Use of new imaging techniques to predict tumour response to therapy

Potential of MRI in Assessing Treatment Response After Neoadjuvant Radiation Therapy Treatment in Breast Cancer Patients: A Scoping Review

The objective of this scoping review is to evaluate the potential of Magnetic Resonance Imaging (MRI) and to determine which of the available MRI techniques reported in the literature are the most promising for assessing treatment response in breast cancer patients following neoadjuvant radiotherapy (NRT). Ovid Medline, Embase, CINAHL, and Cochrane databases were searched to identify relevant studies published from inception until March 13, 2023. After primary selection, 2 reviewers evaluated each study using a standardized data extraction template, guided by set inclusion and exclusion criteria. A total of 5 eligible studies were selected. The positive and negative predictive values for MRI predicting pathological complete response across the studies were 67% to 88% and 76% to 85%, respectively. MRI's potential in assessing postradiotherapy tumor sizes was greater for volume measurements than uni-dimensional longest diameter measurements; however, overestimation in surgical tumor sizes was observed. Apparent diffusion coefficient (ADC) values and Time to Enhance (TTE) was seen to increase post-NRT, with a notable difference between responders and nonresponders at 6 months, indicating a potential role in assessing treatment response. In conclusion, this review highlights tumor volume measurements, ADC, and TTE as promising MRI metrics for assessing treatment response post-NRT in breast cancer. However, further research with larger cohorts is needed to confirm their utility. If MRI can accurately identify responders from nonresponders to NRT, it could enable a more personalized and tailored treatment approach, potentially minimizing radiation therapy related toxicity and enhancing cosmetic outcomes.

Whole tumor based texture analysis of magnetic resonance diffusion imaging for colorectal liver metastases: A prospective study for diffusion model comparison and early response biomarker

PURPOSE

To evaluate and compare the diagnostic value of diffusion-related texture analysis parameters obtained from various magnetic resonance diffusion models as early predictors of the clinical response to chemotherapy in patients with colorectal liver metastases (CRLM).

METHODS

Patients (n = 145) with CRLM were prospectively and consecutively enrolled and scanned using diffusion-weighted imaging (DWI)-magnetic resonance imaging (MRI)/intravoxel incoherent motion (IVIM)/diffusion kurtosis imaging (DKI) before (baseline) and two-three weeks after (follow-up) commencing chemotherapy. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1). The histogram and texture parameters of each diffusion-related parametric map were analysed between the responding and non-responding groups, screened using LASSO, and fitted with binary logistic regression models. The diagnostic efficacy of each model in the early prediction of CRLM was analysed, and the corresponding receiver operating characteristic (ROC) curve was drawn. The area under the curve (AUC) and 95% confidence intervals (CI) were calculated.

RESULTS

Of the 145 analysed patients, 69 were in the responding group and 76 were in the non-responding group. Among all models, the difference value based on the histogram and texture features of the DKI-derived parameters performed best for the early prediction of CRLM treatment efficacy. The AUC of the DKI model in the validation set reached 0.795 (95% CI 0.652-0.938). Among the IVIM-derived parameters, the difference model based on D and D* performed best, and the AUC in the validation set reached 0.737 (95% CI 0.586-0.889). Finally, in the DWI sequence, the model comprising baseline features performed the best, with an AUC of 0.699 (95% CI 0.537-0.86) in the validation set.

CONCLUSIONS

Baseline DWI parameters and follow-up changes in IVIM and DKI parameters predicted the chemotherapeutic response in patients with CRLM. In addition, as very early predictors, DKI-derived parameters were more effective than DWI- and IVIM-related parameters, in which changes in D-parameters performed best.

Non-invasive intravoxel incoherent motion MRI in prediction of histopathological response to neoadjuvant chemotherapy and survival outcome in osteosarcoma at the time of diagnosis

BACKGROUND

Early prediction of response to neoadjuvant chemotherapy (NACT) is important to aid personalized treatment in osteosarcoma. Diffusion-weighted Intravoxel Incoherent Motion (IVIM) MRI was used to evaluate the predictive value for response to NACT and survival outcome in osteosarcoma.

METHODS

Total fifty-five patients with biopsy-proven osteosarcoma were recruited prospectively, among them 35 patients were further analysed. Patients underwent 3 cycles of NACT (Cisplatin + Doxorubicin) followed by surgery and response adapted adjuvant chemotherapy. Treatment outcomes were histopathological response to NACT (good-response ≥ 50% necrosis and poor-response < 50% necrosis) and survival outcome (event-free survival (EFS) and overall survival (OS)). IVIM MRI was acquired at 1.5T at baseline (t0), after 1-cycle (t1) and after 3-cycles (t2) of NACT. Quantitative IVIM parameters (D, D*, f & D*.f) were estimated using advanced state-of-the-art spatial penalty based IVIM analysis method bi-exponential model with total-variation penalty function (BETV) at 3 time-points and histogram analysis was performed.

RESULTS

Good-responders: Poor-responders ratio was 13 (37%):22 (63%). EFS and OS were 31% and 69% with 16.27 and 25.9 months of median duration respectively. For predicting poor-response to NACT, IVIM parameters showed AUC = 0.87, Sensitivity = 86%, Specificity = 77% at t0, and AUC = 0.96, Sensitivity = 86%, Specificity = 100% at t1. Multivariate Cox regression analysis showed smaller tumour volume (HR = 1.002, p = 0.001) higher ADC-25^th-percentile (HR = 0.047, p = 0.005) & D-Mean (HR = 0.1, p = 0.023) and lower D*-Mean (HR = 1.052, p = 0.039) were independent predictors of longer EFS (log-rank p-values: 0.054, 0.0034, 0.0017, 0.0019 respectively) and non-metastatic disease (HR = 4.33, p < 10^-3), smaller tumour-volume (HR = 1.001, p = 0.042), lower D*-Mean (HR = 1.045, p = 0.056) and higher D*.f-skewness (HR = 0.544, p = 0.048) were independent predictors of longer OS (log-rank p-values: < 10^-3, 0.07, < 10^-3, 0.019 respectively).

CONCLUSION

IVIM parameters obtained with a 1.5T scanner along with novel BETV method and their histogram analysis indicating tumour heterogeneity were informative in characterizing NACT response and survival outcome in osteosarcoma.

The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI

Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters. The under sampled data were reconstructed at 5 s temporal resolution using the data-driven low-rank temporal model for MOCCO, compressed sensing with temporal total variation (CS-TV) and more conventional low-rank reconstruction (PCB). Our results demonstrated that MOCCO was able to recover curves with K^trans values ranging from 0.01 to 0.8 min⁻¹ and fixed V_e = 0.3, where the fitted results are within a 10% bias error range. MOCCO reconstruction showed less impact on the selection of different temporal models than conventional low-rank reconstruction and the greater error was observed with PCB. CS-TV showed overall underestimation in both K^trans and V_e. For the Monte-Carlo simulations, MOCCO was found to provide the most accurate reconstruction results for curves with intermediate lesion kinetics in the presence of noise. Initial in vivo experiences are reported in one patient volunteer. Overall, MOCCO was able to provide reconstructed time-series data that resulted in a more accurate measurement of PK parameters than PCB and CS-TV.

SNMMI Clinical Trials Network Research Series for Technologists: Clinical Research Primer- Use of Imaging Agents in Therapeutic Drug Development and Approval

The process of bringing a new drug to market is complex and has recently necessitated a new drug discovery paradigm for the pharmaceutical industry that is both more efficient and more economical. Key to this has been the increasing use of nuclear medicine and molecular imaging to support drug discovery efforts by answering critical questions on the pathway for development and approval of a new therapeutic drug. Some of these questions include: (i) Does the new drug reach its intended target in the body at sufficient levels to effectively treat or diagnose disease without unacceptable toxicity? (ii) How is the drug absorbed, metabolized, and excreted? (iii) What is the effective dose in humans? To conduct the appropriate imaging studies to answer such questions, pharmaceutical companies are increasingly partnering with molecular imaging departments. Nuclear medicine technologists are critical to this process as they perform scans to collect the qualitative and quantitative imaging data used to measure study endpoints. This article describes preclinical and clinical research trials and provides an overview of the different ways that radiopharmaceuticals are used to answer critical questions during therapeutic drug development.

Volumetric apparent diffusion coefficient (ADC) histogram metrics as imaging biomarkers for pretreatment predicting response to fertility-sparing treatment in patients with endometrial cancer

OBJECTIVES

To investigate the feasibility of volumetric apparent diffusion coefficient (ADC) histogram analysis for prediction of fertility-sparing treatment (FST) response in patients with endometrial cancer (EC).

METHODS

Pretreatment data of 54 EC patients with FST were retrospectively analyzed. Treatment response at each follow-up was pathologically evaluated. The associations of ADC histogram metrics (volume, minADC, maxADC, meanADC; 10th, 25th, 50th, 75th and 90th ADC percentiles; skewness; kurtosis) and baseline clinical characteristics with complete response (CR) at the second and third follow-ups, two-consecutive CR, and recurrence at the final follow-up were evaluated by uni- and multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was used for diagnostic performance evaluation.

RESULTS

Compared with non-CR patients, CR patients had significantly higher minADC and 10th and 25th ADC percentiles at the second follow-up (P = 0.008, 0.039, and 0.034, respectively) and higher minADC, older age, lower HE4 level, and higher overweight rate at the third follow-up (P = 0.001, 0.040, 0.021, and 0.004, respectively). Patients with two-consecutive CR had a significantly higher minADC than those without (P = 0.018). There was no association between ADC metrics or clinical characteristics and recurrence (all P > 0.05). MinADC yielded the largest AUC in predicting CR (0.688 and 0.735 at the second and third follow-up, respectively) and the presence of two-consecutive CR (0.753). When combined with patient age and HE4 level, the prediction of CR could be further improved at the third follow-up, with an AUC of 0.786.

CONCLUSION

Pretreatment minADC could be a potential imaging biomarker for predicting FST response. Clinical characteristics may have incremental value to minADC in predicting CR.

Noninvasive Method for Predicting the Expression of Ki67 and Prognosis in Non-Small-Cell Lung Cancer Patients: Radiomics

Purpose

In this study, we aimed to develop and validate a noninvasive method based on radiomics to evaluate the expression of Ki67 and prognosis of patients with non-small-cell lung cancer (NSCLC). Patients and Methods. A total of 120 patients with NSCLC were enrolled in this retrospective study. All patients were randomly assigned to a training dataset (n = 85) and test dataset (n = 35). According to the preprocessed F-FDG PET/CT image of each patient, a total of 384 radiomics features were extracted from the segmentation of regions of interest (ROIs). The Spearman correlation test and least absolute shrinkage and selection operator (LASSO), after normalization on the features matrix, were applied to reduce the dimensionality of the features. Furthermore, multivariable logistic regression analysis was used to propose a model for predicting Ki67. The survival curve was used to explore the prognostic significance of radiomics features.

Results

A total of 62 Ki67 positive patients and 58 Ki67 negative patients formed the training set and test training dataset and test dataset. Radiomics signatures showed good performance in predicting the expression of Ki67 with AUCs of 0.86 (training dataset) and 0.85 (test dataset). Validation and calibration showed that the radiomics had a strong predictive power in patients with NSCLC survival, which was significantly close to the effect of Ki67 expression on the survival of patients with NSCLC.

Conclusion

Radiomics signatures based on preoperative F-FDG PET/CT could distinguish the expression of Ki67, which also had a strong predictive performance for the survival outcome.

Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy

Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.

One-stop local and whole-body staging of children with cancer

Accurate staging and re-staging of cancer in children is crucial for patient management. Currently, children with a newly diagnosed cancer must undergo a series of imaging tests, which are stressful, time-consuming, partially redundant, expensive, and can require repetitive anesthesia. New approaches for pediatric cancer staging can evaluate the primary tumor and metastases in a single session. However, traditional one-stop imaging tests, such as CT and positron emission tomography (PET)/CT, are associated with considerable radiation exposure. This is particularly concerning for children because they are more sensitive to ionizing radiation than adults and they live long enough to experience secondary cancers later in life. In this review article we discuss child-tailored imaging tests for tumor detection and therapy response assessment - tests that can be obtained with substantially reduced radiation exposure compared to traditional CT and PET/CT scans. This includes diffusion-weighted imaging (DWI)/MRI and integrated [F-18]2-fluoro-2-deoxyglucose (18F-FDG) PET/MRI scans. While several investigators have compared the value of DWI/MRI and 18F-FDG PET/MRI for staging pediatric cancer, the value of these novel imaging technologies for cancer therapy monitoring has received surprisingly little attention. In this article, we share our experiences and review existing literature on this subject.

Application of diffusion-weighted whole-body MRI for response monitoring in multiple myeloma after chemotherapy: a systematic review and meta-analysis

OBJECTIVE

Myeloma Response Assessment and Diagnosis System recently published provides a framework for the standardised interpretation of DW-WBMRI in response assessment of multiple myeloma (MM) based on expert opinion. However, there is a lack of meta-analysis providing higher-level evidence to support the recommendations. In addition, some disagreement exists in the literature regarding the effect of timing and lesion subtypes on apparent diffusion coefficient (ADC) value changes post-treatment.

METHOD

Medline, Cochrane and Embase were searched from inception to 20th July 2021, using terms reflecting multiple myeloma and DW-WBMRI. Using PRISMA reporting guidelines, data were extracted by two investigators. Quality was assessed by the Quality Assessment of Diagnostic Accuracy Studies-2 method.

RESULTS

Of the 74 papers screened, 10 studies were included comprising 259 patients (127 males and 102 females) and 1744 reported lesions. Responders showed a significant absolute ADC change of 0.21×10^-3 mm/s² (95% CI, 0.01-0.41) with little evidence of heterogeneity (Cochran Q, p = 0.12, I² = 45%) or publication bias (p = 0.737). Non-responders did not show a significant absolute difference in ADC (0.06 ×10^-3 mm/s², 95% CI, -0.07 to 0.19). A percentage ADC increase of 34.78% (95% CI, 10.75-58.81) was observed in responders. Meta-regression showed an inverse trend between ADC increases and time since chemotherapy initiation which did not reach statistical significance (R² = 20.46, p = 0.282).

CONCLUSIONS

This meta-analysis supports the use of the DW-WBMRI as an imaging biomarker for response assessment. More evidence is needed to further characterise ADC changes by lesion subtypes over time.

KEY POINTS

• In multiple myeloma patients who received chemotherapy, responders have a significant absolute increase in ADC values that is not seen in non-responders. • A 35% increase in ADC from baseline values is found to classify response post-induction chemotherapy which corroborates with expert opinion from the Myeloma Response Assessment and Diagnosis System. • More evidence is needed to further characterise ADC changes by lesion subtypes over time after induction of therapy.

Update in Imaging Evaluation of Bone and Soft Tissue Sarcomas

A evolução na avaliação por imagens dos sarcomas musculoesqueléticos contribuiu para melhora significativa no prognóstico e na sobrevida dos portadores destas neoplasias. A caracterização precisa destas lesões, mediante utilização das modalidades de imagem mais adequadas a cada condição clínica apresentada, é de suma importância no delineamento da abordagem terapêutica a ser instituída, com impacto direto sobre os desfechos clínicos. O presente artigo busca atualizar o leitor a propósito das metodologias de imagem no contexto da avaliação local e sistêmica dos sarcomas ósseos e das partes moles.

Joint Imaging Platform for Federated Clinical Data Analytics

PURPOSE

Image analysis is one of the most promising applications of artificial intelligence (AI) in health care, potentially improving prediction, diagnosis, and treatment of diseases. Although scientific advances in this area critically depend on the accessibility of large-volume and high-quality data, sharing data between institutions faces various ethical and legal constraints as well as organizational and technical obstacles.

METHODS

The Joint Imaging Platform (JIP) of the German Cancer Consortium (DKTK) addresses these issues by providing federated data analysis technology in a secure and compliant way. Using the JIP, medical image data remain in the originator institutions, but analysis and AI algorithms are shared and jointly used. Common standards and interfaces to local systems ensure permanent data sovereignty of participating institutions.

RESULTS

The JIP is established in the radiology and nuclear medicine departments of 10 university hospitals in Germany (DKTK partner sites). In multiple complementary use cases, we show that the platform fulfills all relevant requirements to serve as a foundation for multicenter medical imaging trials and research on large cohorts, including the harmonization and integration of data, interactive analysis, automatic analysis, federated machine learning, and extensibility and maintenance processes, which are elementary for the sustainability of such a platform.

CONCLUSION

The results demonstrate the feasibility of using the JIP as a federated data analytics platform in heterogeneous clinical information technology and software landscapes, solving an important bottleneck for the application of AI to large-scale clinical imaging data.

Prognostic and therapeutic evaluation of nasopharyngeal carcinoma by dynamic contrast-enhanced (DCE), diffusion-weighted (DW) magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS)

Nasopharyngeal carcinoma (NPC) is an aggressive head and neck malignancy, and radiotherapy (with or without chemotherapy) is the primary treatment modality. Reliable tumour assessment during the treatment phase, which can portend the efficacy of radiotherapy and early identification of potential treatment failure in radioresistant disease, has been implicit for better cancer management. Technological advancement in the last decade has fostered the development of functional magnetic resonance imaging (fMRI) techniques into a promising tool for diagnostic and therapeutic assessments in head and neck cancer. Apart from conventional morphological assessment, early detection of the physiological environment by fMRI allows a more thorough investigation in monitoring tumour response. This article discusses the relevant fMRI utilities in NPC as an early prognostic and monitoring tool for treatment. Challenges and future developments of fMRI in radiation oncology are also discussed.

Imaging biomarkers for evaluating tumor response: RECIST and beyond

Response Evaluation Criteria in Solid Tumors (RECIST) is the gold standard for assessment of treatment response in solid tumors. Morphologic change of tumor size evaluated by RECIST is often correlated with survival length and has been considered as a surrogate endpoint of therapeutic efficacy. However, the detection of morphologic change alone may not be sufficient for assessing response to new anti-cancer medication in all solid tumors. During the past fifteen years, several molecular-targeted therapies and immunotherapies have emerged in cancer treatment which work by disrupting signaling pathways and inhibited cell growth. Tumor necrosis or lack of tumor progression is associated with a good therapeutic response even in the absence of tumor shrinkage. Therefore, the use of unmodified RECIST criteria to estimate morphological changes of tumor alone may not be sufficient to estimate tumor response for these new anti-cancer drugs. Several studies have reported the low reliability of RECIST in evaluating treatment response in different tumors such as hepatocellular carcinoma, lung cancer, prostate cancer, brain glioma, bone metastasis, and lymphoma. There is an increased need for new medical imaging biomarkers, considering the changes in tumor viability, metabolic activity, and attenuation, which are related to early tumor response. Promising imaging techniques, beyond RECIST, include dynamic contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI), diffusion-weight imaging (DWI), magnetic resonance spectroscopy (MRS), and ¹⁸ F-fluorodeoxyglucose (FDG) positron emission tomography (PET). This review outlines the current RECIST with their limitations and the new emerging concepts of imaging biomarkers in oncology.

Transpathology: molecular imaging-based pathology

Pathology is the medical specialty concerned with the study of the disease nature and causes, playing a key role in bridging basic researches and clinical medicine. In the course of development, pathology has significantly expanded our understanding of disease, and exerted enormous impact on the management of patients. However, challenges facing pathology, the inherent invasiveness of pathological practice and the persistent concerns on the sample representativeness, constitute its limitations. Molecular imaging is a noninvasive technique to visualize, characterize, and measure biological processes at the molecular level in living subjects. With the continuous development of equipment and probes, molecular imaging has enabled an increasingly precise evaluation of pathophysiological changes. A new pathophysiology visualization system based on molecular imaging is forming and shows the great potential to reform the pathological practice. Several improvements in "trans-," including trans-scale, transparency, and translation, would be driven by this new kind of pathological practice. Pathological changes could be evaluated in a trans-scale imaging mode; tissues could be transparentized to better present the underlying pathophysiological information; and the translational processes of basic research to the clinical practice would be better facilitated. Thus, transpathology would greatly facilitate in deciphering the pathophysiological events in a multiscale perspective, and supporting the precision medicine in the future.

Integration of functional imaging in brachytherapy

Functional imaging allows the evaluation of numerous biological properties that could be considered at all steps of the therapeutic management of patients treated with brachytherapy. Indeed, it enables better initial staging of the disease, and some parameters may also be used as predictive biomarkers for treatment response, allowing better selection of patients eligible for brachytherapy. It may also improve the definition of target volumes with the aim of dose escalations by dose-painting. Finally, it could be useful during the follow-up to assess response to treatment. In this review, we report how functional imaging is integrated at the present time during the brachytherapy procedure, and what are its potential future contributions in the main tumour locations where brachytherapy is recommended. Functional imaging has great potential in the contact of brachytherapy, but still, several issues remain to be resolved before integrating it into clinical practice, especially as a biomarker or in dose painting strategies.

Methylation of PAX1 gene promoter in the prediction of concurrent chemo-radiotherapy efficacy in cervical cancer

Objectives: Cervical cancer is the fourth leading cause of cancer death among women worldwide. In currently, aberrant methylation of PAX1 is found in variety of solid tumors, including cervical cancer. In addition, the role of PAX1 gene methylation in cervical cancer and precancerous lesions screening has been confirmed in previous study. Here, we evaluated the predictive value of PAX1 methylation in concurrent chemo-radiotherapy (CCRT) outcomes in cervical cancer.

Methods: This study enrolled 82 cervical cancer patients from August 2018 to August 2020. We compared the clinical results between different PAX1 methylation status. Hyper-methylation patients were subjects to MRI and quantitative methylation-specific PCR (QMSP) for PAX1 before, in the middle, immediately after, 1 month and 3 months after CCRT. The changes in PAX1 methylation during CCRT were analyzed.

Results: The lower PAX1 methylation status were related to a poor tumor response. Based on the MRI findings three months post-treatment, the hypermethylated patients were classified into the complete response (CR; n=50) and partial remission (PR; n=18) groups. The average PAX1 △Cp value of CR and PR groups before radiotherapy was 5.08±1.98 and 4.32±2.00 respectively, and after concurrent chemo-radiotherapy was significantly increased to 17.35±4.96 and 16.99±6.17, respectively (P<0.05). Furthermore, the PAX1 △Cp value between CR and PR groups were significantly different at mid-treatment and performed well in predicting short-term efficacy (AUC 0.84) in this period, and its sensitivity and specificity for predicting PR were 0.72 and 0.88, respectively.

Conclusion: The PAX1 methylation level may predict the sensitivity and efficacy of CCRT in cervical cancer.

Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging

The efficacy of MRI-based statistical texture analysis (TA) in predicting chemotherapy response among patients with osteosarcoma was assessed. Forty patients (male: female = 31:9; age = 17.2 ± 5.7 years) with biopsy-proven osteosarcoma were analyzed in this prospective study. Patients were scheduled for three cycles of neoadjuvant chemotherapy (NACT) and diffusion-weighted MRI acquisition at three time points: at baseline (t0), after the first NACT (t1) and after the third NACT (t2) using a 1.5 T scanner. Eight patients (nonsurvivors) died during NACT while 34 patients (survivors) completed the NACT regimen followed by surgery. Histopathological evaluation was performed in the resected tumor to assess NACT response (responder [≤50% viable tumor] and nonresponder [>50% viable tumor]) and revealed nonresponder: responder = 20:12. Apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters, diffusion coefficient (D), perfusion coefficient (D*) and perfusion fraction (f) were evaluated. A total of 25 textural features were evaluated on ADC, D, D* and f parametric maps and structural T1-weighted (T1W) and T2-weighted (T2W) images in the entire tumor volume using 3D TA methods gray-level cooccurrence matrix (GLCM), neighborhood gray-tone-difference matrix (NGTDM) and run-length matrix (RLM). Receiver-operating-characteristic curve analysis was performed on the selected textural feature set to assess the role of TA features (a) as marker(s) of tumor aggressiveness leading to mortality at baseline and (b) in predicting the NACT response among survivors in the course of treatment. Findings showed that the NGTDM features coarseness, busyness and strength quantifying tumor heterogeneity in D, D* and f maps and T1W and T2W images were useful markers of tumor aggressiveness in identifying the nonsurvivor group (area-under-the-curve [AUC] = 0.82-0.88) at baseline. The GLCM features contrast and correlation, NGTDM features contrast and complexity and RLM feature short-run-low-gray-level-emphasis quantifying homogeneity/terogeneity in tumor were effective markers for predicting chemotherapeutic response using D (AUC = 0.80), D* (AUC = 0.80) and T2W (AUC = 0.70) at t0, and D* (AUC = 0.80) and f (AUC = 0.70) at t1. 3D statistical TA features might be useful as imaging-based markers for characterizing tumor aggressiveness and predicting chemotherapeutic response in patients with osteosarcoma.

Menstruation: science and society

Women's health concerns are generally underrepresented in basic and translational research, but reproductive health in particular has been hampered by a lack of understanding of basic uterine and menstrual physiology. Menstrual health is an integral part of overall health because between menarche and menopause, most women menstruate. Yet for tens of millions of women around the world, menstruation regularly and often catastrophically disrupts their physical, mental, and social well-being. Enhancing our understanding of the underlying phenomena involved in menstruation, abnormal uterine bleeding, and other menstruation-related disorders will move us closer to the goal of personalized care. Furthermore, a deeper mechanistic understanding of menstruation-a fast, scarless healing process in healthy individuals-will likely yield insights into a myriad of other diseases involving regulation of vascular function locally and systemically. We also recognize that many women now delay pregnancy and that there is an increasing desire for fertility and uterine preservation. In September 2018, the Gynecologic Health and Disease Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development convened a 2-day meeting, "Menstruation: Science and Society" with an aim to "identify gaps and opportunities in menstruation science and to raise awareness of the need for more research in this field." Experts in fields ranging from the evolutionary role of menstruation to basic endometrial biology (including omic analysis of the endometrium, stem cells and tissue engineering of the endometrium, endometrial microbiome, and abnormal uterine bleeding and fibroids) and translational medicine (imaging and sampling modalities, patient-focused analysis of menstrual disorders including abnormal uterine bleeding, smart technologies or applications and mobile health platforms) to societal challenges in health literacy and dissemination frameworks across different economic and cultural landscapes shared current state-of-the-art and future vision, incorporating the patient voice at the launch of the meeting. Here, we provide an enhanced meeting report with extensive up-to-date (as of submission) context, capturing the spectrum from how the basic processes of menstruation commence in response to progesterone withdrawal, through the role of tissue-resident and circulating stem and progenitor cells in monthly regeneration-and current gaps in knowledge on how dysregulation leads to abnormal uterine bleeding and other menstruation-related disorders such as adenomyosis, endometriosis, and fibroids-to the clinical challenges in diagnostics, treatment, and patient and societal education. We conclude with an overview of how the global agenda concerning menstruation, and specifically menstrual health and hygiene, are gaining momentum, ranging from increasing investment in addressing menstruation-related barriers facing girls in schools in low- to middle-income countries to the more recent "menstrual equity" and "period poverty" movements spreading across high-income countries.

Three-dimension amide proton transfer MRI of rectal adenocarcinoma: correlation with pathologic prognostic factors and comparison with diffusion kurtosis imaging

OBJECTIVES

To investigate the utility of 3D amide proton transfer (APT) MRI in predicting pathologic factors for rectal adenocarcinoma, in comparison with diffusion kurtosis imaging.

METHODS

Sixty-one patients with rectal adenocarcinoma were enrolled in this prospective study. 3D APT and diffusion kurtosis imaging (DKI) were performed. Mean APT-weighted signal intensity (APTw SI), mean kurtosis (MK), mean diffusivity (MD), and ADC values of tumors were calculated on these maps. Pathological analysis included WHO grades, pT stages, pN stages, and extramural venous invasion (EMVI) status. Student's t test, Spearman correlation, and receiver operating characteristics (ROC) analysis were used for statistical analysis.

RESULTS

High-grade rectal adenocarcinoma showed significantly higher mean APTw SI and MK values (2.771 ± 0.384 vs 2.108 ± 0.409, 1.167 ± 0.216 vs 1.045 ± 0.175, respectively; p < 0.05). T3 rectal adenocarcinoma demonstrated higher mean APTw SI and MK than T2 tumors (2.433 ± 0.467 vs 1.900 ± 0.302, p < 0.05). No kurtosis, diffusivity, and ADC differences were found between T2 and T3 tumors. Tumors with lymph node metastasis and EMVI involvement showed significantly higher mean APTw SI, MK. No difference was found in diffusivity and ADC between pN0 and pN1-2 groups, and EMVI-negative and EMVI-positive statuses. Mean APTw SI exhibited a significantly high positive correlation with WHO grades, demonstrating 92.31% sensitivity and 79.17% specificity for distinguishing low- from high-grade rectal adenocarcinoma, providing a better diagnostic capacity than MK, MD, and mean ADC values.

CONCLUSION

3D-APT could serve as a non-invasive biomarker for evaluating prognostic factors of rectal adenocarcinoma.

KEY POINTS

• Mean APTw SI was significantly higher in high-grade compared to low-grade rectal adenocarcinoma. • Mean APTw SI was significantly higher in T3 stage rectal adenocarcinoma, with lymph node metastasis, or in EMVI-positive status. • APTw SI exhibited greater diagnostic capability in discriminating low-grade from high-grade rectal adenocarcinoma, compared with kurtosis, diffusivity, and ADC.

Treatment of stage III non-small cell lung cancer in the era of immunotherapy: pathological complete response to neoadjuvant pembrolizumab and chemotherapy

Non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancers. The expected 5-year survival of stage III NSCLC ranges from 13% to 36% for stage III. Due to the heterogeneity and poor efficacy of stage III patients, there is great controversy on how to optimize the therapy strategy. Immunotherapy is providing better clinical efficacy to more NSCLC patients, and is rapidly extending its range of care from advanced stage to locally advanced stage and early stage NSCLC. Due to the patient’s strong treatment intention, drug availability, and a few encouraging results from clinical trials (NADIM, NCT02716038, etc.), the authors observed a case of stage III NSCLC that achieved complete remission after receiving neoadjuvant chemotherapy combined with immunotherapy. In view of such a satisfactory result in neoadjuvant therapy, this article discusses how comprehensive treatment for stage III NSCLC patients may be conducted and the manner in which various therapeutic techniques can be mastered in the era of immunotherapy. Immunotherapy has opened the exploratory space for finding resolutions to numerous challenges of treating stage III NSCLC. Further clinical studies and exploration of personalized treatment, guided by imaging data, and clinical and pathological biomarkers are imperative for the benefit of these patients.

Radiomics: an overview in lung cancer management-a narrative review

Radiomics is a novel approach for optimizing the analysis massive data from medical images to provide auxiliary guidance in clinical issues. Quantitative feature extraction is one of the critical steps of radiomics. The association between radiomics features and the clinicopathological information of diseases can be identified by several statistics methods. For instance, although significant progress has been made in the field of lung cancer, too many questions remain, especially for the individualized decisions. Radiomics offers a new tool to encode the characteristics of lung cancer which is the leading cause of cancer-related deaths worldwide. Here, we reviewed the workflow and clinical utility of radiomics in lung cancer management, including pulmonary nodules detection, classification, histopathology and genetics evaluation, clinical staging, therapy response, and prognosis prediction. Most of these studies showed positive results, indicating the potential value of radiomics in clinical practice. The implementation of radiomics is both feasible and invaluable, and has aided clinicians in ascertaining the nature of a disease with greater precision. However, it should be noted that radiomics in its current state cannot completely replace the work of therapists or tissue examination. The potential future trends of this modality were also remarked. More efforts are needed to overcome the limitations identified above in order to facilitate the widespread application of radiomics in the reasonably near future.

Predictive Value of Early Post-Treatment Diffusion-Weighted MRI for Recurrence or Tumor Progression of Head and Neck Squamous Cell Carcinoma Treated with Chemo-Radiotherapy

Aims: To investigate the predictive capacity of early post-treatment diffusion-weighted magnetic resonance imaging (MRI) for recurrence or tumor progression in patients with no tumor residue after chemo-radiotherapy (CRT) for head and neck squamous cell carcinoma, and, to assess the predictive capacity of pre-treatment diffusion-weighted MRI for persistent tumor residue post-CRT. Materials and Method: A single center cohort study was performed in one French hospital. All patients with squamous cell carcinoma receiving CRT (no surgical indication) were included. Two diffusion-weighted MRI were performed: one within 8 days before CRT and one 3 months after completing CRT with determination of median tumor apparent diffusion coefficient (ADC). Main outcome: The primary endpoint was progression-free survival. Results: 59 patients were included prior to CRT and 46 (78.0%) completed CRT. A post-CRT tumor residue was found in 19/46 (41.3%) patients. In univariate analysis, initial ADC was significantly lower in patients with residue post CRT (0.56 ± 0.11 versus 0.79 ± 0.13; p < 0.001). When initial ADC was dichotomized at the median, initial ADC lower than 0.7 was significantly more frequent in patients with residue post CRT (73.7% versus 11.1%, p < 0.0001). In multivariate analysis, only initial ADC lower than 0.7 was significantly associated with tumor residue (OR = 22.6; IC [4.9–103.6], p < 0.0001). Among 26 patients without tumor residue after CRT and followed up until 12 months, 6 (23.1%) presented recurrence or progression. Only univariate analysis was performed due to a small number of events. The only factor significantly associated with disease progression or early recurrence was the delta ADC (p = 0.0009). When ADC variation was dichotomized at the median, patients with ADC variation greater than 0.7 had time of disease-free survival significantly longer than patients with ADC variation lower than 0.7 (377.5 [286–402] days versus 253 [198–370], p < 0.0001). Conclusion and relevance: Diffusion-weighted MRI could be a technique that enables differentiation of patients with high potential for early recurrence for whom intensive post-CRT monitoring is mandatory. Prospective studies with more inclusions would be necessary to validate our results.

Radiomics with artificial intelligence: a practical guide for beginners

Radiomics is a relatively new word for the field of radiology, meaning the extraction of a high number of quantitative features from medical images. Artificial intelligence (AI) is broadly a set of advanced computational algorithms that basically learn the patterns in the data provided to make predictions on unseen data sets. Radiomics can be coupled with AI because of its better capability of handling a massive amount of data compared with the traditional statistical methods. Together, the primary purpose of these fields is to extract and analyze as much and meaningful hidden quantitative data as possible to be used in decision support. Nowadays, both radiomics and AI have been getting attention for their remarkable success in various radiological tasks, which has been met with anxiety by most of the radiologists due to the fear of replacement by intelligent machines. Considering ever-developing advances in computational power and availability of large data sets, the marriage of humans and machines in future clinical practice seems inevitable. Therefore, regardless of their feelings, the radiologists should be familiar with these concepts. Our goal in this paper was three-fold: first, to familiarize radiologists with the radiomics and AI; second, to encourage the radiologists to get involved in these ever-developing fields; and, third, to provide a set of recommendations for good practice in design and assessment of future works.

Primary dural lymphomas: Clinical presentation, management, and outcome

BACKGROUND

Clinical experience is limited for primary central nervous system (CNS) lymphoma that arises from the dura mater, which is denoted with the term primary dural lymphoma (PDL). This study was aimed at determining the relative incidence, presentation, and outcomes of PDL.

METHODS

The institutional databases of the Divisions of Neuro-Oncology at the Massachusetts General Hospital and the Yale School of Medicine were retrospectively searched for patients with primary CNS lymphoma. Patients with pathologically confirmed dural lymphoma and no evidence of primary cerebral or systemic involvement were identified. Clinical data, diagnostic findings, treatments, and outcomes were recorded.

RESULTS

A total of 20 patients with PDL were identified, and they represented 6.3% of the individuals with primary CNS lymphomas (20 of 316). Histopathological examination of PDL revealed the following underlying subtypes: diffuse large B-cell lymphoma (10 of 20 patients), marginal zone lymphoma (6 of 20), follicular lymphoma (2 of 20), undefined B-cell non-Hodgkin lymphoma (1 of 20), and T-cell non-Hodgkin lymphoma (1 of 20). On imaging, all tumors appeared as extra-axial masses with avid contrast enhancement and mostly mimicked meningioma. The median apparent diffusion coefficient value was 667 ± 26 mm² /s. Cerebrospinal fluid analyses and symptoms were nonspecific, and the diagnosis rested on tissue analysis. Therapeutic approaches included surgery, radiotherapy, and chemotherapy. The median overall survival was not reached after 5 years. Three patients were deceased at database closure because of tumor progression. The extent of tumor resection correlated positively with overall survival (P = .044).

CONCLUSIONS

PDL is a rare variant of primary CNS lymphoma that can be radiographically mistaken for meningioma. The outcome is excellent with multimodality treatment, and aggressive surgery may convey a survival advantage in select cases.

SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging

OBJECTIVE

Histopathological examination (HPE) is the current gold standard for assessing chemotherapy response to tumor, but it is possible only after surgery. The purpose of the study was to develop a noninvasive, imaging-based robust method to delineate, visualize, and quantify the proportions of necrosis and viable tissue present within the tumor along with peritumoral edema before and after neoadjuvant chemotherapy (NACT) and to evaluate treatment response with correlation to HPE necrosis after surgery.

METHODS

The MRI dataset of 30 patients (N = 30; male:female = 24:6; age = 17.6 ± 2.7 years) with osteosarcoma was acquired using 1.5 T Philips Achieva MRI scanner before (baseline) and after 3 cycles of NACT (follow-up). After NACT, all patients underwent surgical resection followed by HPE. Simple linear iterative clustering supervoxels and Otsu multithresholding were combined to develop the proposed method-SLICs+MTh-to subsegment and quantify viable and nonviable regions within tumor using multiparametric MRI. Manually drawn ground-truth ROIs and SLICs+MTh-based segmentation of tumor, edema, and necrosis were compared using Jacquard index (JI), Dice coefficient (DC), precision (P), and recall (R). Postcontrast T1W images (PC-T1W) were used to validate the SLICs+MTh-based necrosis. SLICs+MTh-based necrosis volume at follow-up was compared with HPE necrosis using paired t test (p ≤ 0.05).

RESULTS

Active tumor, necrosis, and edema were segmented with moderate to satisfactory accuracy (JI = 62-78%; DC = 72-87%; P = 67-87%; R = 63-88%). Qualitatively and quantitatively (DC = 74 ± 9%), the SLICs+MTh-based necrosis area correlated well with the hypointense necrosis areas in PC-T1W. No significant difference (paired t test, p = 0.26; Bland-Altman plot, bias = 2.47) between SLICs+MTh-based necrosis at follow-up and HPE necrosis was observed.

CONCLUSION

The proposed multiparametric MRI-based SLICs+MTh method performs noninvasive assessment of NACT response in osteosarcoma that may improve cancer treatment monitoring, planning, and overall prognosis.

KEY POINTS

• The simple linear iterative clustering supervoxels and Otsu multithresholding-based technique (SLICs+MTh) successfully estimates the proportion of necrosis, viable tumor, and edema in osteosarcoma in the course of chemotherapy. • The proposed technique is noninvasive and uses multiparametric MRI to measure necrosis as an indication of anticancer treatment response. • SLICs+MTh-based necrosis was in satisfactory agreement with histological necrosis after surgery.

Benign or Malignant Characterization of Soft-Tissue Tumors by Using Semiquantitative and Quantitative Parameters of Dynamic Contrast-Enhanced Magnetic Resonance Imaging

PURPOSE

To evaluate the efficacy of the semiquantitative and quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating between benign and malignant soft-tissue tumors.

METHODS

A total of 45 patients with pathologically confirmed soft-tissue tumors (15 benign and 30 malignant tumors) underwent DCE-MRI. The semiquantitative parameters assessed were as follows: time to peak (TTP), maximum concentration (MAX Conc), area under the curve of time-concentration curve (AUC-TC), and maximum rise slope (MAX Slope). Quantitative DCE-MRI was analyzed with the extended Tofts-Kety model to assess the following quantitative parameters: volume transfer constant (Ktrans), microvascular permeability reflux constant (Kep), and distribute volume per unit tissue volume (Ve). Data were evaluated using the independent t test or Mann-Whitney U test and receiver operating characteristic (ROC) curves.

RESULTS

The TTP (P = .0035), MAX Conc (P = .0018), AUC-TC (P = .0018), MAX Slope (P = .0018), Ktrans (P = .0018), and Kep (P = .0035) were significantly different between the benign and malignant soft-tissue tumors. The AUC of the ROC curve demonstrated the diagnostic potential of TTP (0.778), MAX Conc (0.849), AUC-TC (0.831), MAX Slope (0.847), Ktrans (0.836), Kep (0.778), and Ve (0.638).

CONCLUSIONS

The use of semiquantitative and quantitative parameters of DCE-MRI enabled differentiation between benign and malignant soft-tissue tumors. The values of TTP were lower, while those of MAX Conc, AUC-TC, MAX Slope, Ktrans, and Kep were higher in malignant than in benign tumors.

Prognostic Value of Texture Analysis Based on Pretreatment DWI-Weighted MRI for Esophageal Squamous Cell Carcinoma Patients Treated With Concurrent Chemo-Radiotherapy

Purpose: The purpose of the research was to assess the prognostic value of three-dimensional (3D) texture features based on diffusion-weighted magnetic resonance imaging (DWI) for esophageal squamous cell carcinoma (ESCC) patients undergoing concurrent chemo-radiotherapy (CRT). Methods: We prospectively enrolled 82 patients with ESCC into a cohort study. Two DWI sequences (b = 0 and b = 600 s/mm²) were acquired along with axial T2WI and T1WI before CRT. Two groups of features were examined: (1) clinical and demographic features (e.g., TNM stage, age and sex) and (2) changes in spatial texture characteristics of the apparent diffusion coefficient (ADC), which characterizes gray intensity changes in tumor areas, spatial pattern and distribution, and related changes caused by CRT. Reproducible feature sets without redundancy were statistically filtered and validated. The prognostic values associated with overall survival (OS) for each parameter were studied using Kaplan-Meier and Cox regression models for univariate and multivariate analyses, respectively. Results: Both univariate and multivariate Cox model analyses showed that the energy of intensity histogram texture (IHIST_energy), radiation dose, mean of the contrast in distance 1 of 26 directions (m_contrast_1), extreme difference of the homogeneity in distance 2 of 26 directions (Diff_homogeneity_2), mean of the inverse variance in distance 2 of 26 directions (m_lnversevariance_2), high-intensity small zone emphasis (HISE), and low-intensity large zone emphasis (LILE) were significantly associated with survival. The results showed that 6 texture parameters extracted from the ADC images before treatment could distinguish among high-, medium-, and low-risk groups (log-rank χ² = 9.7; P = 0.00773). The biased C-index value was 0.715 (95% CI: 0.708 to 0.732) based on bootstrapping validation. Conclusions: The ADC 3D texture feature can be used as a useful biomarker to predict the survival of ESCC patients undergoing CRT. Combining ADC 3D texture features with conventional prognostic factors can generate reliable survival prediction models.

Pathologic response after modern radiotherapy for non-small cell lung cancer

In non-small cell lung cancer (NSCLC), pathologic complete response (pCR) following radiotherapy treatment has been shown to be an independent prognostic factor for long-term survival, progression-free survival and locoregional control. PCR is considered a surrogate to therapeutic efficacy, years before survival data are available, and therefore can be used to guide treatment plans and additional therapeutic interventions post-surgical resection. Given the extensive fibrotic changes induced by radiotherapy in the lung, radiological assessment of response can potentially misrepresent pathologic response. The optimal timing for assessment of pathologic response after conventionally fractionated radiotherapy and stereotactic ablative radiotherapy (SABR) remains poorly understood. In this review, we summarize recent literature on pathologic response after radiotherapy for early stage and locally advanced NSCLC, we discuss current controversies around radiobiological considerations, and we present upcoming trials that will provide insight into current knowledge gaps.

What Is the Role of Imaging at Primary Diagnostic Work-Up in Uterine Cervical Cancer?

PURPOSE OF REVIEW

For uterine cervical cancer, the recently revised International Federation of Gynecology and Obstetrics (FIGO) staging system (2018) incorporates imaging and pathology assessments in its staging. In this review we summarize the reported staging performances of conventional and novel imaging methods and provide an overview of promising novel imaging methods relevant for cervical cancer patient care.

RECENT FINDINGS

Diagnostic imaging during the primary diagnostic work-up is recommended to better assess tumor extent and metastatic disease and is now reflected in the 2018 FIGO stages 3C1 and 3C2 (positive pelvic and/or paraaortic lymph nodes). For pretreatment local staging, imaging by transvaginal or transrectal ultrasound (TVS, TRS) and/or magnetic resonance imaging (MRI) is instrumental to define pelvic tumor extent, including a more accurate assessment of tumor size, stromal invasion depth, and parametrial invasion. In locally advanced cervical cancer, positron emission tomography-computed tomography (PET-CT) or computed tomography (CT) is recommended, since the identification of metastatic lymph nodes and distant metastases has therapeutic consequences. Furthermore, novel imaging techniques offer visualization of microstructural and functional tumor characteristics, reportedly linked to clinical phenotype, thus with a potential for further improving risk stratification and individualization of treatment. Diagnostic imaging by MRI/TVS/TRS and PET-CT/CT is instrumental for pretreatment staging in uterine cervical cancer and guides optimal treatment strategy. Novel imaging techniques may also provide functional biomarkers with potential relevance for developing more targeted treatment strategies in cervical cancer.

Early detection of changes in phospholipid metabolism during neoadjuvant chemotherapy in breast cancer patients using phosphorus magnetic resonance spectroscopy at 7T

The purpose of this work was to investigate whether noninvasive early detection (after the first cycle) of response to neoadjuvant chemotherapy (NAC) in breast cancer patients was possible. ³¹ P-MRSI at 7 T was used to determine different phosphor metabolites ratios and correlate this to pathological response. ³¹ P-MRSI was performed in 12 breast cancer patients treated with NAC. ³¹ P spectra were fitted and aligned to the frequency of phosphoethanolamine (PE). Metabolic signal ratios for phosphomonoesters/phosphodiesters (PME/PDE), phosphocholine/glycerophosphatidylcholine (PC/GPtC), phosphoethanolamine/glycerophosphoethanolamine (PE/GPE) and phosphomonoesters/in-organic phosphate (PME/Pi) were determined from spectral fitting of the individual spectra and the summed spectra before and after the first cycle of NAC. Metabolic ratios were subsequently related to pathological response. Additionally, the correlation between the measured metabolic ratios and Ki-67 levels was determined using linear regression. Four patients had a pathological complete response after treatment, five patients a partial pathological response, and three patients did not respond to NAC. In the summed spectrum after the first cycle of NAC, PME/Pi and PME/PDE decreased by 18 and 13%, respectively. A subtle difference among the different response groups was observed in PME/PDE, where the nonresponders showed an increase and the partial and complete responders a decrease (P = 0.32). No significant changes in metabolic ratios were found. However, a significant association between PE/Pi and the Ki-67 index was found (P = 0.03). We demonstrated that it is possible to detect subtle changes in ³¹ P metabolites with a 7 T MR system after the first cycle of NAC treatment in breast cancer patients. Nonresponders showed different changes in metabolic ratios compared with partial and complete responders, in particular for PME/PDE; however, more patients need to be included to investigate its clinical value.

Image-based biomarkers for solid tumor quantification

The last few decades have witnessed tremendous technological developments in image-based biomarkers for tumor quantification and characterization. Initially limited to manual one- and two-dimensional size measurements, image biomarkers have evolved to harness developments not only in image acquisition technology but also in image processing and analysis algorithms. At the same time, clinical validation remains a major challenge for the vast majority of these novel techniques, and there is still a major gap between the latest technological developments and image biomarkers used in everyday clinical practice. Currently, the imaging biomarker field is attracting increasing attention not only because of the tremendous interest in cutting-edge therapeutic developments and personalized medicine but also because of the recent progress in the application of artificial intelligence (AI) algorithms to large-scale datasets. Thus, the goal of the present article is to review the current state of the art for image biomarkers and their use for characterization and predictive quantification of solid tumors. Beginning with an overview of validated imaging biomarkers in current clinical practice, we proceed to a review of AI-based methods for tumor characterization, such as radiomics-based approaches and deep learning.Key Points• Recent years have seen tremendous technological developments in image-based biomarkers for tumor quantification and characterization.• Image-based biomarkers can be used on an ongoing basis, in a non-invasive (or mildly invasive) way, to monitor the development and progression of the disease or its response to therapy.• We review the current state of the art for image biomarkers, as well as the recent developments in artificial intelligence (AI) algorithms for image processing and analysis.

Prediction of Treatment Outcome of Chemotherapy Using Perfusion Computed Tomography in Patients with Unresectable Advanced Gastric Cancer

Objective

To evaluate whether data acquired from perfusion computed tomography (PCT) parameters can aid in the prediction of treatment outcome after palliative chemotherapy in patients with unresectable advanced gastric cancer (AGC).

Materials and Methods

Twenty-one patients with unresectable AGCs, who underwent both PCT and palliative chemotherapy, were prospectively included. Treatment response was assessed according to Response Evaluation Criteria in Solid Tumors version 1.1 (i.e., patients who achieved complete or partial response were classified as responders). The relationship between tumor response and PCT parameters was evaluated using the Mann-Whitney test and receiver operating characteristic analysis. One-year survival was estimated using the Kaplan-Meier method.

Results

After chemotherapy, six patients exhibited partial response and were allocated to the responder group while the remaining 15 patients were allocated to the non-responder group. Permeability surface (PS) value was shown to be significantly different between the responder and non-responder groups (51.0 mL/100 g/min vs. 23.4 mL/100 g/min, respectively; p = 0.002), whereas other PCT parameters did not demonstrate a significant difference. The area under the curve for prediction in responders was 0.911 (p = 0.004) for PS value, with a sensitivity of 100% (6/6) and specificity of 80% (12/15) at a cut-off value of 29.7 mL/100 g/min. One-year survival in nine patients with PS value > 29.7 mL/100 g/min was 66.7%, which was significantly higher than that in the 12 patients (33.3%) with PS value ≤ 29.7 mL/100 g/min (p = 0.019).

Conclusion

Perfusion parameter data acquired from PCT demonstrated predictive value for treatment outcome after palliative chemotherapy, reflected by the significantly higher PS value in the responder group compared with the non-responder group.

Pathophysiological mapping of tumor habitats in the breast in DCE-MRI using molecular texture descriptor

BACKGROUND

We propose a computational methodology capable of detecting and analyzing breast tumor habitats in images acquired by magnetic resonance imaging with dynamic contrast enhancement (DCE-MRI), based on the pathophysiological behavior of the contrast agent (CA).

METHODS

The proposed methodology comprises three steps. In summary, the first step is the acquisition of images from the Quantitative Imaging Network Breast. In the second step, the segmentation of the breasts is performed to remove the background, noise, and other unwanted objects from the image. In the third step, the generation of habitats is performed by applying two techniques: the molecular texture descriptor (MTD) that highlights the CA regions in the breast, and pathophysiological texture mapping (MPT), which generates tumor habitats based on the behavior of the CA. The combined use of these two techniques allows the automatic detection of tumors in the breast and analysis of each separate habitat with respect to their malignancy type.

RESULTS

The results found in this study were promising, with 100% of breast tumors being identified. The segmentation results exhibited an accuracy of 99.95%, sensitivity of 71.07%, specificity of 99.98%, and volumetric similarity of 77.75%. Moreover, we were able to classify the malignancy of the tumors, with 6 classified as malignant type III (WashOut) and 14 as malignant type II (Plateau), for a total of 20 cases.

CONCLUSION

We proposed a method for the automatic detection of tumors in the breast in DCE-MRI and performed the pathophysiological mapping of tumor habitats by analyzing the behavior of the CA, combining MTD and MPT, which allowed the mapping of internal tumor habitats.

A novel approach to monitoring the efficacy of anti-tumor treatments in animal models: combining functional MRI and texture analysis

Background

The aim of this study was to evaluate the early anti-tumor efficiency of different therapeutic agents with a combination of multi-b-value DWI, DCE-MRI and texture analysis.

Methods

Eighteen 4 T1 homograft tumor models were divided into control, paclitaxel monotherapy and paclitaxel and bevacizumab combination therapy groups (n = 6) that underwent multi-b-value DWI, DCE-MRI and texture analysis before and 15 days after treatment.

Results

After treatment, the tumors in the control group were significantly larger than those in the combination group (P = 0.018). In multi-b-value DWI, the ADC_slow obviously increased in the combination group compared to that in the others (P < 0.01). The f increased in the control and paclitaxel groups, but the combination group showed a significant decrease versus the others (P < 0.02). Additionally, in DCE-MRI, the decreasing K^trans showed an evident difference between the combination and control groups (P = 0.003) due to the latter’s increasing K^trans. The intra-group comparisons of tumor texture in pre-, mid- and post-treatments showed that the entropy had all significantly increased in all groups (P < 0.01, SSF = 0–6), though the MPP, mean and SD increased only in the combination group (P_MPP,mean,SD < 0.05, SSF = 4–6). Moreover, the inter-group comparisons revealed that the mean and MPP exhibited significant differences after treatment (P_mean,MPP < 0.05, SSF = 0–3).

Conclusion

All these results suggest some strong correlations among DWI, DCE and texture analysis, which are beneficial for further study and clinical research.

Identifying an early treatment window for predicting breast cancer response to neoadjuvant chemotherapy using immunohistopathology and hemoglobin parameters

BACKGROUND

Breast cancer pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) varies with tumor subtype. The purpose of this study was to identify an early treatment window for predicting pCR based on tumor subtype, pretreatment total hemoglobin (tHb) level, and early changes in tHb following NAC.

METHODS

Twenty-two patients (mean age 56 years, range 34-74 years) were assessed using a near-infrared imager coupled with an Ultrasound system prior to treatment, 7 days after the first treatment, at the end of each of the first three cycles, and before their definitive surgery. Pathologic responses were dichotomized by the Miller-Payne system. Tumor vascularity was assessed from tHb; vascularity changes during NAC were assessed from a percentage tHb normalized to the pretreatment level (%tHb). After training the logistic prediction models using the previous study data, we assessed the early treatment window for predicting pathological response according to their tumor subtype (human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), triple-negative (TN)) based on tHb, and %tHb measured at different cycles and evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

In the new study cohort, maximum pretreatment tHb and %tHb changes after cycles 1, 2, and 3 were significantly higher in responder Miller-Payne 4-5 tumors (n = 13) than non-or partial responder Miller-Payne 1-3 tumors (n = 9). However, no significance was found at day 7. The AUC of the predictive power of pretreatment tHb in the cohort was 0.75, which was similar to the performance of the HER2 subtype as a single predictor (AUC of 0.78). A greater predictive power of pretreatment tHb was found within each subtype, with AUCs of 0.88, 0.69, and 0.72, in the HER2, ER, and TN subtypes, respectively. Using pretreatment tHb and cycle 1 %tHb, AUC reached 0.96, 0.91, and 0.90 in HER2, ER, and TN subtypes, respectively, and 0.95 regardless of subtype. Additional cycle 2 %tHb measurements moderately improved prediction for the HER2 subtype but did not improve prediction for the ER and TN subtypes.

CONCLUSIONS

By combining tumor subtypes with tHb, we predicted the pCR of breast cancer to NAC before treatment. Prediction accuracy can be significantly improved by incorporating cycle 1 and 2 %tHb for the HER2 subtype and cycle 1 %tHb for the ER and TN subtypes.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02092636 . Registered in March 2014.

Amide chemical exchange saturation transfer at 7 T: a possible biomarker for detecting early response to neoadjuvant chemotherapy in breast cancer patients

BACKGROUND

The purpose of this work was to investigate noninvasive early detection of treatment response of breast cancer patients to neoadjuvant chemotherapy (NAC) using chemical exchange saturation transfer (CEST) measurements sensitive to amide proton transfer (APT) at 7 T.

METHODS

CEST images were acquired in 10 tumors of nine breast cancer patients treated with NAC. APT signals in the tumor, before and after the first cycle of NAC, were quantified using a three-pool Lorentzian fit of the z-spectra in the region of interest. The changes in APT were subsequently related to pathological response after surgery defined by the Miller-Payne system.

RESULTS

Significant differences (P <  0.05, unpaired Mann-Whitney test) were found in the APT signal before and after the first cycle of NAC in six out of 10 lesions, of which two showed a pathological complete response. Of the remaining four lesions, one showed a pathological complete response. No significant difference in changes of APT signal were found between the different pathological responses to NAC treatment (P > 0.05, Kruskal-Wallis test).

CONCLUSIONS

This preliminary study shows the feasibility of using APT CEST magnetic resonance imaging as a noninvasive biomarker to assess the effect of NAC in an early stage of NAC treatment of breast cancer patients.

TRIAL REGISTRATION

Registration number, NL49333.041.14/ NTR4980 . Registered on 16 October 2014.

A study on diffusion and kurtosis features of cervical cancer based on non-Gaussian diffusion weighted model

OBJECTIVE

To explore the diffusion and kurtosis features of cervical cancer (CC) and study the feasibility of diffusion kurtosis imaging (DKI) based on the non-Gaussian diffusion-weighted model to differentiate the stage and grade of CC.

METHODS

A total of 50 patients with pathologically confirmed CC were enrolled. MRI examinations including DKI (with 5b values 200, 500, 1000, 1500, and 2000smm^-2 were performed before any treatment. The apparent coefficient (D_app) and the apparent kurtosis value (K_app) were derived from the non-gaussian diffusion model, and the apparent diffusion coefficient (ADC) was derived from the Gaussian model. The parameters of CC and normal tissue (myometrium) were obtained, analyzed statistically, and evaluated with respect to differentiating stage and grade between the tissue and the CC.

RESULTS

ADC and D_app values of CC were significantly lower than that of the normal myometrium (P=0.024 and P<0.001, respectively), while the K_app value was not found to exhibit a significant difference. Compared to the well/moderately differentiated CC, poorly differentiated CC had a significantly decreased mean ADC and D_app (P=0.018 and P=0.026, respectively); however, the mean K_app (P=0.035) increased significantly. In the clinical staging, the DKI sequence was advantageous over conventional MRI sequences (degree of accuracy: 90% vs. 74%), Although in the quantitative analysis, these parameters did not show a significant difference.

CONCLUSIONS

The pilot study demonstrated that these diffusion and kurtosis indices from DKI based on the non-Gaussian diffusion-weighted model putatively differentiated the grade and stage of CC.

MRI-only treatment planning: benefits and challenges

Over the past decade, the application of magnetic resonance imaging (MRI) has increased, and there is growing evidence to suggest that improvements in the accuracy of target delineation in MRI-guided radiation therapy may improve clinical outcomes in a variety of cancer types. However, some considerations should be recognized including patient motion during image acquisition and geometric accuracy of images. Moreover, MR-compatible immobilization devices need to be used when acquiring images in the treatment position while minimizing patient motion during the scan time. Finally, synthetic CT images (i.e. electron density maps) and digitally reconstructed radiograph images should be generated from MRI images for dose calculation and image guidance prior to treatment. A short review of the concepts and techniques that have been developed for implementation of MRI-only workflows in radiation therapy is provided in this document.

Emerging Molecular Imaging Techniques in Gynecologic Oncology

Molecular imaging (mainly PET and MR imaging) has played important roles in gynecologic oncology. Emerging MR-based technologies, including DWI, CEST, DCE-MR imaging, MRS, and DNP, as well as FDG-PET and many novel PET radiotracers, will continuously improve practices. In combination with radiomics analysis, a new era of decision making in personalized medicine and precisely guided radiation treatment planning or real-time surgical interventions is being entered into, which will directly impact on patient survival. Prospective trials with well-defined endpoints are encouraged to evaluate the multiple facets of these emerging imaging tools in the management of gynecologic malignancies.

FLT-PET for early response evaluation of colorectal cancer patients with liver metastases: a prospective study

BACKGROUND

Fluoro-L-thymidine (FLT) is a positron emission tomography/computed tomography (PET/CT) tracer which reflects proliferative activity in a cancer lesion. The main objective of this prospective explorative study was to evaluate whether FLT-PET can be used for the early evaluation of treatment response in colorectal cancer patients (CRC) with liver metastases. Patients with metastatic CRC having at least one measurable (>1 cm) liver metastasis receiving first-line chemotherapy were included. A FLT-PET/CT scan was performed at baseline and after the first treatment. The maximum and mean standardised uptake values (SUVmax, SUVmean) were measured. After three cycles of chemotherapy, treatment response was assessed by CT scan based on RECIST 1.1.

RESULTS

Thirty-nine consecutive patients were included of which 27 were evaluable. Dropout was mainly due to disease complications. Nineteen patients (70%) had a partial response, seven (26%) had stable disease and one (4%) had progressive disease. A total of 23 patients (85%) had a decrease in FLT uptake following the first treatment. The patient with progressive disease had the highest increase in FLT uptake in SUVmax. There was no correlation between the response according to RECIST and the early changes in FLT uptake measured as SUVmax (p = 0.24).

CONCLUSIONS

No correlation was found between early changes in FLT uptake after the first cycle of treatment and the response evaluated from subsequent CT scans. It seems unlikely that FLT-PET can be used on its own for the early response evaluation of metastatic CRC.

Radiomics: the bridge between medical imaging and personalized medicine

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 1: ADCmean

Diffusion weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique based on measure of water diffusion in tissues. This diffusion can be quantified by apparent diffusion coefficient (ADC). Some reports indicated that ADC can reflect tumor proliferation potential. The purpose of this meta-analysis was to provide evident data regarding associations between ADC and KI 67 in different tumors. Studies investigating the relationship between ADC and KI 67 in different tumors were identified. MEDLINE library was screened for associations between ADC and KI 67 in different tumors up to April 2017. Overall, 42 studies with 2026 patients were identified. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients. Associations between ADC and KI 67 were analyzed by Spearman's correlation coefficient. The reported Pearson correlation coefficients in some studies were converted into Spearman correlation coefficients. The pooled correlation coefficient between ADC_mean and KI 67 for all included tumors was ρ = -0.44. Furthermore, correlation coefficient for every tumor entity was calculated. The calculated correlation coefficients were as follows: ovarian cancer: ρ = -0.62, urothelial carcinomas: ρ = -0.56, cerebral lymphoma: ρ = -0.55, neuroendocrine tumors: ρ = -0.52, glioma: ρ = -0.51, lung cancer: ρ = -0.50, prostatic cancer: ρ = -0.43, rectal cancer: ρ = -0.42, pituitary adenoma:ρ = -0.44, meningioma, ρ = -0.43, hepatocellular carcinoma: ρ = -0.37, breast cancer: ρ = -0.22.

DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

This study investigates the effectiveness of hundreds of texture features extracted from voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps for early prediction of breast cancer response to neoadjuvant chemotherapy (NAC). In total, 38 patients with breast cancer underwent DCE-MRI before (baseline) and after the first of the 6-8 NAC cycles. Quantitative pharmacokinetic (PK) parameters and semiquantitative metrics were estimated from DCE-MRI time-course data. The residual cancer burden (RCB) index value was computed based on pathological analysis of surgical specimens after NAC completion. In total, 1043 texture features were extracted from each of the 13 parametric maps of quantitative PK or semiquantitative metric, and their capabilities for early prediction of RCB were examined by correlating feature changes between the 2 MRI studies with RCB. There were 1069 pairs of feature-map combinations that showed effectiveness for response prediction with 4 correlation coefficients >0.7. The 3-dimensional gray-level cooccurrence matrix was the most effective feature extraction method for therapy response prediction, and, in general, the statistical features describing texture heterogeneity were the most effective features. Quantitative PK parameters, particularly those estimated with the shutter-speed model, were more likely to generate effective features for prediction response compared with the semiquantitative metrics. The best feature-map pair could predict pathologic complete response with 100% sensitivity and 100% specificity using our cohort. In conclusion, breast tumor heterogeneity in microvasculature as measured by texture features of voxel-based DCE-MRI parametric maps could be a useful biomarker for early prediction of NAC response.

Primary and metastatic ovarian cancer: Characterization by 3.0T diffusion-weighted MRI

Objectives

We aimed to investigate whether apparent diffusion coefficients (ADCs) measured by 3.0T diffusion-weighted magnetic resonance imaging (DWI) associate with histological aggressiveness of ovarian cancer (OC) or predict the clinical outcome. This prospective study enrolled 40 patients with primary OC, treated 2011-2014.

Methods

DWI was performed prior to surgery. Two observers used whole lesion single plane region of interest (WLsp-ROI) and five small ROIs (S-ROI) to analyze ADCs. Samples from tumours and metastases were collected during surgery. Immunohistochemistry and quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used to measure the expression of vascular endothelial growth factor (VEGF) and its receptors.

Results

The interobserver reliability of ADC measurements was excellent for primary tumours ICC 0.912 (WLsp-ROI). Low ADCs significantly associated with poorly differentiated OC (WLsp-ROI P = 0.035). In primary tumours, lower ADCs significantly associated with high Ki-67 (P = 0.001) and low VEGF (P = 0.001) expression. In metastases, lower ADCs (WLsp-ROI) significantly correlated with low VEGF receptors mRNA levels. ADCs had predictive value; 3-year overall survival was poorer in patients with lower ADCs (WLsp-ROI P = 0.023, S-ROI P = 0.038).

Conclusion

Reduced ADCs are associated with histological severity and worse outcome in OC. ADCs measured with WLsp-ROI may serve as a prognostic biomarker of OC.

Key Points

• Reduced ADCs correlate with prognostic markers: poor differentiation and high Ki-67 expression

• ADCs also significantly correlated with VEGF protein expression in primary tumours

• Lower ADC values are associated with poorer survival in ovarian cancer

• Whole lesion single plane-ROI ADCs may be used as a prognostic biomarker in OC

Decision support systems for personalized and participative radiation oncology

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

What Is the Best Preoperative Imaging for Endometrial Cancer?

Although endometrial cancer is surgicopathologically staged, preoperative imaging is recommended for diagnostic work-up to tailor surgery and adjuvant treatment. For preoperative staging, imaging by transvaginal ultrasound (TVU) and/or magnetic resonance imaging (MRI) is valuable to assess local tumor extent, and positron emission tomography-CT (PET-CT) and/or computed tomography (CT) to assess lymph node metastases and distant spread. Preoperative imaging may identify deep myometrial invasion, cervical stromal involvement, pelvic and/or paraaortic lymph node metastases, and distant spread, however, with reported limitations in accuracies and reproducibility. Novel structural and functional imaging techniques offer visualization of microstructural and functional tumor characteristics, reportedly linked to clinical phenotype, thus with a potential for improving risk stratification. In this review, we summarize the reported staging performances of conventional and novel preoperative imaging methods and provide an overview of promising novel imaging methods relevant for endometrial cancer care.

Evaluation of Soft Tissue Sarcoma Response to Preoperative Chemoradiotherapy Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging

This study aims to assess the utility of quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters in comparison with imaging tumor size for early prediction and evaluation of soft tissue sarcoma response to preoperative chemoradiotherapy. In total, 20 patients with intermediate- to high-grade soft tissue sarcomas received either a phase I trial regimen of sorafenib + chemoradiotherapy (n = 8) or chemoradiotherapy only (n = 12), and underwent DCE-MRI at baseline, after 2 weeks of treatment with sorafenib or after the first chemotherapy cycle, and after therapy completion. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors) guidelines. Pharmacokinetic analyses of DCE-MRI data were performed using the Shutter-Speed model. After only 2 weeks of treatment with sorafenib or after 1 chemotherapy cycle, K^trans (rate constant for plasma/interstitium contrast agent transfer) and its percent change were good early predictors of optimal versus suboptimal pathological response with univariate logistic regression C statistics values of 0.90 and 0.80, respectively, whereas RECIST LD percent change was only a fair predictor (C = 0.72). Post-therapy K^trans, v_e (extravascular and extracellular volume fraction), and k_ep (intravasation rate constant), not RECIST LD, were excellent (C > 0.90) markers of therapy response. Several DCE-MRI parameters before, during, and after therapy showed significant (P < .05) correlations with percent necrosis of resected tumor specimens. In conclusion, absolute values and percent changes of quantitative DCE-MRI parameters provide better early prediction and evaluation of the pathological response of soft tissue sarcoma to preoperative chemoradiotherapy than the conventional measurement of imaging tumor size change.