted to Department of Neurosurgery, Medical College, Thiruvananthapuram who underwent decompressive surgery with or without stabilization in one year were studied. Pre-operative magnetic resonance imaging (MRI) findings were correlated with post-operative surgical outcomes (Nurick grade) after 3 months of follow up. The pattern of spinal cord signal intensity was classified as: group A (MRI N/N) - no SI T1WI or T2WI, group B (MRI N/Hi) - no SI T1WI and high SI on T2WI and, group C (MRI Lo/Hi) - low SI T1WI and high SI on T2WI. CSM clinical outcomes were evaluated using Nurick grading system, which was used pre- and post-operatively (pre op and post op).Results: Post operatively improvement was seen in 75% of group A and 61.35 % of group B patients, but among group C only 25% patient improved according to Nurick grading pre op and post op.Conclusions: Patients with high intramedullary signal intensity on T2WI may experience a good surgical outcome. A less favorable surgical outcome is predicted by the presence of low intramedullary signal on T1WI.

Clinical experience of robotic myomectomy for fertility preservation using preoperative magnetic resonance imaging predictor.

ObjectiveThis study aimed to demonstrate the use of preoperative magnetic resonance imaging (MRI) findings to select the optimal surgical technique between single-site (SS) and multi-site (MS) robotic myomectomy based on clinical experience, for the preservation of fertility.MethodsNinety-eight patients who underwent SS or MS robotic myomectomy using the da Vinci® Si system after undergoing MRI were evaluated retrospectively. The correlation between preoperative MRI findings and the intraoperative or postoperative findings during robotic myomectomy for the preservation of fertility was analyzed. The reproductive outcome was investigated when the patient wished to conceive.ResultsThe mean age of the patients was 35.68±5.04 years and 80 patients (81.6%) were nulliparous. The total diameter of myomas on MRI was 106.75±54.52 mm. The number of resected myomas was 4.31±4.39 (range, 1–27), and the total weight of resected myomas was 293.11±281.13 (range, 30–1,260) g. Myomas with high signal intensity on MRI required less time for resection. MS robotic myomectomy was performed for an increased number and total diameter of a myoma or a deep-seated myoma. Postoperatively, all patients resumed normal menstruation. Of the 15 patients who wished to conceive, 12 (80%) conceived successfully. Of these, uterine dehiscence occurred in 1 patient and 10 patients underwent an uneventful cesarean section.ConclusionSS or MS robotic myomectomy can be recommended for patients who wish to conserve fertility. However, the optimal surgical technique should be selected based on preoperative MRI findings to predict an effective surgical process and the successful preservation of fertility.

Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer

Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning. To develop a deep learning model using preoperative magnetic resonance imaging for prediction of lymph node metastasis in cervical cancer. This diagnostic study developed an end-to-end deep learning model to identify lymph node metastasis in cervical cancer using magnetic resonance imaging (MRI). A total of 894 patients with stage IB to IIB cervical cancer who underwent radical hysterectomy and pelvic lymphadenectomy were reviewed. All patients underwent radical hysterectomy and pelvic lymphadenectomy, received pelvic MRI within 2 weeks before the operations, had no concurrent cancers, and received no preoperative treatment. To achieve the optimal model, the diagnostic value of 3 MRI sequences was compared, and the outcomes in the intratumoral and peritumoral regions were explored. To mine tumor information from both image and clinicopathologic levels, a hybrid model was built and its prognostic value was assessed by Kaplan-Meier analysis. The deep learning model and hybrid model were developed on a primary cohort consisting of 338 patients (218 patients from Sun Yat-sen University Cancer Center, Guangzhou, China, between January 2011 and December 2017 and 120 patients from Henan Provincial People's Hospital, Zhengzhou, China, between December 2016 and June 2018). The models then were evaluated on an independent validation cohort consisting of 141 patients from Yunnan Cancer Hospital, Kunming, China, between January 2011 and December 2017. The primary diagnostic outcome was lymph node metastasis status, with the pathologic characteristics diagnosed by lymphadenectomy. The secondary primary clinical outcome was survival. The primary diagnostic outcome was assessed by receiver operating characteristic (area under the curve [AUC]) analysis; the primary clinical outcome was assessed by Kaplan-Meier survival analysis. A total of 479 patients (mean [SD] age, 49.1 [9.7] years) fulfilled the eligibility criteria and were enrolled in the primary (n = 338) and validation (n = 141) cohorts. A total of 71 patients (21.0%) in the primary cohort and 32 patients (22.7%) in the validation cohort had lymph node metastais confirmed by lymphadenectomy. Among the 3 image sequences, the deep learning model that used both intratumoral and peritumoral regions on contrast-enhanced T1-weighted imaging showed the best performance (AUC, 0.844; 95% CI, 0.780-0.907). These results were further improved in a hybrid model that combined tumor image information mined by deep learning model and MRI-reported lymph node status (AUC, 0.933; 95% CI, 0.887-0.979). Moreover, the hybrid model was significantly associated with disease-free survival from cervical cancer (hazard ratio, 4.59; 95% CI, 2.04-10.31; P < .001). The findings of this study suggest that deep learning can be used as a preoperative noninvasive tool to diagnose lymph node metastasis in cervical cancer.

Associations Between Initial Clinical Examination and Imaging Findings and Return-to-Sport in Male Athletes With Acute Adductor Injuries: A Prospective Cohort Study

Background: Time to return-to-sport (RTS) after acute adductor injuries varies among athletes, yet we know little about which factors determine this variance. Purpose: To investigate the association between initial clinical and imaging examination findings and time to RTS in male athletes with acute adductor injuries. Study Design: Cohort study (Prognosis); Level of evidence, 2. Methods: Male adult athletes with an acute adductor injury were included within 7 days of injury. Standardized patient history and clinical and magnetic resonance imaging (MRI) examinations were conducted for all athletes. Athletes performed a supervised standardized criteria-based exercise treatment program. Three RTS milestones were defined: (1) clinically pain-free, (2) completed controlled sports training, and (3) first full team training. Univariate and multiple regression analyses were performed to determine the association between the specific candidate variables of the initial examinations and the RTS milestones. Results: We included 81 male adult athletes. The median duration for the 3 RTS milestones were 15 days (interquartile range, 12-28 days), 24 days (16-32 days), and 22 days (15-31 days), respectively. Clinical examination including patient history was able to explain 63%, 74%, and 68% of the variance in time to RTS. The strongest predictors for longer time to RTS were pain on palpation of the proximal adductor longus insertion or a palpable defect. The addition of MRI increased the explained variance with 7%, 0%, and 7%. The strongest MRI predictor was injury at the bone-tendon junction. Post hoc multiple regression analyses of players without the 2 most important clinical findings were able to explain 24% to 31% of the variance, with no added value of the MRI findings. Conclusion: The strongest predictors of a longer time to RTS after acute adductor injury were palpation pain at the proximal adductor longus insertion, a palpable defect, and/or an injury at the bone-tendon junction on MRI. For athletes without any of these findings, even extensive clinical and MRI examination does not assist considerably in providing a more precise estimate of time to RTS.

Structural MRI outcomes and predictors of disease progression in amyotrophic lateral sclerosis

Background and aimsConsidering the great heterogeneity of amyotrophic lateral sclerosis (ALS), the identification of accurate prognostic predictors is fundamental for both the clinical practice and the design of treatment trials. This study aimed to explore the progression of clinical and structural brain changes in patients with ALS, and to assess magnetic resonance imaging (MRI) measures of brain damage as predictors of subsequent functional decline. Methods50 ALS patients underwent clinical evaluations and 3 T MRI scans at regular intervals for a maximum of 2 years (total MRI scans = 164). MRI measures of cortical thickness, as well as diffusion tensor (DT) metrics of microstructural damage along white matter (WM) tracts were obtained. Voxel-wise regression models and longitudinal mixed-effects models were used to test the relationship between clinical decline and baseline and longitudinal MRI features. ResultsThe rate of decline of the ALS Functional Rating Scale revised (ALSFRS-r) was significantly associated with the rate of fractional anisotropy (FA) decrease in the body of the corpus callosum (CC). Corticospinal tract (CST) and CC-body alterations had a faster progression in patients with higher baseline ALSFRS-r scores and greater CC-body disruption at baseline. Lower FA of the cerebral peduncle was associated with faster subsequent clinical progression. ConclusionsIn this longitudinal study, we identified a significant association between measures of WM damage of the motor tracts and functional decline in ALS patients. Our data suggest that a multiparametric approach including DT MRI measures of brain damage would provide an optimal method for an accurate stratification of ALS patients into prognostic classes.

Correlation Between Endorectal Ultrasound and Magnetic Resonance Imaging for Predicting the Circumferential Resection Margin in Patients With Mid-Low Rectal Cancer Without Preoperative Chemoradiotherapy.

To assess the correlation between endorectal ultrasound (ERUS) and magnetic resonance imaging (MRI) in predicting the circumferential resection margin (CRM) status of patients with mid-low rectal cancer without preoperative chemoradiotherapy. Twenty patients with rectal cancer who did not receive preoperative chemoradiotherapy and underwent ERUS and MRI examinations before total mesorectal excision from May 2018 to April 2019 were included in this study. The patient and tumor characteristics, lymph nodes, tumor stages, ERUS and MRI predictors of the CRM status, and postoperative pathologic results were recorded. The closest distance between the deepest portion of lesion invasion and the mesorectal fascia was independently measured on MRI and ERUS images by 2 observers. The observers were blinded to the pathologic results. Measurements from ERUS and MRI were compared. The mean distance between the distal edge of the lesion and the anal verge was 5.7 cm (range, 3.1-8.1 cm). The ERUS and pathologic evaluations of CRM involvement were consistent in 90% of the cases. The MRI and pathologic evaluations of CRM involvement were concordant in 95% of the cases. The Cohen κ coefficient of ERUS and MRI was 0.608 (P = .007). The correlation coefficient of ERUS and MRI for assessing the closest distance from the edge of cancer invasion to the mesorectal fascia was 0.99 (P = .0005). Endorectal ultrasound and MRI assessments of the preoperative CRM status appear to be highly consistent. Endorectal ultrasound can be used as a complementary tool with MRI to predict the CRM status of patients with mid-low rectal cancer without preoperative chemoradiotherapy.

Cognitive trajectories in relapsing–remitting multiple sclerosis: A longitudinal 6-year study

Background: Information concerning longitudinal cognitive trajectories in multiple sclerosis (MS) is relatively scarce. Moreover, it is unclear which factors are associated with cognitive decline and what is the clinical impact of cognitive impairment (CI) in the long run. Objective: To investigate cognitive trajectories in relapsing–remitting multiple sclerosis (RRMS) patients, analyzing clinical and magnetic resonance imaging (MRI) predictors of cognitive decline. Methods: We enrolled 42 patients and 30 controls. They underwent brain MRI and clinical/neuropsychological evaluation at baseline and after 1, 2, and 6 years. We evaluated cognitive domains with principal component analysis and performed multivariable regression analyzing predictors of clinical/cognitive deterioration. We also performed repeated measures analysis to assess whether clinical progression was different according to CI at baseline. Results: A total of 23 (62.2%) patients deteriorated in combined cognitive domains after 6 years, most in processing speed and memory. The number of baseline impaired cognitive domains was strongly associated with 6-year cognitive (R2 = 0.452; p < 0.001) and Expanded Disability Status Scale (EDSS) deterioration (R2 = 0.263; p < 0.001). Patients with baseline CI in combined domains had worse clinical progression. Conclusion: Isolated CI tends to become more widespread, affecting memory and processing speed alongside. The extent of baseline CI was the best predictor of both clinical and cognitive deterioration after 6 years.

Early assessment with magnetic resonance imaging for prediction of pathologic response to neoadjuvant chemotherapy in triple-negative breast cancer: Results from the phase III BrighTNess trial

IntroductionThe ability of breast magnetic resonance imaging (MRI) to predict pathologic complete response (pCR) to neoadjuvant systemic therapy (NST) varies across biological subtypes. We sought to determine how well breast MRI findings following initial treatment on the phase III BrighTNess trial correlated with pCR in patients with triple negative breast cancer (TNBC). MethodsBaseline and mid-treatment imaging and pathologic response data were available in 519 patients with stage II-III TNBC who underwent NST as per protocol. MRI complete response (mCR) was defined as disappearance of all target lesion(s) and MRI partial response (mPR) as a ≥50% reduction in the largest tumor diameter. ResultsOverall, mCR was demonstrated in 116 patients (22%), whereas 166 (32%) had mPR and 237 (46%) had stable/progressive disease (SD/PD). The positive predictive value (PPV), negative predictive value, and overall accuracy of the mid-treatment MRI for pCR were 78%, 56%, and 61%, respectively; accuracy did not differ significantly between gBRCA mutation carriers and non-carriers (52% vs. 63%, p = 0.10). When compared to patients with SD/PD, those with mPR or mCR were 3.35-fold (95% CI 2.07–5.41) more likely to have pCR at surgery. MRI response during NST was significantly associated with eligibility for breast-conserving surgery following completion of treatment (93.1% for mCR vs. 81.6% for SD/PD, p < 0.001). ConclusionsComplete response on mid-treatment MRI in the BrighTNess trial had a PPV of 78% for demonstration of pCR after completion of NST in TNBC. However, a substantial proportion of patients with mPR or SD/PD also achieved a pCR. Clinical trial registrationNCT02032277.

Role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography and magnetic resonance imaging in prediction of response to neoadjuvant chemotherapy in pediatric osteosarcoma.

The aim of our study was to evaluate the role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) in prediction of response to neoadjuvant chemotherapy (NAC) in pediatric osteosarcoma (OS) patients compared to percentage of tumor necrosis after surgical excision of the tumor. Forty-six pediatric OS patients treated with neoadjuvant chemotherapy and surgery were underwent PET/CT and MRI before, after 3 cycles, and after the completion of neoadjuvant chemotherapy. Imaging parameters include maximum standardized uptake value (SUVmax1, 2, and 3), tumor liver ratio (TLR 1, 2, and 3), and MRI tumor volume (MRTV 1, 2, and 3) at initial assessment before starting NAC, after finishing three cycles and after finishing 6 cycles before tumor excision, respectively. Cutoff values of the PET/CT and MRI parameters were determined using receiver operating characteristic (ROC) curve analysis and percentage of tumor necrosis of postsurgical specimen. Fourteen patients were good responders (30.4%), with more than 90% tumor necrosis, while 31 patients were poor responders (67.4%). The results of one patient were missed. We noticed that higher sensitivity for detecting poor responders was detected by SUVmax3/1, TLR3/1, and MRTV2/1 ratio cutoff values, while higher specificity was detected by TRL2 and SUVmax3 cutoff values. ROC curve analysis of MRTV2/1 and MRTV3/1 ratio was fair in predicting poor responders. PET/CT parameters are capable of predicting histological response to NAC in OS patients with overall sensitivity and specificity higher than MRI parameters.