Prediction of neoadjuvant radiation chemotherapy response and survival using pretreatment [18F]FDG PET/CT scans in locally advanced rectal cancer

Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach

Background

The modern approach to treating rectal cancer, which involves total mesorectal excision directed by imaging assessments, has significantly enhanced patient outcomes. However, locally recurrent rectal cancer (LRRC) continues to be a significant clinical issue. Identifying LRRC through imaging is complex, due to the mismatch between fibrosis and inflammatory pelvic tissue. This work aimed to develop a machine learning model for predicting LRRC using radiomic features extracted from 18F-FDG Positron Emission Tomography/Computed Tomography (PET/CT).

Methods

CT and PET images of PET/CT examinations were retrospectively collected from 44 patients, with 29 cases of recurrence (66 %) and 15 cases with no local recurrence (34 %). The whole analysis was conducted separately for CT and PET images to evaluate their different predictive power. Radiomic features were extracted from suspected lesion volumes identified by physicians and the most relevant radiomic features were selected to predict the presence or absence of LRRC. Feature selection was performed using a novel approach derived from gene expression analysis, based on the DNetPRO algorithm. The prediction was done using a Support Vector Classifier (SVC) with a 10-fold cross-validation. The efficiency of the pipeline in predicting LRRC was evaluated according to the sensitivity, specificity, Balanced Accuracy Score (BAS) and Matthews's Correlation Coefficient (MCC).

Results

CT features were found to be the most predictive, showing a sensitivity of 0.80, a specificity of 0.82, a BAS of 0.81 and an MCC of 0.61. PET features obtained a sensitivity of 0.93, a specificity of 0.61, a BAS of 0.77 and a MCC of 0.52. The combination of PET and CT features obtained a sensitivity of 0.80, a specificity of 0.75, a BAS of 0.77 and a MCC of 0.53.

Conclusions

To the best of our knowledge, the DNetPRO algorithm was applied for the first time to medical image analysis and proved suitable for the selection of radiomic features with the highest predictive power, a crucial step in a radiomic pipeline. Our results confirmed the efficiency of radiomic features in predicting LRRC, with CT features outperforming PET features in identifying the characteristic texture of LRRC. The combination of both yielded no performance improvement.

Radiomics and Machine Learning for prediction of two-year disease-specific mortality and KRAS mutation status in metastatic colorectal cancer

PURPOSE

Colorectal cancer is usually accompanied by liver metastases. The prediction of patient evolution is essential for the choice of the appropriate therapy. The aim of this study is to develop and evaluate machine learning models to predict KRAS gene mutations and 2-year disease-specific mortality from medical images.

METHODS

Clinical and follow-up information was collected from patients with metastatic colorectal cancer who had undergone computed tomography prior to liver resection. The dominant liver lesion was segmented in each scan and radiomic features were extracted from the volumes of interest. The 65% of the cases were employed to perform feature selection and to train machine learning algorithms through cross-validation. The best performing models were assembled and evaluated in the remaining cases of the cohort.

RESULTS

For the mortality model development, 101 cases were used as training set (64 alive, 37 deceased) and 35 as test set (22 alive, 13 deceased); while for KRAS mutation models, 55 cases were used for training (31 wild-type, 24 mutated) and 30 for testing (17 wild-type, 13 mutated). The ensemble of top performing models resulted in an area under the receiver operating characteristic curve of 0.878 for mortality and 0.905 for KRAS prediction.

CONCLUSIONS

Predicting the prognosis of patients with metastatic colorectal cancer is useful for making timely decisions about the best treatment options. This study presents a noninvasive method based on quantitative analysis of baseline images to identify factors influencing patient outcomes, with the aim of incorporating these tools as support systems.

PET/MRI in colorectal and anal cancers: an update

Positron emission tomography (PET) in the era of personalized medicine has a unique role in the management of oncological patients and offers several advantages over standard anatomical imaging. However, the role of molecular imaging in lower GI malignancies has historically been limited due to suboptimal anatomical evaluation on the accompanying CT, as well as significant physiological 18F-flurodeoxyglucose (FDG) uptake in the bowel. In the last decade, technological advancements have made whole-body FDG-PET/MRI a feasible alternative to PET/CT and MRI for lower GI malignancies. PET/MRI combines the advantages of molecular imaging with excellent soft tissue contrast resolution. Hence, it constitutes a unique opportunity to improve the imaging of these cancers. FDG-PET/MRI has a potential role in initial diagnosis, assessment of local treatment response, and evaluation for metastatic disease. In this article, we review the recent literature on FDG-PET/MRI for colorectal and anal cancers; provide an example whole-body FDG-PET/MRI protocol; highlight potential interpretive pitfalls; and provide recommendations on particular clinical scenarios in which FDG-PET/MRI is likely to be most beneficial for these cancer types.

Radiomics in colorectal cancer patients

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.

Value of pre-treatment 18F-FDG PET/CT radiomics in predicting the prognosis of stage III-IV colorectal cancer

Background and purpose

To investigate the value of radiomics features extracted from pre-treatment ¹⁸F-FDG PET/CT in predicting the outcomes of stage III-IV colorectal cancer (CRC), which may assist in clinical management strategies and precise treatment of stage III-IV CRC.

Materials and methods

124 patients with pathologically confirmed stage III-IV CRC who underwent pre-treatment ¹⁸F-FDG PET/CT scans were enrolled in this study. The least absolute shrinkage and selection operator Cox regression (LASSO-Cox) was used to select radiomics features, and the radiomics scores (Rad-scores) were calculated to build radiomics models. The performance of radiomics models was represented by the concordance index (C-index) and compared with clinical models and complex model. The bootstrap resampling method was used to create validation sets. Additionally, nomograms were developed based on complex models.

Results

The C-indices of the radiomics model for predicting PFS and OS were 0.712 (95%CI: 0.680-0.744) and 0.758 (0.728-0.789), respectively. In the clinical model, these values were 0.690 (0.664-0.0.717) and 0.738 (0.709-0.767), respectively. However, in the complex model were 0.734 (0.705-0.762) and 0.780 (0.754-0.807), respectively. The Kaplan-Meier curves demonstrated that the radiomics model could effectively separate patients with stage III-IV stage CRC into high- and low-risk groups (p < 0.001). Multivariate Cox regression analysis confirmed the independent prognostic value of Rad-scores.

Conclusion

Pre-treatment ¹⁸F-FDG PET/CT radiomics features can stratify the risk of patients with stage III-IV CRC and accurately predict their outcomes. These findings could be clinically valuable for precision treatment and management decisions in stage III-IV CRC.

Primary tumor heterogeneity on pretreatment 18F-FDG PET/CT to predict outcome in patients with rectal cancer who underwent surgery after neoadjuvant therapy

PURPOSE

This retrospective study aimed to investigate the value of texture features of primary tumors in pretreatment ¹⁸F-FDG PET/CT in the prediction of response to treatment, progression, and overall survival in patients with rectal cancer who underwent surgery after neoadjuvant therapy(NAT).

METHODS

Patients with rectal cancer who had pretreatment ¹⁸F-FDG PET/CT, and underwent surgery after NAT were included in this study. Clinicopathologic features, date of last follow-up, progression, and death were recorded. Textural and conventional PET parameters(maximum standardized uptake value-SUVmax, metabolic tumor volume-MTV, total lesion glycolysis-TLG) were obtained from PET/CT images using LifeX program. Parameters were grouped using Youden index in ROC analysis. Factors predicting the pathological response to treatment, progression, and overall survival were determined using logistic regression and Cox regression analyses.

RESULTS

Forty-four patients (26(59%) male, 18(41%) female; 60.1±11.4 years) with rectal cancer were included in this study. The numbers of patients with responders and non-responders to NAT were 15(34.9%) and 28(65.1%), respectively. One patient' pathology report did not contain the response status to NAT. The median of follow-up duration was 29.9 months. 9(20.5%) showed disease progression, and 8(18.2%) died during the follow-up period. Difference entropy_GLCM and correlation_GLCM parameters were found as independent predictors for response to NAT. The positivity of surgical margin, intensity interquartile range_CONV and AUC-CSH_DISC texture parameters were independent predictors of progression, while normalized inverse difference_GLCM and LZLGE_GLZLM parameters were independent predictors of mortality.

CONCLUSION

The texture parameters obtained from pretreatment ¹⁸F-FDG PET/CT have presented a more robust predictive value than conventional parameters in patients with rectal cancer who underwent surgery after NAT.

Prediction value of 18F-FDG PET/CT intratumor metabolic heterogeneity parameters for recurrence after radical surgery of stage II/III colorectal cancer

Purpose

We explored the predictive effect of intratumor metabolic heterogeneity indices extracted from ¹⁸F-FDG PET/CT on recurrence in stage II/III colorectal cancer after radical surgery.

Methods

A total of 140 stage II/III colorectal cancer patients who received preoperative ¹⁸F-FDG PET/CT and radical resection were enrolled. ¹⁸F-FDG traditional parameters including the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) under different thresholds; heterogeneity indices including the coefficient of variation with SUV 2.5 as a threshold (CV2.5), CV40%, heterogeneity index-1 (HI-1) calculated by the fixed-threshold method, and HI-2 calculated by the percentage threshold method; and clinicopathological information were collected. We concluded that relationships exist between these data and patients’ disease-free survival (DFS).

Results

Regional lymph node status (P < 0.001), nerve invasion (P = 0.036), tumor thrombus (P = 0.005), and HI-1 (P = 0.010) exhibited significant differences between the relapse and non-relapse groups, while SUVmax, MTV2.5, MTV40%, TLG2.5, TLG40%, CV2.5, CV40%, HI-2, and other clinicopathological factors had no differences between the relapse and non-relapse groups. Multivariate analysis demonstrated that HI-1 (HR = 1.02, 1.00–1.04, P = 0.038), regional lymph node metastasis (HR = 2.95, 1.37–6.38, P = 0.006), and tumor thrombus status (HR = 2.37, 1.13–4.99, P = 0.022) were independent factors significantly related to DFS.

Conclusion

HI-1, tumor thrombus status, and regional lymph node status could predict the recurrence of stage II/III colorectal cancer after radical resection and had an advantage over other ¹⁸F-FDG PET/CT conventional parameters and heterogeneity indices.

Radiomics analysis of pre-treatment F-18 FDG PET/CT for predicting response to transarterial radioembolization in liver tumors

Aim: To investigate the relationship between the textural features extracted from pre-treatment fluorine-18 fluorodeoxyglucose positron emission with computed tomography (F-18 FDG PET/CT) and the response to treatment in patients undergoing transarterial radioembolization (TARE) due to primary or metastatic liver tumors. Material and Method: A total of 25 liver lesions from the pre-treatment F-18 PET/CT images of 14 patients were segmented manually. Standard uptake value (SUV) metrics and radiomics features were extracted for each lesion. Metabolic treatment response was determined according to PERCIST criteria in 18F-FDG PET/CT imaging performed 2 months after the treatment. Feature selection was done with recursive feature elimination (RFE). The association between selected features and treatment response was evaluated with logistic regression analysis. Results: Eventually, 13 lesions responded to TARE, while 12 lesions remain stable or progressed. All standard uptake values and 27 out of 30 textural heterogeneity indicators were significantly higher in lesions that responded to treatment. SUVmax, kurtosis and dissimilarity features were selected by the RFE algorithm for the prediction of response to TARE. Logistic regression analysis revealed that all three parameters were significantly associated with treatment outcome. Conclusion: Textural features extracted from pre-treatment F-18 FDG PET/CT in patients undergoing TARE due to liver tumors are promising biomarkers that can be potentially used to predict metabolic treatment response.

Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models

Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent external validation that is decisive for their clinical application. Therefore, in this manuscript we present two complementary studies. In our modelling study, we developed and validated different radiomics signatures for outcome prediction after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) based on computed tomography (CT) and T2-weighted (T2w) magnetic resonance (MR) imaging datasets of 4 independent institutions (training: 122, validation 68 patients). We compared different feature classes extracted from the gross tumour volume for the prognosis of tumour response and freedom from distant metastases (FFDM): morphological and first order (MFO) features, second order texture (SOT) features, and Laplacian of Gaussian (LoG) transformed intensity features. Analyses were performed for CT and MRI separately and combined. Model performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumour response and FFDM, respectively. Overall, intensity features of LoG transformed CT and MR imaging combined with clinical T stage (cT) showed the best performance for tumour response prediction, while SOT features showed good performance for FFDM in independent validation (AUC = 0.70, CI = 0.69). In our external validation study, we aimed to validate previously published radiomics signatures on our multicentre cohort. We identified relevant publications on comparable patient datasets through a literature search and applied the reported radiomics models to our dataset. Only one of the identified studies could be validated, indicating an overall lack of reproducibility and the need of further standardization of radiomics before clinical application.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.

Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome

The aim of this study was to investigate the application of [18F]FDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [18F]FDGPET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded for a minimum of 12 months after PET/CT. Radiomics features from each avid lesion in PET and low-dose CT images were extracted. A hybrid descriptive-inferential method and the discriminant analysis (DA) were used for feature selection and for predictive model implementation, respectively. The performance of the features in predicting PD was performed for per-lesion analysis, per-patient analysis, and liver lesions analysis. All lesions were again considered to assess the diagnostic performance of the features in discriminating liver lesions. In predicting PD in the whole group of patients, on PET features radiomics analysis, among per-lesion analysis, only the GLZLM_GLNU feature was selected, while three features were selected from PET/CT images data set. The same features resulted more accurately by associating CT features with PET features (AUROC 65.22%). In per-patient analysis, three features for stand-alone PET images and one feature (i.e., HUKurtosis) for the PET/CT data set were selected. Focusing on liver metastasis, in per-lesion analysis, the same analysis recognized one PET feature (GLZLM_GLNU) from PET images and three features from PET/CT data set. Similarly, in liver lesions per-patient analysis, we found three PET features and a PET/CT feature (HUKurtosis). In discrimination of liver metastasis from the rest of the other lesions, optimal results of stand-alone PET imaging were found for one feature (SUVbwmin; AUROC 88.91%) and two features for merged PET/CT features analysis (AUROC 95.33%). In conclusion, our machine learning model on restaging [18F]FDGPET/CT was demonstrated to be feasible and potentially useful in the predictive evaluation of disease progression in MCC.

Advantages and Applications of Total-Body PET Scanning

Recent studies have focused on the development of total-body PET scanning in a variety of fields such as clinical oncology, cardiology, personalized medicine, drug development and toxicology, and inflammatory/infectious disease. Given its ultrahigh detection sensitivity, enhanced temporal resolution, and long scan range (1940 mm), total-body PET scanning can not only image faster than traditional techniques with less administered radioactivity but also perform total-body dynamic acquisition at a longer delayed time point. These unique characteristics create several opportunities to improve image quality and can provide a deeper understanding regarding disease detection, diagnosis, staging/restaging, response to treatment, and prognostication. By reviewing the advantages of total-body PET scanning and discussing the potential clinical applications for this innovative technology, we can address specific issues encountered in routine clinical practice and ultimately improve patient care.

Radiomic analysis for predicting prognosis of colorectal cancer from preoperative 18F-FDG PET/CT

Background

To develop and validate a survival model with clinico-biological features and ¹⁸F- FDG PET/CT radiomic features via machine learning, and for predicting the prognosis from the primary tumor of colorectal cancer.

Methods

A total of 196 pathologically confirmed patients with colorectal cancer (stage I to stage IV) were included. Preoperative clinical factors, serum tumor markers, and PET/CT radiomic features were included for the recurrence-free survival analysis. For the modeling and validation, patients were randomly divided into the training (n = 137) and validation (n = 59) set, while the 78 stage III patients [training (n = 55), and validation (n = 23)] was divided for the further experiment. After selecting features by the log-rank test and variable-hunting methods, random survival forest (RSF) models were built on the training set to analyze the prognostic value of selected features. The performance of models was measured by C-index and was tested on the validation set with bootstrapping. Feature importance and the Pearson correlation were also analyzed.

Results

Radiomics signature (containing four PET/CT features and four clinical factors) achieved the best result for prognostic prediction of 196 patients (C-index 0.780, 95% CI 0.634–0.877). Moreover, four features (including two clinical features and two radiomics features) were selected for prognostic prediction of the 78 stage III patients (C-index was 0.820, 95% CI 0.676–0.900). K–M curves of both models significantly stratified low-risk and high-risk groups (P < 0.0001). Pearson correlation analysis demonstrated that selected radiomics features were correlated with tumor metabolic factors, such as SUVmean, SUVmax.

Conclusion

This study presents integrated clinico-biological-radiological models that can accurately predict the prognosis in colorectal cancer using the preoperative ¹⁸F-FDG PET/CT radiomics in colorectal cancer. It is of potential value in assisting the management and decision making for precision treatment in colorectal cancer.

Trial registration The retrospectively registered study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center (No. 1909207-14-1910) and the data were analyzed anonymously.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03262-5.

Prognostic Impact of Total Lesion Glycolysis (TLG) from Preoperative 18F-FDG PET/CT in Stage II/III Colorectal Adenocarcinoma: Extending the Value of PET/CT for Resectable Disease

We investigated the prognostic role of metabolic parameters from preoperative ¹⁸F-FDG PET/CT in stage II/III colorectal adenocarcinoma. A total of 327 stage II/III colorectal adenocarcinoma patients who underwent curative resection were included. The maximal standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were analyzed for optimal cut-offs and their effect on DFS. Differences in DFS rates and hazard ratios for DFS between cut-offs were statistically significant in SUVmax, MTV2.5, MTV3, TLG 2.5, TLG3, and TLG30%. Factors significantly related to DFS in univariate Cox regression were age, sex, stage, preoperative CEA, SUVmax, MTV2.5, MTV3, TLG2.5, TLG3, and TLG30%. Age, sex, preoperative CEA, and TLG2.5 (p = 0.009) sustained statistically significant difference in multivariate analysis. The 1-, 3-, and 5-year DFS rates for TLG2.5 ≤ 448.5 were 98.1%, 79.6%, and 74.8%, significantly higher than 78.4%, 68.5%, and 61.1% of TLG2.5 > 448.5, respectively (p = 0.012). TLG, a parameter indicating both the metabolic activity and metabolic volume, was the strongest predictor independently associated with DFS, among several PET parameters with statistical significance. These results suggest the potential prognostic value of preoperative ¹⁸F-FDG PET/CT in stage II/III resectable colorectal cancer.

Metabolic positron emission tomography/CT response after induction chemotherapy and chemo(re)irradiation is associated with higher negative resection margin rate in patients with locally recurrent rectal cancer

AIM

Positron emission tomography (PET)/CT can be used to monitor the metabolic changes that occur after intensified treatment with induction chemotherapy and chemo(re)irradiation for locally recurrent rectal cancer (LRRC). This study aimed to analyse the correlation between the PET/CT response and final histopathological outcomes.

METHODS

All LRRC patients who underwent induction chemotherapy prior to surgery between January 2010 and July 2020 and were monitored with pretreatment and post-treatment PET/CT were included. Visual qualitative analysis was performed, and patients were scored as having achieved a complete metabolic response (CMR), partial metabolic response (PMR) or no response (NR). The histopathological response was assessed according to the Mandard tumour regression (TRG) score and categorized as major (TRG 1-2), partial (TRG 3) or poor (TRG 4-5). The PET/CT and TRG categories were compared, and possible confounders were analysed.

RESULTS

A total of 106 patients were eligible for analysis; 24 (23%) had a CMR, 54 (51%) had a PMR and 28 (26%) had NR. PET/CT response was a significant predictor of the negative resection margin rate, achieving 96% for CMR, 69% for PMR and 50% for NR. The overall accuracy between PET score and pathological TRG was 45%, and the positive predictive value for CMR was 63%. A longer interval between post-treatment PET/CT and surgery negatively influenced the predictive value.

CONCLUSION

Metabolic PET/CT response evaluation after neoadjuvant treatment proves to be a complementary diagnostic tool to standard MRI in assessing tumour response, and may play a role for treatment planning in LRRC patients.

Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer with Metric Learning Using Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography

OBJECTIVES

Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is the mainstay of treatment for patients with locally advanced rectal cancer. Based on baseline 18F-fluorodeoxyglucose ([18F]-FDG)-positron emission tomography (PET)/computed tomography (CT), a new artificial intelligence model using metric learning (ML) was introduced to predict responses to NCRT.

PATIENTS AND METHODS

This study used the data of 236 patients with newly diagnosed rectal cancer; the data of 202 and 34 patients were for training and validation, respectively. All patients received pretreatment [18F]FDG-PET/CT, NCRT, and surgery. The treatment response was scored by Dworak tumor regression grade (TRG); TRG3 and TRG4 indicated favorable responses. The model employed ML combined with the Uniform Manifold Approximation and Projection for dimensionality reduction. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance.

RESULTS

In the training cohort, 115 patients (57%) achieved TRG3 or TRG4 responses. The area under the ROC curve was 0.96 for the prediction of a favorable response. The sensitivity, specificity, and accuracy were 98.3%, 96.5%, and 97.5%, respectively. The sensitivity, specificity, and accuracy for the validation cohort were 95.0%, 100%, and 98.8%, respectively.

CONCLUSIONS

The new ML model presented herein was used to determined that baseline 18F[FDG]-PET/CT images could predict a favorable response to NCRT in patients with rectal cancer. External validation is required to verify the model's predictive value.

Machine learning-based prediction of invisible intraprostatic prostate cancer lesions on 68 Ga-PSMA-11 PET/CT in patients with primary prostate cancer

PURPOSE

⁶⁸ Ga-PSMA PET/CT has high specificity and sensitivity for the detection of both intraprostatic tumor focal lesions and metastasis. However, approximately 10% of primary prostate cancer are invisible on PSMA-PET (exhibit no or minimal uptake). In this work, we investigated whether machine learning-based radiomics models derived from PSMA-PET images could predict invisible intraprostatic lesions on ⁶⁸ Ga-PSMA-11 PET in patients with primary prostate cancer.

METHODS

In this retrospective study, patients with or without prostate cancer who underwent ⁶⁸ Ga-PSMA PET/CT and presented negative on PSMA-PET image at either of two different institutions were included: institution 1 (between 2017 and 2020) for the training set and institution 2 (between 2019 and 2020) for the external test set. Three random forest (RF) models were built using selected features extracted from standard PET images, delayed PET images, and both standard and delayed PET images. Then, subsequent tenfold cross-validation was performed. In the test phase, the three RF models and PSA density (PSAD, cut-off value: 0.15 ng/ml/ml) were tested with the external test set. The area under the receiver operating characteristic curve (AUC) was calculated for the models and PSAD. The AUCs of the radiomics model and PSAD were compared.

RESULTS

A total of 64 patients (39 with prostate cancer and 25 with benign prostate disease) were in the training set, and 36 (21 with prostate cancer and 15 with benign prostate disease) were in the test set. The average AUCs of the three RF models from tenfold cross-validation were 0.87 (95% CI: 0.72, 1.00), 0.86 (95% CI: 0.63, 1.00), and 0.91 (95% CI: 0.69, 1.00), respectively. In the test set, the AUCs of the three trained RF models and PSAD were 0.903 (95% CI: 0.830, 0.975), 0.856 (95% CI: 0.748, 0.964), 0.925 (95% CI:0.838, 1.00), and 0.662 (95% CI: 0.510, 0.813). The AUCs of the three radiomics models were higher than that of PSAD (0.903, 0.856, and 0.925 vs. 0.662, respectively; P = .007, P = .045, and P = .005, respectively).

CONCLUSION

Random forest models developed by ⁶⁸ Ga-PSMA-11 PET-based radiomics features were proven useful for accurate prediction of invisible intraprostatic lesion on ⁶⁸ Ga-PSMA-11 PET in patients with primary prostate cancer and showed better diagnostic performance compared with PSAD.

Predictive Value of Metabolic Parameters Derived From 18F-FDG PET/CT for Microsatellite Instability in Patients With Colorectal Carcinoma

Background

Microsatellite instability (MSI) is one of the important factors that determine the effectiveness of immunotherapy in colorectal cancer (CRC) and serves as a prognostic biomarker for its clinical outcomes.

Purpose

To investigate whether the metabolic parameters derived from¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) can predict MSI status in patients with CRC.

Materials and Methods

A retrospective analysis was performed on CRC patients who underwent ¹⁸F-FDG PET/CT examination before surgery between January 2015 and April 2021. The metabolic ¹⁸F-FDG PET/CT parameters of the primary CRC lesion were calculated and recorded with different thresholds, including the maximum, peak, and mean standardized uptake value (SUV_max, SUV_peak, and SUV_mean), as well as the metabolic tumor volume (MTV) and the total lesion glycolysis (TLG). The status of MSI was determined by immunohistochemical assessment. The difference of quantitative parameters between MSI and microsatellite stability (MSS) groups was assessed, and the receiver operating characteristic (ROC) analyses with area under ROC curves (AUC) was used to evaluate the predictive performance of metabolic parameters.

Results

A total of 44 patients (24 men and 20 women; mean ± standard deviation age: 71.1 ± 14.2 years) were included. There were 14 patients in the MSI group while there were 30 in the MSS group. MTV_30%, MTV_40%, MTV_50%, and MTV_60%, as well as TLG_50% and TLG_60% showed significant difference between two groups (all p-values <0.05), among which MTV_50% demonstrated the highest performance in the prediction of MSI, with an AUC of 0.805 [95% confidence interval (CI): 0.657-0.909], a sensitivity of 92.9% (95% CI: 0.661-0.998), and a specificity of 66.7% (95% CI: 0.472-0.827). Patients' age and MTV_50% were significant predictive indicators of MSI in multivariate logistic regression.

Conclusion

The metabolic parameters derived from¹⁸F-FDG PET/CT were able to preoperatively predict the MSI status in CRC, with MTV_50% demonstrating the highest predictive performance. PET/CT imaging could serve as a noninvasive tool in the guidance of immunotherapy and individualized treatment in CRC patients.

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

INTRODUCTION

Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy.

MATERIALS AND METHODS

Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used.

RESULTS

This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer.

CONCLUSIONS

Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

Prognostic value of 18F-FDG PET/CT with texture analysis in patients with rectal cancer treated by surgery

PURPOSE

The aim of this study was to evaluate the ability of texture analysis using pretreatment 18F-FDG PET/CT to predict prognosis in patients with surgically treated rectal cancer.

METHODS

We analyzed 94 patients with pathologically proven rectal cancer who underwent pretreatment 18F-FDG PET/CT and were subsequently treated with surgery. The volume of interest of the primary tumor was defined using a threshold of 40% of the maximum standardized uptake value (SUVmax), and conventional (SUVmax, metabolic tumor volume [MTV], total lesion glycolysis [TLG]) and textural PET features were extracted. Harmonization of PET features was performed with the ComBat method. The study endpoints were overall survival (OS) and progression-free survival (PFS), and the prognostic value of PET features was evaluated by Cox regression analysis.

RESULTS

In the follow-up period (median 41.7 [interquartile range, 30.5-60.4] months), 21 (22.3%) and 30 (31.9%) patients had cancer-related death or disease progression, respectively. Univariate analysis revealed a significant association of (1) MTV, TLG, and gray-level co-occurrence matrix (GLCM) entropy with OS; and (2) SUVmax, MTV, TLG, and GLCM entropy with PFS. In multivariate analysis including clinical characteristics, GLCM entropy (≥ 2.13) was the only relevant prognostic PET feature for poor OS (hazard ratio [HR]: 4.16, p = 0.035) and PFS (HR: 2.70, p = 0.046).

CONCLUSION

GLCM entropy, which indicates metabolic intratumoral heterogeneity, was an independent prognostic factor in patients with surgically treated rectal cancer. Compared with conventional PET features, GLCM entropy has better predictive value and shows potential to facilitate precision medicine.

Prognostic value of metabolic parameters on 18F-fluorodeoxyglucose positron tomography/computed tomography in classical rectal adenocarcinoma

We aimed to investigate the prognostic value of the metabolic parameters of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in classical rectal adenocarcinoma (CRAC). We retrospectively reviewed 149 patients with CRAC who underwent preoperative 18F-FDG PET/CT at initial diagnosis followed by curative surgical resection. 18F-FDG PET/CT metabolic parameters including maximum standardized uptake value (SUVmax), metabolic tumour volume (MTV), and total lesion glycolysis (TLG) for disease-free survival (DFS) and overall survival (OS) were evaluated for prognostic significance by univariate and multivariate analyses, along with conventional risk factors including pathologic T (pT) stage, lymph node (LN) metastasis, lymphovascular invasion (LVI), perineural invasion (PNI), and preoperative carcinoembryonic antigen (CEA) level. On univariate analysis, high pT stage, positive LN metastasis, LVI, PNI, MTV, and TLG were significant prognostic factors affecting DFS (all P < 0.05), while CEA level, high pT stage, positive LN metastasis, LVI, PNI, MTV, and TLG affected OS (all P < 0.05). On multivariate analysis, positive LN metastasis, LVI, MTV, and TLG were independent prognostic factors affecting DFS (all P < 0.05), while CEA level, positive LN metastasis, and MTV affected OS (all P < 0.05). Thus, the volume-based metabolic parameters from preoperative 18F-FDG PET/CT scans are independent prognostic factors in patients with CRAC.

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

PURPOSE

Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors.

METHODS

A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score.

RESULTS

The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice.

CONCLUSION

Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

Radiomics Features of 18F-Fluorodeoxyglucose Positron-Emission Tomography as a Novel Prognostic Signature in Colorectal Cancer

The aim of this study was to investigate the prognostic value of radiomics signatures derived from 18F-fluorodeoxyglucose (18F-FDG) positron-emission tomography (PET) in patients with colorectal cancer (CRC). From April 2008 to Jan 2014, we identified CRC patients who underwent 18F-FDG-PET before starting any neoadjuvant treatments and surgery. Radiomics features were extracted from the primary lesions identified on 18F-FDG-PET. Patients were divided into a training and validation set by random sampling. A least absolute shrinkage and selection operator Cox regression model was applied for prognostic signature building with progression-free survival (PFS) using the training set. Using the calculated radiomics score, a nomogram was developed, and its clinical utility was assessed in the validation set. A total of 381 patients with surgically resected CRC patients (training set: 228 vs. validation set: 153) were included. In the training set, a radiomics signature labeled as a rad_score was generated using two PET-derived features, such as gray-level run length matrix long-run emphasis (GLRLM_LRE) and gray-level zone length matrix short-zone low-gray-level emphasis (GLZLM_SZLGE). Patients with a high rad_score in the training and validation set had a shorter PFS. Multivariable analysis revealed that the rad_score was an independent prognostic factor in both training and validation sets. A radiomics nomogram, developed using rad_score, nodal stage, and lymphovascular invasion, showed good performance in the calibration curve and comparable predictive power with the staging system in the validation set. Textural features derived from 18F-FDG-PET images may enable detailed stratification of prognosis in patients with CRC.

Predicting pathological response to chemoradiotherapy for rectal cancer: a systematic review

Introduction: Pathological complete response (pCR) rates of approximately 20% following neoadjuvant long-course chemoradiotherapy for rectal cancer have given rise to non-operative or watch-and-wait (W&W) management. To improve outcomes there has been significant research into predictors of response. The goal is to optimize selection for W&W, avoid chemoradiotherapy in those who won't benefit and improve treatment to maximize the clinical complete response (cCR) rate and the number of patients who can be considered for W&W.Areas covered: A systematic review of articles published 2008-2018 and indexed in PubMed, Embase or Medline was performed to identify predictors of pathological response (including pCR and recognized tumor regression grades) to fluoropyrimidine-based chemoradiotherapy in patients who underwent total mesorectal excision for rectal cancer. Evidence for clinical, biomarker and radiological predictors is discussed as well as potential future directions.Expert opinion: Our current ability to predict the response to chemoradiotherapy for rectal cancer is very limited. cCR of 40% has been achieved with total neoadjuvant therapy. If neoadjuvant treatment for rectal cancer continues to improve it is possible that the treatment for rectal cancer may eventually parallel that of anal squamous cell carcinoma, with surgery reserved for the minority of patients who don't respond to chemoradiotherapy.

18F-FDG PET/CT Metrics Are Correlated to the Pathological Response in Esophageal Cancer Patients Treated With Induction Chemotherapy Followed by Neoadjuvant Chemo-Radiotherapy

Background and Objective

The aim of this study was to assess the ability of Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (¹⁸F-FDG PET/CT) to provide functional information useful in predicting pathological response to an intensive neoadjuvant chemo-radiotherapy (nCRT) protocol for both esophageal squamous cell carcinoma (SCC) and adenocarcinoma (ADC) patients.

Material and Methods

Esophageal carcinoma (EC) patients, treated in our Center between 2014 and 2018, were retrospectively reviewed. The nCRT protocol schedule consisted of an induction phase of weekly administered docetaxel, cisplatin, and 5-fluorouracil (TCF) for 3 weeks, followed by a concomitant phase of weekly TCF for 5 weeks with concurrent radiotherapy (50–50.4 Gy in 25–28 fractions). Three ¹⁸F-FDG PET/CT scans were performed: before (PET₁) and after (PET₂) induction chemotherapy (IC), and prior to surgery (PET₃). Correlation between PET parameters [maximum and mean standardized uptake value (SUV_max and SUV_mean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)], radiomic features and tumor regression grade (TGR) was investigated.

Results

Fifty-four patients (35 ADC, 19 SCC; 48 cT3/4; 52 cN+) were eligible for the analysis. Pathological response to nCRT was classified as major (TRG1-2, 41/54, 75.9%) or non-response (TRG3-4, 13/54, 24.1%). A major response was statistically correlated with SCC subtype (p = 0.02) and smaller tumor length (p = 0.03). MTV and TLG measured prior to IC (PET₁) were correlated to TRG1-2 response (p = 0.02 and p = 0.02, respectively). After IC (PET₂), SUV_mean and TLG correlated with major response (p = 0.03 and p = 0.04, respectively). No significance was detected when relative changes of metabolic parameters between PET₁ and PET₂ were evaluated. At textural quantitative analysis, three independent radiomic features extracted from PET₁ images ([JointEnergy and InverseDifferenceNormalized of GLCM and LowGrayLevelZoneEmphasis of GLSZM) were statistically correlated with major response (p < 0.0002).

Conclusions

¹⁸F-FDG PET/CT traditional metrics and textural features seem to predict pathologic response (TRG) in EC patients treated with induction chemotherapy followed by neoadjuvant chemo-radiotherapy. Further investigations are necessary in order to obtain a reliable predictive model to be used in the clinical practice.

Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review

Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on ¹⁸F-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models.

The Current State of Radiomics for Meningiomas: Promises and Challenges

Meningiomas are the most common primary tumors of the central nervous system. Given the fact that the majority of meningiomas are benign, the preoperative risk stratification and treatment strategy decision-making highly rely on the conventional subjective radiologic evaluation. However, this traditional diagnostic and treatment modality may not be effective in patients with aggressive-growing tumors or symptomatic patients with potential risk of recurrence after surgical resection or radiotherapy, as this passive “wait and see” strategy could miss the optimal opportunity of intervention. Radiomics, a new rising discipline, translates high-dimensional image information into abundant mathematical data by multiple computational algorithms. It provides an objective and quantitative approach to interpret the imaging data, rather than the subjective and qualitative interpretation from relatively limited human visual observation. In fact, the enormous amount of information generated by radiomics analyses provides radiological to histopathological tumor information, which are visually imperceptible, and offers technological basis to its applications amid diagnosis, treatment, and prognosis. Here, we review the latest advancements of radiomics and its applications in the prediction of the pathological grade, pathological subtype, recurrence possibility, and differential diagnosis of meningiomas, and the potential and challenges in general clinical applications. In this review, we highlight the generalization of shared radiomic features among different studies and compare different performances of popular algorithms. At last, we discuss several possible aspects of challenges and future directions in the development of radiomic applications in meningiomas.

Value of volumetric and textural analysis in predicting the treatment response in patients with locally advanced rectal cancer

OBJECTIVE

The aim of this study was to assess the value of baseline 18F-FDG PET/CT in predicting the response to neoadjuvant chemo-radiotherapy (NCRT) in patients with locally advanced rectal cancer (LARC) via the volumetric and texture data obtained from 18F-FDG PET/CT images.

METHODS

 In total, 110 patients who had undergone NCRT after initial PET/CT and followed by surgical resection were included in this study. Patients were divided into two groups randomly as a train set (n: 88) and test set (n: 22). Pathological response using three-point tumor regression grade (TRG) and metastatic lymph nodes in PET/CT images were determined. TRG1 were accepted as responders and TRG2-3 as non-responders. Region of interest for the primary tumors was drawn and volumetric features (metabolic tumor volume (MTV) and total lesion glycolysis (TLG)) and texture features were calculated. In train set, the relationship between these features and TRG was investigated with Mann-Whitney U test. Receiver operating curve analysis was performed for features with p < 0.05. Correlation between features were evaluated with Spearman correlation test, features with correlation coefficient < 0.8 were evaluated with the logistic regression analysis for creating a model. The model obtained was tested with a test set that has not been used in modeling before.

RESULTS

 In train set 32 (36.4%) patients were responders. The rate of visually detected metastatic lymph node at baseline PET/CT was higher in non-responders than responders (71.4% and 46.9%, respectively, p = 0.022). There was a statistically significant difference between TLG, MTV, SHAPE_compacity, NGLDMcoarseness, GLRLM_GLNU, GLRLM_RLNU, GLZLM_LZHGE and GLZLM_GLNU between responders and non-responders. MTV and NGLDMcoarseness demonstrated the most significance (p = 0.011). A multivariate logistic regression analysis that included MTV, coarseness, GLZLM_LZHGE and lymph node metastasis was performed. Multivariate analysis demonstrated MTV and lymph node metastasis were the most meaningful parameters. The model's AUC was calculated as 0.714 (p = 0.001,0.606-0.822, 95% CI). In test set, AUC was determined 0.838 (p = 0.008,0.671-1.000, 95% CI) in discriminating non-responders.

CONCLUSIONS

Although there were points where textural features were found to be significant, multivariate analysis revealed no diagnostic superiority over MTV in predicting treatment response. In this study, it was thought higher MTV value and metastatic lymph nodes in PET/CT images could be a predictor of low treatment response in patients with LARC.

The Impact of Primary Tumor and Locoregional Metastatic Lymph Node SUVmax on Predicting Survival in Patients with Rectal Cancer

Objectives:

The aim of this study was to evaluate the impact of maximum standard uptake value (SUV_max) of the primary tumor and locoregional metastatic lymph node in predicting survival in patients with the preoperative rectal adenocarcinoma.

Methods:

One hundred and fifteen patients [mean age ± standard deviation (SD): 58.7±11.4 years] with biopsy-proven rectal adenocarcinoma underwent ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging for the staging were included in this study. All patients were followed-up for a minimum of 12 months (mean ± SD: 29.7±13.5 months). Tumor-node-metastasis 2017 clinical staging, SUV_max of the primary rectal tumor and locoregional lymph nodes on the PET/CT studies were evaluated.

Results:

All patients had increased FDG activity of the primary tumor. The mean ± SD SUV_max of the primary tumor and locoregional metastatic lymph node were 21.0±9.1 and 4.6±2.8, respectively. Primary tumor SUV_max did not have an effect on predicting survival (p=0.525) however locoregional metastatic lymph node SUV_max had an effect (p<0.05) on predicting survival. Clinical stage of the disease was a factor predicting survival (p<0.001).

Conclusion:

¹⁸F-FDG PET/CT is an effective imaging modality for detecting primary tumors and metastases in rectal adenocarcinoma and clinical stage assessment with PET/CT had an effect on predicting survival. Furthermore, in our study locoregional lymph node SUV_maks was defined as a factor in predicting survival.

Recent advances of PET imaging in clinical radiation oncology

Radiotherapy and radiation oncology play a key role in the clinical management of patients suffering from oncological diseases. In clinical routine, anatomic imaging such as contrast-enhanced CT and MRI are widely available and are usually used to improve the target volume delineation for subsequent radiotherapy. Moreover, these modalities are also used for treatment monitoring after radiotherapy. However, some diagnostic questions cannot be sufficiently addressed by the mere use standard morphological imaging. Therefore, positron emission tomography (PET) imaging gains increasing clinical significance in the management of oncological patients undergoing radiotherapy, as PET allows the visualization and quantification of tumoral features on a molecular level beyond the mere morphological extent shown by conventional imaging, such as tumor metabolism or receptor expression. The tumor metabolism or receptor expression information derived from PET can be used as tool for visualization of tumor extent, for assessing response during and after therapy, for prediction of patterns of failure and for definition of the volume in need of dose-escalation. This review focuses on recent and current advances of PET imaging within the field of clinical radiotherapy / radiation oncology in several oncological entities (neuro-oncology, head & neck cancer, lung cancer, gastrointestinal tumors and prostate cancer) with particular emphasis on radiotherapy planning, response assessment after radiotherapy and prognostication.

Predicting pathological complete response in rectal cancer after chemoradiotherapy with a random forest using 18F-fluorodeoxyglucose positron emission tomography and computed tomography radiomics

Background

Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is the standard treatment for patients with locally advanced rectal cancer. This study developed a random forest (RF) model to predict pathological complete response (pCR) based on radiomics derived from baseline ¹⁸F-fluorodeoxyglucose ([¹⁸F]FDG)-positron emission tomography (PET)/computed tomography (CT).

Methods

This study included 169 patients with newly diagnosed rectal cancer. All patients received ¹⁸F[FDG]-PET/CT, NCRT, and surgery. In total, 68 radiomic features were extracted from the metabolic tumor volume. The numbers of splits in a decision tree and trees in an RF were determined based on their effects on predictive performance. Receiver operating characteristic curve analysis was performed to evaluate predictive performance and ascertain the optimal threshold for maximizing prediction accuracy.

Results

After NCRT, 22 patients (13%) achieved pCR, and 42 features that could differentiate tumors with pCR were used to construct the RF model. Six decision trees and seven splits were suitable. Accordingly, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 81.8%, 97.3%, 81.8%, 97.3%, and 95.3%, respectively.

Conclusions

By using an RF, we determined that radiomics derived from baseline ¹⁸F[FDG]-PET/CT could accurately predict pCR in patients with rectal cancer. Highly accurate and predictive values can be achieved but should be externally validated.

The 2017 Assisi Think Tank Meeting on rectal cancer: A positioning paper

BACKGROUND AND PURPOSES

To describe current practice in the management of rectal cancer, to identify uncertainties that usually arise in the multidisciplinary team (MDT)'s discussions ('grey zones') and propose next generation studies which may provide answers to them.

MATERIALS AND METHODS

A questionnaire on the areas of controversy in managing T2, T3 and T4 rectal cancer was drawn up and distributed to the Rectal-Assisi Think Tank Meeting (ATTM) Expert European Board. Less than 70% agreement on a treatment option was indicated as uncertainty and selected as a 'grey zone'. Topics with large disagreement were selected by the task force group for discussion at the Rectal-ATTM.

RESULTS

The controversial clinical issues that had been identified within cT2-cT3-cT4 needed further investigation. The discussions focused on the role of (1) neoadjuvant therapy and organ preservation on cT2-3a low-middle rectal cancer; (2) neoadjuvant therapy in cT3 low rectal cancer without high risk features; (3) total neoadjuvant therapy, radiotherapy boost and the best chemo-radiotherapy schedule in T4 tumors. A description of each area of investigation and trial proposals are reported.

CONCLUSION

The meeting successfully identified 'grey zones' and, in the light of new evidence, proposed clinical trials for treatment of early, intermediate and advanced stage rectal cancer.

Perspectives in Radiomics for Personalized Medicine and Theranostics

Radiomics handles imaging biomarker from high-throughput feature extraction through complex pattern recognition that is difficult for human to process. Recent medical paradigms are rapidly changing to personalized medicine, including molecular targeted therapy, immunotherapy, and theranostics, and the importance of biomarkers for these is growing day by day. Even though biopsy continues to gold standard for tumor assessment in personalized medicine, imaging is expected to complement biopsy because it allows whole tumor evaluation, whole body evaluation, and non-invasive and repetitive evaluation. Radiomics is known as a useful method to get imaging biomarkers related to intratumor heterogeneity in molecular targeted therapy as well as one-size-fits-all therapy. It is also expected to be useful in new paradigms such as immunotherapy and somatostatin receptor (SSTR) or prostate-specific membrane antigen (PSMA)-targeted theranostics. Radiomics research should move to multimodality (CT, MR, PET, etc.), multicenter, and prospective studies from current single modality, single institution, and retrospective studies. Image-quality harmonization, intertumor heterogeneity, and integrative analysis of information from different scales are thought to be important keywords in future radiomics research. It is clear that radiomics will play an important role in personalized medicine.

Predicting Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer: Combined Statistical Modeling Using Clinicopathological Factors and FDG PET/CT Texture Parameters

PURPOSE

The aim of this study was to develop a combined statistical model using both clinicopathological factors and texture parameters from F-FDG PET/CT to predict responses to neoadjuvant chemotherapy in patients with breast cancer.

MATERIALS AND METHODS

A total of 435 patients with breast cancer were retrospectively enrolled. Clinical and pathological data were obtained from electronic medical records. Texture parameters were extracted from pretreatment FDG PET/CT images. The end point was pathological complete response, defined as the absence of residual disease or the presence of residual ductal carcinoma in situ without residual lymph node metastasis. Multivariable logistic regression modeling was performed using clinicopathological factors and texture parameters as covariates.

RESULTS

In the multivariable logistic regression model, various factors and parameters, including HER2, histological grade or Ki-67, gradient skewness, gradient kurtosis, contrast, difference variance, angular second moment, and inverse difference moment, were selected as significant prognostic variables. The predictive power of the multivariable logistic regression model incorporating both clinicopathological factors and texture parameters was significantly higher than that of a model with only clinicopathological factors (P = 0.0067). In subgroup analysis, texture parameters, including gradient skewness and gradient kurtosis, were selected as independent prognostic factors in the HER2-negative group.

CONCLUSIONS

A combined statistical model was successfully generated using both clinicopathological factors and texture parameters to predict the response to neoadjuvant chemotherapy. Results suggest that addition of texture parameters from FDG PET/CT can provide more information regarding treatment response prediction compared with clinicopathological factors alone.

Does baseline [18F] FDG-PET/CT correlate with tumor staging, response after neoadjuvant chemoradiotherapy, and prognosis in patients with rectal cancer?

BACKGROUND

[18F] fluorodeoxyglucose positron emission tomography/computed tomography ([18F] FDG-PET/CT) may be used for tumor staging and prognosis in several tumors but its role in rectal cancer is still debated. The aim of the present study was to assess the correlation of baseline [18F] FDG-PET parameters with tumor staging, tumor response (tumor regression grade (TRG)), and outcome in a series of patients affected by locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (CRT).

METHODS

One hundred patients treated with neoadjuvant CRT and radical surgery were enrolled in the present study. Maximum standardized uptake value (SUVmax), SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) at the baseline [18F] FDG-PET were calculated. These PET parameters were correlated with tumor staging, histopathological data (TRG1 vs. TRG2-5 and TRG1-2 vs. TRG3-5), disease-free survival, and overall survival.

RESULTS

SUVmax and SUVmean of primary tumor were statistically associated with T4-stage. SUVmax, SUVmean, and TLG did not result statistically associated with TRG (TRG1 or TRG1-2). MTV resulted statistically associated with TRG1-2 group (OR 2.9; 95% CI 1.2-7.1). Finally, no PET parameter was significantly associated with disease-free or overall survival.

CONCLUSION

Our results showed that baseline [18F] FDG-PET parameters correlated with tumor staging, and only MTV correlated with TRG 1-2. PET parameters failed to predict disease-free and overall survival after treatment completion. The results leave open to further studies the issue of identifying patients suitable for conservative approaches.

Radiomics analysis of pre-treatment [18F]FDG PET/CT for patients with metastatic colorectal cancer undergoing palliative systemic treatment

BACKGROUND

The aim of this study was to assess radiomics features on pre-treatment [¹⁸F]FDG positron emission tomography (PET) as potential biomarkers for response and survival in patients with metastatic colorectal cancer (mCRC).

METHODS

Patients with mCRC underwent [¹⁸F]FDG PET/computed tomography (CT) prior to first- or third-line palliative systemic treatment. Tumour lesions were semiautomatically delineated and standard uptake value (SUV), metabolically active tumour volume (MATV), total lesion glycolysis (TLG), entropy, area under the curve of the cumulative SUV-volume histogram (AUC-CSH), compactness and sphericity were obtained.

RESULTS

Lesions of 47 patients receiving third-line systemic treatment had higher SUV_max, SUV_peak, SUV_mean, MATV and TLG, and lower AUC-CSH, compactness and sphericity compared to 52 patients receiving first-line systemic treatment. Therefore, first- and third-line groups were evaluated separately. In the first-line group, anatomical changes on CT correlated negatively with TLG (ρ = 0.31) and MATV (ρ = 0.36), and positively with compactness (ρ = -0.27) and sphericity (ρ = -0.27). Patients without benefit had higher mean entropy (p = 0.021). Progression-free survival (PFS) and overall survival (OS) were worse with a decreased mean AUC [hazard ratio (HR) 0.86, HR 0.77] and increase in mean MATV (HR 1.15, HR 1.22), sum MATV (HR 1.14, HR 1.19), mean TLG (HR 1.16, HR 1.22) and sum TLG (HT1.12, HR1.18). In the third-line group, AUC-CSH correlated negatively with anatomical change (ρ = 0.21). PFS and OS were worse with an increased mean MATV (HR 1.27, HR 1.68), sum MATV (HR 1.35, HR 2.04), mean TLG (HR 1.29, HR 1.52) and sum TLG (HT 1.27, HR 1.80). SUV_max and SUV_peak negatively correlated with OS (HR 1.19, HR 1.21). Cluster analysis of the 10 radiomics features demonstrated no complementary value in identifying aggressively growing lesions or patients with impaired survival.

CONCLUSION

We demonstrated an association between improved clinical outcome and pre-treatment low tumour volume and heterogeneity as well as high sphericity on [¹⁸F]FDG PET. Future PET imaging research should include radiomics features that incorporate tumour volume and heterogeneity when correlating PET data with clinical outcome.

Radiomics in Oncological PET/CT: Clinical Applications

18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used for staging, evaluating treatment response, and predicting prognosis in malignant diseases. FDG uptake and volumetric PET parameters such as metabolic tumor volume have been used and are still used as conventional PET parameters to assess biological characteristics of tumors. However, in recent years, additional features derived from PET images by computational processing have been found to reflect intratumoral heterogeneity, which is related to biological tumor features, and to provide additional predictive and prognostic information, which leads to the concept of radiomics. In this review, we focus on recent clinical studies of malignant diseases that investigated intratumoral heterogeneity on PET/CT, and we discuss its clinical role in various cancers.

Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges

Radiomics is a recent area of research in precision medicine and is based on the extraction of a large variety of features from medical images. In the field of radiation oncology, comprehensive image analysis is crucial to personalization of treatments. A better characterization of local heterogeneity and the shape of the tumor, depicting individual cancer aggressiveness, could guide dose planning and suggest volumes in which a higher dose is needed for better tumor control. In addition, noninvasive imaging features that could predict treatment outcome from baseline scans could help the radiation oncologist to determine the best treatment strategies and to stratify patients as at low risk or high risk of recurrence. Nuclear medicine molecular imaging reflects information regarding biological processes in the tumor thanks to a wide range of radiotracers. Many studies involving ¹⁸F-fluorodeoxyglucose positron emission tomography suggest an added value of radiomics compared with the use of conventional PET metrics such as standardized uptake value for both tumor diagnosis and prediction of recurrence or treatment outcome. However, these promising results should not hide technical difficulties that still currently prevent the approach from being widely studied or clinically used. These difficulties mostly pertain to the variability of the imaging features as a function of the acquisition device and protocol, the robustness of the models with respect to that variability, and the interpretation of the radiomic models. Addressing the impact of the variability in acquisition and reconstruction protocols is needed, as is harmonizing the radiomic feature calculation methods, to ensure the reproducibility of studies in a multicenter context and their implementation in a clinical workflow. In this review, we explain the potential impact of positron emission tomography radiomics for radiation therapy and underline the various aspects that need to be carefully addressed to make the most of this promising approach.

Value of FDG-PET/CT Volumetry After Chemoradiotherapy in Rectal Cancer

BACKGROUND

Neoadjuvant chemoradiotherapy followed by an optimal surgery is the standard treatment for patients with locally advanced rectal cancer. FDG-PET/CT is commonly used as the modality for assessing the effect of chemoradiotherapy.

OBJECTIVE

The purpose of this study was to investigate whether PET/CT-based volumetry could contribute to the prediction of pathological complete response or prognosis after neoadjuvant chemoradiotherapy.

DESIGN

This was a retrospective cohort study.

SETTINGS

This study was conducted at a single research center.

PATIENTS

Ninety-one consecutive patients with locally advanced rectal cancer were enrolled between January 2005 and December 2015.

INTERVENTION

Patients underwent PET/CT before and after neoadjuvant chemoradiotherapy.

MAIN OUTCOME MEASURES

Maximum standardized uptake value and total lesion glycolysis on PET/CT before and after neoadjuvant chemoradiotherapy were calculated using isocontour methods. Correlations between these variables and clinicopathological factors and prognosis were assessed.

RESULTS

PET/CT-associated variables before chemoradiotherapy were not correlated with either clinicopathological factors or prognosis. Maximum standardized uptake value was associated with pathological complete response, but total lesion glycolysis was not. Maximum standardized uptake value correlated with ypT, whereas total lesion glycolysis correlated with both ypT and ypN. High total lesion glycolysis was associated with a considerably poorer prognosis; the 5-year recurrence rate was 65% and the 5-year mortality rate 42%, whereas in lesions with low total lesion glycolysis, these were 6% and 2%. On multivariate analysis, high total lesion glycolysis was an independent risk factor for recurrence (HR = 4.718; p = 0.04).

LIMITATIONS

The gain in fluoro-2-deoxy-D-glucose uptake may differ between scanners, thus the general applicability of this threshold should be validated.

CONCLUSIONS

In patients with locally advanced rectal cancer, high total lesion glycolysis after neoadjuvant chemoradiotherapy is strongly associated with a worse prognosis. Total lesion glycolysis after chemoradiotherapy may be a promising preoperative predictor of recurrence and death. See Video Abstract at http://links.lww.com/DCR/A464.

FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer

PURPOSE

The aim of this study was to investigate the prognostic value of baseline ¹⁸F-FDG PET/CT textural analysis in locally-advanced rectal cancer (LARC).

METHODS

Eighty-six patients with LARC underwent ¹⁸F-FDG PET/CT before treatment. Maximum and mean standard uptake values (SUVmax and SUVmean), metabolic tumoral volume (MTV), total lesion glycolysis (TLG), histogram-intensity features, as well as 11 local and regional textural features, were evaluated. The relationships of clinical, pathological and PET-derived metabolic parameters with disease-specific survival (DSS), disease-free survival (DFS) and overall survival (OS) were assessed by Cox regression analysis. Logistic regression was used to predict the pathological response by the Dworak tumor regression grade (TRG) in the 66 patients treated with neoadjuvant chemoradiotherapy (nCRT).

RESULTS

The median follow-up of patients was 41 months. Seventeen patients (19.7%) had recurrent disease and 18 (20.9 %) died, either due to cancer progression (n = 10) or from another cause while in complete remission (n = 8). DSS was 95% at 1 year, 93% at 2 years and 87% at 4 years. Weight loss, surgery and the texture parameter coarseness were significantly associated with DSS in multivariate analyses. DFS was 94 % at 1 year, 86 % at 2 years and 79 % at 4 years. From a multivariate standpoint, tumoral differentiation and the texture parameters homogeneity and coarseness were significantly associated with DFS. OS was 93% at 1 year, 87% at 2 years and 79% after 4 years. cT, surgery, SUVmean, dissimilarity and contrast from the neighborhood intensity-difference matrix (contrast_NGTDM) were significantly and independently associated with OS. Finally, RAS-mutational status (KRAS and NRAS mutations) and TLG were significant predictors of pathological response to nCRT (TRG 3-4).

CONCLUSION

Textural analysis of baseline ¹⁸F-FDG PET/CT provides strong independent predictors of survival in patients with LARC, with better predictive power than intensity- and volume-based parameters. The utility of such features, especially coarseness, should be confirmed by larger clinical studies before considering their potential integration into decisional algorithms aimed at personalized medicine.

Characterization of PET/CT images using texture analysis: the past, the present… any future?

After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.

(18)F-FDG PET/CT imaging in rectal cancer: relationship with the RAS mutational status

OBJECTIVE

Treating metastatic colorectal cancer with anti-EGFR monoclonal antibodies is recommended only for patients whose tumour does not harbour mutations of KRAS or NRAS. The aim of this study was to investigate the biology of rectal cancers and specifically to evaluate the relationship between fluorine-18 fludeoxyglucose ((18)F-FDG) positron emission tomography (PET) intensity and heterogeneity parameters and their mutational status.

METHODS

151 patients with newly diagnosed rectal cancer were included in this retrospective study. All patients underwent a baseline (18)F-FDG PET/CT within a median time interval of 27 days of tumour tissue sampling, which was performed before any treatment. Standardized uptake values (SUVs), volume-based parameters and texture analysis were studied. We retrospectively performed KRAS genotyping on codons 12, 13, 61, 117 and 146, NRAS genotyping on codons 12, 13 and 61 and BRAF on codon 600. Associations between PET/CT parameters and the mutational status were assessed using univariate and multivariate analysis.

RESULTS

83 (55%) patients had an RAS mutation: 74 KRAS and 9 NRAS, while 68 patients had no mutation (wild-type tumours). No patient had BRAF mutation. First-order features based on intensity histogram analysis were significantly associated with RAS mutations: maximum SUV (SUVmax) (p-value = 0.002), mean SUV (p-value = 0.006), skewness (p-value = 0.049), SUV standard deviation (p-value = 0.001) and SUV coefficient of variation (SUVcov) (p-value = 0.001). Both SUVcov and SUVmax showed an area under the curve of 0.65 with sensitivity of 56% and 69%, respectively, and specificity of 64% and 52%, respectively. None of the volume-based (metabolic tumour volume and total lesion glycolysis), nor local or regional textural features were associated with the presence of RAS mutations.

CONCLUSION

Although rectal cancers with KRAS or NRAS mutations display a significantly higher glucose metabolism than wild-type cancers, the accuracy of the currently proposed quantitative metrics extracted from (18)F-FDG PET/CT is not sufficiently high for playing a meaningful clinical role.

ADVANCES IN KNOWLEDGE

RAS-mutated rectal cancers have a significantly higher glucose metabolism. However, the accuracy of (18)F-FDG PET/CT quantitative metrics is not as such as the technique could play a clinical role.