Pretreatment MRI Research Articles

MRI Radiomic Habitats Predict for Radiation Response in Soft Tissue Sarcoma

Favorable tumor response (pathologic necrosis [PN] >90-95%) has been associated with improved outcome in soft tissue sarcoma (STS). STS is a diverse disease with variable response to radiation (RT), therefore tools such as radiomics may improve our prediction of tumor radiosensitivity. Radiomic habitats are analyses of overlaid images, providing insight on the underlying innate biology. We hypothesize that utilizing pretreatment MRI habitat analysis can predict for favorable tumor response in STS. We identified a total of 432 non-metastatic sarcoma patients that were treated with neoadjuvant radiation (50Gy/25 fractions) followed by surgery, from 2001 to 2019. Patients with a pre-RT MRI were included, gross volume was manually segmented on T1-post contrast and T2 STIR sequences. The sequences were dichotomized by signal intensity and each region designated into 1 of 4 habitats (e.g. T1 high/low, T2 high/low), and 154 habitat features were extracted. The cohort was split 2:1 into training and validation subsets balanced by PN, clinical tumor T (cT) and N stage (cN), receiving induction chemotherapy, and age. Feature reduction and machine learning was utilized to identify habitat features predictive of PN rates >90% or >95% at the time of surgery. Overall, patients eligible for analysis (n = 97) had a median age of 63 and tumor size of 11 cm. They most commonly consisted of undifferentiated pleomorphic sarcoma (40%), extremity (80%), and no prior chemotherapy (87%). An 8-feature model, 4 radiomic and 4 clinical (recurrent disease, age, cT stage, cN stage), predicted for PN>90% (training: 72%, ROC 0.746; validation: 65%, ROC: 0.713). Evaluating patients treated with neoadjuvant RT alone (n = 84), a second model with 5 radiomic and 3 clinical features (prior RT, cT and cN stage), had the highest prediction for PN>95% (training: 78%, ROC 0.721; validation: 72%, ROC 0.600). In both the overall cohort and RT only subset, the diversity in the number of habitat volumes within the tumor predicted for tumor response (OR 7.71 and 3.31). Models incorporating pretreatment MRI-habitats and clinical features can be used to predict radiation response in STS. Radiomic features suggestive of habitat diversity within a tumor appear to be associated with radiation response, and highlight the importance of intratumoral heterogeneity when evaluating radiosensitivity. Prospective studies utilizing this model to predict radiation response may identify patients that could benefit from treatment escalation.

The Prognostic Impact of Delayed Time From Biopsy in Men with Low Risk Prostate Cancer Treated with Definitive SBRT

Active Surveillance (AS) and Stereotactic Body Radiation Therapy (SBRT) are validated treatment approaches for men with low risk prostate cancer. Diagnostic MRI imaging has become widely utilized and at times is used in place of subsequent biopsy. For men undergoing prostate SBRT upfront or after a course of AS, the optimal timing of treatment from last biopsy and its prognostic impact remain elusive. This study attempts to clarify the optimal scheduling of prostate SBRT from the time of biopsy in men with a diagnosis of low risk disease. A total of 684 patients with NCCN low risk prostate cancer were treated with robotic, non-coplanar SBRT at an academic institution from April 10, 2006 to November 18, 2019. The mean age was 65.6 years (41-89). The average pre-treatment PSA was 5.59ng/ml (0.3-9.9). 57 (7.9%) patients were formally placed on Active Surveillance prior to treatment. 78.8% of patients were treated with a dose of 3500cGy (3500-3625) over 5 fractions. 5.6% of patients were treated with ADT. The mean follow-up period was 39.8 months (9-166). Disease Free Survival (DFS) was scored using the Phoenix definition. Cross tabulations were tested by Pearson chi-square analysis. Survival curves were calculated by the Kaplan-Meier method and multivariate analysis was assessed by the log rank test using Cox regression. 88 (12.9%) patients were treated over 12 months (range 1-58) from the time of their biopsy. These patients were more likely to have been on Active Surveillance (29.1% vs. 5.1%, p<.001) prior to treatment and were equally likely to undergo pre-treatment MRI (97.4% vs. 95.3% p = NS). Patient age, ethnicity, initial PSA, prescribed dose, or ADT use did not differ based on time from biopsy. Patients treated within 12 months of biopsy had a significantly higher 7-year DFS (92.9% vs. 76.5%, p = .014). On subgroup analysis, there was further benefit with treatment within 6 months of biopsy, with 7-year DFS of 100%, 94%, and 76.5% (p = .011) for patients treated 0-6, 6-12, and 12+ months from biopsy, respectively. On multivariate analysis, treatment within 12 months of biopsy (OR 9.04, CI 1.80-45.33, p = .007) and lower PSA (OR 1.76, CI 1.15-2.71, p = .01) were lone predictors of long-term DFS. To our knowledge this is the only study to assess the effect of protracted time from biopsy on DFS in men with low risk prostate cancer treated with prostate SBRT. While DFS for the overall cohort is favorable, there is a considerable decline in biochemical outcome with a delay of >12 months from biopsy. This might suggest an occult progression prior to treatment. This study provides guidance regarding the prognostic impact of a delay in treatment from last biopsy in patients receiving prostate SBRT.

Sexually dimorphic radiogenomic models identify distinct imaging and biological pathways that are prognostic of overall survival in glioblastoma.

Recent epidemiological studies have suggested that sexual dimorphism influences treatment response and prognostic outcome in glioblastoma (GBM). To this end, we sought to (i) identify distinct sex-specific radiomic phenotypes-from tumor subcompartments (peritumoral edema, enhancing tumor, and necrotic core) using pretreatment MRI scans-that are prognostic of overall survival (OS) in GBMs, and (ii) investigate radiogenomic associations of the MRI-based phenotypes with corresponding transcriptomic data, to identify the signaling pathways that drive sex-specific tumor biology and treatment response in GBM. In a retrospective setting, 313 GBM patients (male = 196, female = 117) were curated from multiple institutions for radiomic analysis, where 130 were used for training and independently validated on a cohort of 183 patients. For the radiogenomic analysis, 147 GBM patients (male = 94, female = 53) were used, with 125 patients in training and 22 cases for independent validation. Cox regression models of radiomic features from gadolinium T1-weighted MRI allowed for developing more precise prognostic models, when trained separately on male and female cohorts. Our radiogenomic analysis revealed higher expression of Laws energy features that capture spots and ripple-like patterns (representative of increased heterogeneity) from the enhancing tumor region, as well as aggressive biological processes of cell adhesion and angiogenesis to be more enriched in the "high-risk" group of poor OS in the male population. In contrast, higher expressions of Laws energy features (which detect levels and edges) from the necrotic core with significant involvement of immune related signaling pathways was observed in the "low-risk" group of the female population. Sexually dimorphic radiogenomic models could help risk-stratify GBM patients for personalized treatment decisions.

Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data.

.Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations.Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap).Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively (). The DL method achieved a mean Dice coefficient of 0.87 on the test cases.Conclusions: This was the first objective comparison of automated and human segmentation using a blinded controlled assessment study. Our DL system learned to outperform its “human teachers” and produced output that was better, on average, than its training data.

A Radiomics Model for Predicting the Response to Bevacizumab in Brain Necrosis after Radiotherapy.

Bevacizumab is considered a promising therapy for brain necrosis after radiotherapy, while some patients fail to derive benefit or even worsen. Hence, we developed and validated a radiomics model for predicting the response to bevacizumab in patients with brain necrosis after radiotherapy. A total of 149 patients (with 194 brain lesions; 101, 51, and 42 in the training, internal, and external validation sets, respectively) receiving bevacizumab were enrolled. In total, 1,301 radiomic features were extracted from the pretreatment MRI images of each lesion. In the training set, a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm. Multivariable logistic regression analysis was then used to develop a radiomics model incorporated in the radiomics signature and independent clinical predictors. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness with internal and external validation. The radiomics signature consisted of 18 selected features and showed good discrimination performance. The model, which integrates the radiomics signature, the interval between radiotherapy and diagnosis of brain necrosis, and the interval between diagnosis of brain necrosis and treatment with bevacizumab, showed favorable calibration and discrimination in the training set (AUC 0.916). These findings were confirmed in the validation sets (AUC 0.912 and 0.827, respectively). Decision curve analysis confirmed the clinical utility of the model. The presented radiomics model, available as an online calculator, can serve as a user-friendly tool for individualized prediction of the response to bevacizumab in patients with brain necrosis after radiotherapy.

Exploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy.

BackgroundWe hypothesized that spatial heterogeneity exists between recurrent and non-recurrent regions within a tumor. The aim of this study was to determine if there is a difference between radiomics features derived from recurrent versus non recurrent regions within the tumor based on pre-treatment MRI.MethodsA total of 14 T4NxM0 NPC patients with histologically proven “in field” recurrence in the post nasal space following curative intent IMRT were included in this study. Pretreatment MRI were co-registered with MRI at the time of recurrence for the delineation of gross tumor volume at diagnosis(GTV) and at recurrence(GTVr). A total of 7 histogram features and 40 texture features were computed from the recurrent(GTVr) and non-recurrent region(GTV-GTVr). Paired t-tests and Wilcoxon signed-rank tests were carried out on the 47 quantified radiomics features.ResultsA total of 7 features were significantly different between recurrent and non-recurrent regions. Other than the variance from intensity-based histogram, the remaining six significant features were either from the gray-level size zone matrix (GLSZM) or the neighbourhood gray-tone difference matrix (NGTDM).ConclusionsThe radiomic features extracted from pre-treatment MRI can potentially reflect the difference between recurrent and non-recurrent regions within a tumor and has a potential role in pre-treatment identification of intra-tumoral radio-resistance for selective dose escalation.

Machine Learning-Based MRI Texture Analysis to Predict the Histologic Grade of Oral Squamous Cell Carcinoma.

OBJECTIVE. This study aimed to explore the performance of machine learning (ML)-based MRI texture analysis in discriminating between well-differentiated (WD) oral squamous cell carcinoma (OSCC) and moderately or poorly differentiated OSCC. MATERIALS AND METHODS. The study enrolled 80 patients with pathologically confirmed OSCC (18 WD OSCCs and 62 moderately or poorly differentiated OSCCs) who underwent pretreatment MRI. ROIs were manually delineated to cover the entire tumor to the greatest possible extent on T2-weighted imaging and contrast-enhanced T1-weighted imaging, and 1118 texture features were extracted. Dimension reduction was performed using reproducibility analysis by two radiologists, collinearity analysis, and feature selection with a minimum-redundancy maximum-relevance algorithm. Models were created using random forest (RF), artificial neural network, and logistic regression (LR) alone and with a synthetic minority oversampling technique (SMOTE). Classifier performance was assessed using 10-fold cross-validation. RESULTS. Dimension reduction steps yielded eight texture features, including four features from each sequence. None of the clinical variables was selected. Among the eight texture features, five and seven texture features showed significant differences between the two groups in the actual data and balanced data, respectively (p < 0.05). All classifiers with SMOTE achieved better performances than those alone. The RF classifier with SMOTE achieved the best performance with an area under the ROC curve of 0.936 and accuracy of 86.3%. CONCLUSION. ML-based MRI texture analysis provides a promising noninvasive approach for predicting the histologic grade of OSCC.

Tumor necrosis by pretreatment breast MRI: association with neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC).

To determine if tumor necrosis by pretreatment breast MRI and its quantitative imaging characteristics are associated with response to NAST in TNBC. This retrospective study included 85 TNBC patients (mean age 51.8 ± 13years) with MRI before NAST and definitive surgery during 2010-2018. Each MRI included T2-weighted, diffusion-weighted (DWI), and dynamic contrast-enhanced (DCE) imaging. For each index carcinoma, total tumor volume including necrosis (TTV), excluding necrosis (TV), and the necrosis-only volume (NV) were segmented on early-phase DCE subtractions and DWI images. NV and %NV were calculated. Percent enhancement on early and late phases of DCE and apparent diffusion coefficient were extracted from TTV, TV, and NV. Association between necrosis with pathological complete response (pCR) was assessed using odds ratio (OR). Multivariable analysis was used to evaluate the prognostic value of necrosis with T stage and nodal status at staging. Mann-Whitney U tests and area under the curve (AUC) were used to assess performance of imaging metrics for discriminating pCR vs non-pCR. Of 39 patients (46%) with necrosis, 17 had pCR and 22 did not. Necrosis was not associated with pCR (OR, 0.995; 95% confidence interval [CI] 0.4-2.3) and was not an independent prognostic factor when combined with T stage and nodal status at staging (P = 0.46). None of the imaging metrics differed significantly between pCR and non-pCR in patients with necrosis (AUC < 0.6 and P > 0.40). No significant association was found between necrosis by pretreatment MRI or the quantitative imaging characteristics of tumor necrosis and response to NAST in TNBC.

Prognostic value of pretreatment MRI texture features in breast cancer brain metastasis treated with Gamma Knife radiosurgery.

Gamma Knife radiosurgery (GKS) was recommended for treating patients with breast cancer brain metastasis (BCBM), but predictions of the existing prognostic models for therapeutic responsiveness vary substantially. To investigate the prognostic value of pretreatment clinical, MRI radiologic, and texture features in patients with BCBM undergoing GKS. The data of 81 BCBMs in 44 patients were retrospectively reviewed. Progressive disease was defined as an increase of at least 20% in the longest diameter of the target lesion or the presence of new intracranial lesions on contrast-enhanced T1-weighted (CE-T1W) imaging. Radiomic features were extracted from pretreatment CE-T1W images, T2-weighted (T2W) images, and ADC maps. Cox proportional hazard analyses were performed to identify independent predictors associated with BCBM-specific progression-free survival (PFS). A nomogram was constructed and its calibration ability was assessed. The cumulative BCBM-specific PFS was 52.27% at six months and 11.36% at one year, respectively. Age (hazard ratio [HR] 1.04; 95% confidence interval [CI] 1.01-1.06; P = 0.004) and CE-T1W-based kurtosis (HR 0.72; 95% CI 0.57-0.92; P = 0.008) were the independent predictors. The combination of CE-T1W-based kurtosis and age displayed a higher C-index (C-index 0.70; 95% CI 0.63-0.77) than did CE-T1W-based kurtosis (C-index 0.65; 95% CI 0.57-0.73) or age (C-index 0.63; 95% CI 0.56-0.70) alone. The nomogram based on the combinative model provided a better performance over age (P < 0.05). The calibration curves elucidated good agreement between prediction and observation for the probability of 7- and 12-month BCBM-specific PFS. Pretreatment CE-T1W-based kurtosis combined with age could improve prognostic ability in patients with BCBM undergoing GKS.

Can Follow-up be Avoided for Probably Benign US Masses with No Enhancement on MRI?

To assess whether no enhancement on pre-treatment MRI can rule out malignancy of additional US mass(es) initially assessed as BI-RADS 3 or 4 in women with newly diagnosed breast cancer. This retrospective study included consecutive women from 2010-2018 with newly diagnosed breast cancer; at least one additional breast mass (distinct from index cancer) assigned a BI-RADS 3 or 4 on US; and a bilateral contrast-enhanced breast MRI performed within 90 days of US. All malignant masses were pathologically proven; benign masses were pathologically proven or defined as showing at least 2 years of imaging stability. Incidence of malignant masses and NPV were calculated on a per-patient level using proportions and exact 95% CIs. In 230 patients with 309 additional masses, 140/309 (45%) masses did not enhance while 169/309 (55%) enhanced on MRI. Of the 140 masses seen in 105 women (mean age, 54 years; range 28-82) with no enhancement on MRI, all had adequate follow-up and 140/140 (100%) were benign, of which 89/140 (63.6%) were pathologically proven and 51/140 (36.4%) demonstrated at least 2 years of imaging stability. Pre-treatment MRI demonstrating no enhancement of US mass correlate(s) had an NPV of 100% (95% CI 96.7-100.0). All BI-RADS 3 and 4 US masses with a non-enhancing correlate on pre-treatment MRI were benign. The incorporation of MRI, when ordered by the referring physician, may decrease unnecessary follow-up imaging and/or biopsy if the initial US BI-RADS assessment and management recommendation were to be retrospectively updated. • Of 309 BI-RADS 3 or 4 US masses with a corresponding mass on MRI, 140/309 (45%) demonstrated no enhancement whereas 169/309 (55%) demonstrated enhancement • All masses classified as BI-RADS 3 or 4 on US without enhancement on MRI were benign • MRI can rule out malignancy in non-enhancing US masses with an NPV of 100.

Baseline MR Staging of Rectal Cancer: A Practical Approach

As therapeutic options to treat rectal cancers have advanced over the last several decades, MRI has become the standard of care for baseline local tumor and nodal staging of rectal cancers. An understanding of the technique, anatomy, tumor appearance, and elements of staging on MRI is essential to provide prognostic information and to guide neoadjuvant chemoradiation and surgical treatment. We provide a framework for imaging the rectum on MRI followed by a practical case-based approach to interpretation of pre-treatment MRI of the rectum in evaluation of rectal cancers, with examples and illustrations of the range of local tumor (T) stage and nodal (N) disease involvement. This approach can be paired with standardized reporting templates to support clear, accurate and clinically relevant imaging assessment of rectal cancers.

Utility of texture analysis on T2-weighted MR for differentiating tumor deposits from mesorectal nodes in rectal cancer patients, in a retrospective cohort.

The purpose of the study was to evaluate the utility of MR texture analysis for differentiating tumor deposits from mesorectal nodes in rectal cancer. Pretreatment MRI of 40 patients performed between 2006 and 2018 with pathologically proven tumor deposits and/or malignant nodes in the setting of rectal cancer were retrospectively reviewed. In total, 25 tumor deposits (TDs) and 71 positive lymph nodes (LNs) were analyzed for morphological and first-order texture analysis features on T2-weighted axial images. MR morphological features (lesion shape, size, signal heterogeneity, contrast enhancement) were analyzed and agreed in consensus by two experienced radiologists followed by assessmentwith Fisher's exact test. Texture analysis of the lesions was performed using TexRAD, a proprietary software algorithm. First-order texture analysis features (mean, standard deviation, skewness, entropy, kurtosis, MPP) were obtained after applying spatial scaling filters (SSF; 0, 2, 3, 4, 5, 6). Univariate analysis was performed with non-parametric Mann-Whitney U test. The results of univariate analysis were reassessed with generalized estimating equations followed by multivariate analysis. Using histopathology as a gold standard, diagnostic accuracy was assessed by obtaining area under the receiver operating curve. MR morphological parameter, lesion shape was a strong discriminator between TDs and LNs with a p value of 0.02 (AUC: 0.76, 95% CI of 0.66 to 0.84, SE: 0.06) and sensitivity, specificity of 90% and 68%, respectively. Skewness extracted at fine filter (SSF-2) was the only significant texture analysis parameter for distinguishing TDs from LNs with p value of 0.03 (AUC: 0.70, 95% CI of 0.59 to 0.79, SE: 0.06) and sensitivity, specificity of 70% and 72%, respectively. When lesion shape and skewness-2 were combined into a single model, the diagnostic accuracy was improved with AUC of 0.82 (SE: 0.05, 95% CI of 0.72 to 0.88 with p value of < 0.01). This model also showed a high sensitivity of 91% with specificity of 68%. Lesion shape on MR can be a useful predictor for distinguishing TDs from positive LNs in rectal cancer patients. When interpreted along with MR texture parameter of skewness, accuracy is further improved.

Avoidance of Overtreatment of Rectal Cancer by Selective Chemoradiotherapy: Results of the Optimized Surgery and MRI-Based Multimodal Therapy Trial

Neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer carries a high risk of adverse effects. The aim of this study was to examine the selective application of nCRT based on patient risk profile, as determined by MRI, to find the optimal range between undertreatment and overtreatment. In this prospective multicenter observational study, nCRT before total mesorectal excision (TME) was indicated in high-risk patients with involved or threatened mesorectal fascia (≤1mm), or cT4 or cT3 carcinomas of the lower rectal third. All other patients received primary surgery. Of the 1,093 patients, 878 (80.3%) were treated according to the protocol, 526 patients (59.9%) underwent primary surgery, and 352 patients (40.1%) underwent nCRT followed by surgery. The 3-year locoregional recurrence (LR) rate was 3.1%. Of 604 patients with clinical stages II and III, 267 (44.2%) had primary surgery; 337 (55.8%) received nCRT followed by TME. The 3-year LR rate was 3.9%, without significant differences between groups. In patients with clinical stages II and III who underwent primary surgery, 27.3% were diagnosed with pathological stage I. The results justify the restriction of nCRT to high-risk patients with rectal cancer classified by pretreatment MRI. Provided that a high-quality MRI diagnosis, TME surgery, and standardized examination of the resected specimen are performed, nCRT, with its adverse effects, costs, and treatment time can be avoided in more than 40% of patients with stage II or III rectal cancer with minimal risk of undertreatment. (clinicaltrials.gov NCT325649).

Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging.

The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR−), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR− breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR− breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated.

Incidence and treatment of positive pelvic sidewall lymph nodes in patients with rectal cancer.

The involvement of pelvic sidewall (PSW) lymph nodes in rectal cancer is a marker of locally advanced disease and poor prognosis. Eastern countries generally advocate lateral lymph node dissection (LLND) over the Western approach of neoadjuvant chemoradiotherapy and more limited surgery. The aim of this study was to evaluate how these advanced cancers were treated in three UK Health Boards. This was a retrospective review of three colorectal multidisciplinary team meetings from 2008 to 2016. All patients with rectal cancer and suspicious PSW lymph nodes on pretreatment MRI were included. There were 153 (6.2%) patients who met the inclusion criteria from a total of 2461 diagnosed rectal cancers. There was significant variability between the three centres with surgical intervention ranging from 59.2% to 84.4%, P=0.015. There were 81 patients who had neoadjuvant chemoradiotherapy prior to surgery; of these 67 (82.7%) still had positive PSW nodes on the restaging MRI, but only 13 (19.4%) had LLND. There was no difference in local recurrence (15.3% vs 11.8%, P=0.66), 5-year overall survival (69.2% vs 80.1%, P=0.16) or 5-year disease-free survival (69.2% vs 79.4%, P=0.72) between patients having LLND and those receiving standard neoadjuvant treatment followed by total mesorectal excision surgery. This study has demonstrated that rectal cancer patients with PSW positive nodal disease have advanced disease, mostly of the lower rectum, and receive a highly heterogeneous spectrum of therapies, even within a relatively small geographical area. Greater accuracy in our preoperative staging is needed to select those patients who will benefit from LLND surgery.

Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study.

To investigate whether pretreatment MRI-based radiomics of locally advanced rectal cancer (LARC) and/or the surrounding mesorectal compartment (MC) can predict pathologic complete response (pCR), neoadjuvant rectal (NAR) score, and tumor regression grade (TRG). One hundred thirty-two consecutive patients with LARC who underwent neoadjuvant chemoradiation and total mesorectal excision (TME) were retrospectively collected from 2 centers in the USA and Italy. The primary tumor and surrounding MC were segmented on the best available T2-weighted sequence (axial, coronal, or sagittal). Three thousand one hundred ninety radiomic features were extracted using a python package. The most salient radiomic features as well as MRI parameter and clinical-based features were selected using recursive feature elimination. A logistic regression classifier was built to distinguish between any 2 binned categories in the considered endpoints: pCR, NAR, and TRG. Repeated k-fold validation was performed and AUCs calculated. There were 24, 87, and 21 T4, T3, and T2 LARCs, respectively (median age 63years, 32 to 86). For NAR and TRG, the best classification performance was obtained using both the tumor and MC segmentations. The AUCs for classifying NAR 0 versus 2, pCR, and TRG 0/1 versus 2/3 were 0.66 (95% CI, 0.60-0.71), 0.80 (95% CI, 0.74-0.85), and 0.80 (95% CI, 0.77-0.82), respectively. Radiomics of pretreatment MRIs can predict pCR, TRG, and NAR score in patients with LARC undergoing neoadjuvant treatment and TME with moderate accuracy despite extremely heterogenous image data. Both the tumor and MC contain important prognostic information. • Machine learning of rectal cancer on images from the pretreatment MRI can predict important patient outcomes with moderate accuracy. • The tumor and the tissue around it both contain important prognostic information.

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study.

Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82–0.99) in the training set and AUC = 0.80 (95% CI: 0.58–0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87–22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.

Machine Learning Based Radiomic HPV Phenotyping of Oropharyngeal SCC: A Feasibility Study Using MRI.

To investigate whether a radiomic MRI feature-based prediction model can differentiate oropharyngeal squamous cell carcinoma (SCC) according to the human papillomavirus (HPV) status. Retrospective cohort study. Pretreatment MRI data from 62 consecutive patients with oropharyngeal SCC were retrospectively reviewed, and chronologically allocated to training (n = 43) and test sets (n = 19). Enhancing tumors were semi-automatically segmented on each slice of the postcontrast T1WI to span the entire tumor volume, after registration of T2WI to postcontrast T1WI; 170 radiomic features were extracted from the entire tumor volume. Relevant features were selected and radiomics models were trained using least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation, after subsampling of training sets using synthetic minority over-sampling technique to mitigate data imbalance. The selected features, weighted by their respective coefficients, were combined linearly to yield a radiomics score. The diagnostic performance of the radiomic score was evaluated using the area under the receiver operating characteristic curve (AUC). Six radiomic features, which revealed strong association with HPV status of oropharyngeal SCC, were selected using LASSO. The radiomics model yielded excellent performance on the training set (AUC, 0.982 [95% CI, 0.942-1.000]) and moderate performance on the test set (AUC, 0.744 [95% CI, 0.496-0.991]) for differentiating oropharyngeal SCC according to HPV status. Radiomics-based MRI phenotyping differentiates oropharyngeal SCC according to HPV status, and thus, is a potential imaging biomarker. 3 Laryngoscope, 131:E851-E856, 2021.

Predicting Therapeutic Antibody Delivery into Human Head and Neck Cancers.

The efficacy of antibody-based therapeutics depends on successful drug delivery into solid tumors; therefore, there is a clinical need to measure intratumoral antibody distribution. This study aims to develop and validate an imaging and computation platform to directly quantify and predict antibody delivery into human head and neck cancers in a clinical study. Twenty-four patients received systemic infusion of a near-infrared fluorescence-labeled therapeutic antibody followed by surgical tumor resection. A computational platform was developed to quantify the extent of heterogeneity of intratumoral antibody distribution. Both univariate and multivariate regression analyses were used to select the most predictive tumor biological factors for antibody delivery. Quantitative image features from the pretreatment MRI were extracted and correlated with fluorescence imaging of antibody delivery. This study not only confirmed heterogeneous intratumoral antibody distribution in-line with many preclinical reports, but also quantified the extent of interpatient, intertumor, and intratumor heterogeneity of antibody delivery. This study demonstrated the strong predictive value of tumor size for intratumoral antibody accumulation and its significant impact on antibody distribution in both primary tumor and lymph node metastasis. Furthermore, this study established the feasibility of using contrast-enhanced MRI to predict antibody delivery. This study provides a clinically translatable platform to measure antibody delivery into solid tumors and yields valuable insight into clinically relevant antibody tumor penetration, with implications in the selection of patients amenable to antibody therapy and the design of more effective dosing strategies.

NORAD01-GRECCAR16 multicenter phase III non-inferiority randomized trial comparing preoperative modified FOLFIRINOX without irradiation to radiochemotherapy for resectable locally advanced rectal cancer (intergroup FRENCH-GRECCAR- PRODIGE trial)

BackgroundPreoperative radiochemotherapy (RCT) is recommended in France prior to total mesorectal excision in patients with mid or low locally advanced rectal cancer (LARC) (cT3/T4 and/or N+) because it has been shown to improve local control. Preoperative RCT has also disadvantages including the absence of proven impact on metastatic recurrence and the risk of late side effects on bowel and genitourinary function. In patients with primarily resectable LARC, preoperative systemic chemotherapy without pelvic irradiation could be used as an alternative to RCT.MethodsThis study is a multicenter, open-label randomized, 2-arm phase III non-inferiority trial. Patients with mid or low resectable LARC (cT3N0 or cT1-T3N+ with circumferential resection margin [CRM] > 2 mm on pretreatment MRI) will be randomized to either modified FOLFIRINOX for 3 months or RCT (Cap50 intensified-modulated radiotherapy). All patients have restaging MRI after preoperative treatment. The primary endpoint is 3-year progression-free survival (PFS) from the time to randomization including progression during preoperative treatment. Secondary endpoints are treatment related toxicity, treatment compliance, R0 resection rate, sphincter saving surgery rate, postoperative morbidity and mortality rates, loco-regional recurrence free survival, overall survival, bowel and sexual functions at diagnosis, quality of life, radiologic and pathologic response after preoperative treatment. The number of patients required is 574.DiscussionThe choice of modified FOLFIRINOX for preoperative chemotherapy is supported by recent and consistent data on safety and efficacy of this regimen on rectal cancer. The use of preoperative chemotherapy instead of RCT could be associated with pronounced advantages in terms of functional results and quality of life in cancer survivors. However and first of all, the non-inferiority of preoperative chemotherapy compared to RCT on oncologic outcome has to be validated. If this study demonstrates the non-inferiority of chemotherapy compared to RCT, this can lead to a crucial change in clinical practice in a large subset of rectal cancer patients.Trial registrationClinicalTrials.gov NCT03875781 (March 15, 2019).Version 1.1.