AI-Enhanced PET and MR Imaging for Patients with Breast Cancer

New challenges are currently faced by clinical and surgical oncologists in the management of patients with breast cancer, mainly related to the need for molecular and prognostic data. Recent technological advances in diagnostic imaging and informatics have led to the introduction of functional imaging modalities, such as hybrid PET/MR imaging, and artificial intelligence (AI) software, aimed at the extraction of quantitative radiomics data, which may reflect tumor biology and behavior. In this article, the most recent applications of radiomics and AI to PET/MR imaging are described to address the new needs of clinical and surgical oncology.

Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study

In this study, we aimed to systematically review the current literature on radiomics applied to cross-sectional adrenal imaging and assess its methodological quality. Scopus, PubMed and Web of Science were searched to identify original research articles investigating radiomics applications on cross-sectional adrenal imaging (search end date February 2021). For qualitative synthesis, details regarding study design, aim, sample size and imaging modality were recorded as well as those regarding the radiomics pipeline (e.g., segmentation and feature extraction strategy). The methodological quality of each study was evaluated using the radiomics quality score (RQS). After duplicate removal and selection criteria application, 25 full-text articles were included and evaluated. All were retrospective studies, mostly based on CT images (17/25, 68%), with manual (19/25, 76%) and two-dimensional segmentation (13/25, 52%) being preferred. Machine learning was paired to radiomics in about half of the studies (12/25, 48%). The median total and percentage RQS scores were 2 (interquartile range, IQR = −5–8) and 6% (IQR = 0–22%), respectively. The highest and lowest scores registered were 12/36 (33%) and −5/36 (0%). The most critical issues were the absence of proper feature selection, the lack of appropriate model validation and poor data openness. The methodological quality of radiomics studies on adrenal cross-sectional imaging is heterogeneous and lower than desirable. Efforts toward building higher quality evidence are essential to facilitate the future translation into clinical practice.

Qualitative Heterogeneous Signal Drop on Chemical Shift (CS) MR Imaging: Correlative Quantitative Analysis between CS Signal Intensity Index and Contrast Washout Parameters Using T1-Weighted Sequences

The aim of this study was to calculate MRI quantitative parameters extracted from chemical-shift (CS) and dynamic contrast-enhanced (DCE) T1-weighted (T1-WS) images of adrenal lesions (AL) with qualitative heterogeneous signal drop on CS T1-WS and compare them to those of AL with homogeneous or no signal drop on CS T1-WS. On 3 T MRI, 65 patients with a total of 72 AL were studied. CS images were qualitatively assessed for grouping AL as showing homogeneous (Group 1, n = 19), heterogeneous (Group 2, n = 23), and no (Group 3, n = 30) signal drop. Histopathology or follow-up data served as reference standard to classify AL. ROIs were drawn both on CS and DCE images to obtain adrenal CS signal intensity index (ASII), absolute (AWO), and relative washout (RWO) values. Quantitative parameters (QP) were compared with ANOVA analysis and post hoc Dunn’s test. The performance of QP to classify AL was assessed with receiver operating characteristic analysis. CS ASII values were significantly different among the three groups (p < 0.001) with median values of 71%, 53%, and 3%, respectively. AWO/RWO values were similar in Groups 1 (adenomas) and 2 (benign AL) but significantly (p < 0.001) lower in Group 3 (20 benign AL and 10 malignant AL). With cut-offs, respectively, of 60% (Group 1 vs. 2), 20% (Group 2 vs. 3), and 37% (Group 1 vs. 3), CS ASII showed areas under the curve of 0.85, 0.96, and 0.93 for the classification of AL, overall higher than AWO/RWO. In conclusion, AL with qualitative heterogeneous signal drop at CS represent benign AL with QP by DCE sequence similar to those of AL with homogeneous signal drop at CS, but different to those of AL with no signal drop at CS; ASII seems to be the only quantitative parameter able to differentiate AL among the three different groups.

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.

Current Imaging Evaluation of Tumor Response to Advanced Medical Treatment in Metastatic Renal-Cell Carcinoma: Clinical Implications

The present review is focused on the role of diagnostic tomographic imaging such as computed tomography and magnetic resonance imaging to assess and predict tumor response to advanced medical treatments in metastatic renal cell carcinoma (RCC) patients. In this regard, antiangiogenic agents and immune checkpoint inhibitors (ICIs) have developed as advanced treatment options replacing the conventional therapy based on interferon-alpha and interleuchin-2 which had unfavorable toxicity profile and low response rates. In clinical practice, the imaging evaluation of treatment response in cancer patients is based on dimensional changes of tumor lesions in sequential scans; in particular, Response Evaluation Criteria in Solid Tumors (RECIST) have been defined for this purpose and also applied in patients with metastatic RCC. However, these new drugs with predominant cytostatic effect make RECIST insufficient to realize an adequate response imaging evaluation. Therefore, new imaging criteria (mCHOI and iRECIST) have been proposed to assess tumor response to advanced medical treatments of metastatic RCC, they correlate better than RECIST with the progression-free survival and overall survival. Finally, a potential role of radiomics and machine learning models has been suggested to predict tumor response.

Assessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives

Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving NAC. Accurate assessment of the response to NAC is crucial for subsequent surgical planning. Furthermore, early prediction of tumor response could avoid patients being overtreated with useless chemotherapy sections, which are not free from side effects and psychological implications. In this review, we first analyze and compare the accuracy of conventional and advanced imaging techniques as well as discuss the application of artificial intelligence tools in the assessment of tumor response after NAC. Thereafter, the role of advanced imaging techniques, such as MRI, nuclear medicine, and new hybrid PET/MRI imaging in the prediction of the response to NAC is described in the second part of the review. Finally, future perspectives in NAC response prediction, represented by AI applications, are discussed.

Pancreatic Neuroendocrine Tumors in Patients with Multiple Endocrine Neoplasia Type 1: Diagnostic Value of Different MRI Sequences

BACKGROUND

MRI is a useful imaging modality to assess the presence of pancreatic neuroendocrine tumors (PNETs), allowing repeat monitoring examinations in multiple endocrine neoplasia type 1 (MEN-1) patients.

OBJECTIVES

We aimed to compare the diagnostic accuracy of conventional MRI sequences to identify which sequence better depicts the presence of PNETs in MEN-1 patients.

METHOD

We performed a retrospective analysis of consecutive MEN-1 patients who underwent a conventional MRI protocol to monitor previously proven PNETs. MRI sequences T1-w chemical shift (CS), T2-w HASTE, fat-suppressed (FS) T2-w HASTE, diffusion-weighted imaging (DWI), and pre- and post-contrast FS T1-w sequences were independently analyzed by 2 experienced radiologists using a 3-grade score (no lesion, uncertain lesion, and certain lesion); lesion size and signal intensity were recorded. A Friedman ANOVA and a Wilcoxon pairwise test for the post hoc analysis were used. The sensitivity of each sequence was measured, and the results were analyzed with the χ2 test.

RESULTS

We included 21 patients with a total of 45 PNETs proven by histology, endoscopic ultrasonography-guided fine-needle aspiration, CT, and nuclear medicine studies. A statistically significant (p < 0.01) difference was observed in the detection performance of each MRI sequence, particularly between DWI (91%) and T2-w FS (85%) sequences in comparison to the others (T1-w CS, T2-w, and pre- and post-contrast FS T1-w, ≤56% for all); no significant (p = 0.5) difference was found between the detection performance of DWI and T2-w FS sequences. No correlation was observed between the qualitative score of each sequence and lesion tumor size.

CONCLUSIONS

DWI and T2-w FS sequences proved to be the most accurate in the detection of PNETs, thus suggesting a role for an abbreviated MRI protocol without contrast medium administration for monitoring MEN-1 patients.

Breast implant associated anaplastic large cell lymphoma (BIA-ALCL): a challenging cytological diagnosis with hybrid PET/MRI staging and follow-up

We report a case of a 55-year-old woman with left breast cosmetic augmentation performed 5 years earlier, showing at ultrasound a left small amount of peri-implant effusion suspicious for an anaplastic large cell lymphoma localization. The final diagnosis was obtained by cytology using a small amount of fluid (6 ml). Subsequently, hybrid 18F-FDG PET/MRI was used for pre-operative staging and follow-up. An appropriate management of BIA-ALCL could be obtained even in cases of a small amount of peri-implant effusion, using a comprehensive approach of clinical and imaging evaluation, including PET/MRI as useful and innovative staging imaging technique.

Current trends of artificial intelligence in cancer imaging

In this editorial, we discussed the current research status of artificial intelligence (AI) in Oncology, reviewing the basics of machine learning (ML) and deep learning (DL) techniques and their emerging applications on clinical and imaging cancer workflow. The growing amounts of available “big data” coupled to the increasing computational power have enabled the development of computer-based systems capable to perform advanced tasks in many areas of clinical care, especially in medical imaging. ML is a branch of data science that allows the creation of computer algorithms that can learn and make predictions without prior instructions. DL is a subgroup of artificial neural network algorithms configurated to automatically extract features and perform high-level tasks; convolutional neural networks are the most common DL models used in medical image analysis. AI methods have been proposed in many areas of oncology granting promising results in radiology-based clinical applications. In detail, we explored the emerging applications of AI in oncological risk assessment, lesion detection, characterization, staging, and therapy response. Critical issues such as the lack of reproducibility and generalizability need to be addressed to fully implement AI systems in clinical practice. Nevertheless, AI impact on cancer imaging has been driving the shift of oncology towards a precision diagnostics and personalized cancer treatment.

Tumor segmentation analysis at different post-contrast time points: A possible source of variability of quantitative DCE-MRI parameters in locally advanced breast cancer

PURPOSE

to assess if tumor segmentation analysis performed at different post-contrast time points (TPs) on dynamic images could influence the extraction of dynamic contrast enhanced (DCE)-MRI parameters in locally advanced breast cancer (LABC), and potentially represent a source of variability.

METHOD

forty patients with forty-two LABC lesions were prospectively enrolled and underwent breast DCE-MRI examination at 3 T. On post-processed dynamic images, enhancing tumor lesions were manually segmented at four different TPs: at the first post-contrast dynamic image in which the lesion was appreciable (TP 1) and at 1, 5 and 10 min after contrast-agent administration (TPs 2, 3 and 4, respectively) and corresponding DCE-MRI parameters were extracted. Friedman's test followed by Bonferroni-adjusted Wilcoxon signed rank test for post-hoc analysis was used to compare DCE-MRI parameters. Intra- and inter-observer reliability of DCE-MRI parameters measurements was assessed using the Intraclass Correlation Coefficient (ICC) analysis.

RESULTS

Ktrans, Kep and iAUC were significantly higher when extracted from ROIs placed at TP1 and progressively decreased from TP 2-4. The intra-observer reliability ranged from good to excellent (ICC's: 0.894 to 0.990). The inter-observer reliability varied from moderate to excellent (0.770 to 0.942). The inter-observer reliability was significantly higher for Ktrans and Kep extracted at TPs1 and 2 as compared to TPs 3 and 4.

CONCLUSIONS

A significant variability of DCE-MRI quantitative parameters occurs when tumor segmentation is performed at different TPs. We suggest to performing tumor delineation at an established TP, preferably the earliest, in order to extract reliable and comparable DCE-MRI data.

Outcomes Associated with First-Line anti-PD-1/ PD-L1 agents vs. Sunitinib in Patients with Sarcomatoid Renal Cell Carcinoma: A Systematic Review and Meta-Analysis

Immunotherapy based on anti PD-1/PD-L1 inhibitors has proven to be more effective than sunitinib in the first-line setting of advanced renal cell carcinoma (RCC). RCC patients with sarcomatoid histology (sRCC) have a poor prognosis and limited therapeutic options. We performed a systematic review and a meta-analysis of randomized-controlled trials (RCTs) of first-line anti PD-1/PDL-1 agents vs. sunitinib, presenting efficacy data in the sub-group of sRCC patients. The systematic research was conducted on Google Scholar, Cochrane Library, PubMed and Embase and updated until 31th January, 2020. Abstracts from ESMO and ASCO (2010–2019) were also reviewed. Full texts and abstracts reporting about RCTs testing first-line anti-PD-1/ PD-L1 agents vs. sunitinib in RCC were included if sRCC sub-group analyses of either PFS (progression-free survival), OS (overall survival) or radiological response rate were available. Pooled data from 3814 RCC patients in the ITT (intention-to-treat) population and from 512 sRCC patients were included in the quantitative synthesis. In the sRCC sub-group vs. the ITT population, pooled estimates of the PFS-HRs were 0.57 (95%: 0.45–0.74) vs. 0.79 (95% CI: 0.70–0.89), respectively, with a statistically meaningful interaction favoring the sRCC sub-group (pooled ratio of the PFS-HRs = 0.64; 95% CI: 0.50–0.82; p < 0.001). Pooled estimates of the difference in CR-R (complete response-rate) achieved with anti-PD-1/PDL-1 agents vs. sunitinib were + 0.10 (95% CI: 0.04–0.16) vs. + 0.04 (95% CI: 0.00–0.07) in the sRCC vs. the non-sRCC sub groups, with a statistically meaningful difference of + 0.06 (95% CI: 0.02–0.10; p = 0.007) favoring the sRCC sub-group. Sarcomatoid histology may be associated with improved efficacy of anti PD-1/PDL-1 agents vs. sunitinib in terms of PFS and CR-R.

Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach

BACKGROUND/AIM

To investigate whether a radiomic machine learning (ML) approach employing texture-analysis (TA) features extracted from primary tumor lesions (PTLs) is able to predict tumor grade (TG) and nodal status (NS) in patients with oropharyngeal (OP) and oral cavity (OC) squamous-cell carcinoma (SCC).

PATIENTS AND METHODS

Contrast-enhanced CT images of 40 patients with OP and OC SCC were post-processed to extract TA features from PTLs. A feature selection method and different ML algorithms were applied to find the most accurate subset of features to predict TG and NS.

RESULTS

For the prediction of TG, the best accuracy (92.9%) was achieved by Naïve Bayes (NB), bagging of NB and K Nearest Neighbor (KNN). For the prediction of NS, J48, NB, bagging of NB and boosting of J48 overcame the accuracy of 90%.

CONCLUSION

A radiomic ML approach applied to PTLs is able to predict TG and NS in patients with OC and OP SCC.

US and MR imaging findings to detect placental adhesion spectrum (PAS) in patients with placenta previa: a comparative systematic study

PURPOSE

To compare the performance US and MR in identifying placental adhesion spectrum (PAS) in placenta previa (PP) and to establish a potential method of image interpretation.

METHODS

US and MR examinations of 51 patients with PP were selected. The presence of imaging signs commonly used to detect PAS was assessed. Penalized logistic regression was performed considering histology as standard of reference; only signs statistically significant (p < 0.05) were considered for ROC and multivariate analysis. The probability of PAS according to the presence of US and/or MR signs was then assessed.

RESULTS

At univariate analysis, loss of retroplacental clear space, myometrial thinning (MT) and placenta lacunar spaces on US, intraplacental dark bands (IDBs), focal interruption of myometrial border (FIMB) and abnormal vascularity (AV) on MR were statistically significant (p < 0.01). Three diagnostic methods for PAS were then developed for both US and MR when at least one (Method 1), two (Method 2) or three (Method 3) imaging signs occurred, respectively. Method 2 for MR showed a significantly (p < 0.05) higher accuracy (91%) compared to the other methods. When MR IDBs and AV as well as IDBs and FIMB were present in combination with US MT the probability of PAS increased from 75 to 90% and from 80 to 91%, respectively.

CONCLUSION

MR demonstrated a higher diagnostic accuracy than US to detect PAS. However, since the combination of MR and US signs could improve the probability to detect PAS, a complementary diagnostic role of these techniques could be considered.

Colorectal cancer: Parametric evaluation of morphological, functional and molecular tomographic imaging

Colorectal cancer (CRC) represents one of the leading causes of tumor-related deaths worldwide. Among the various tools at physicians’ disposal for the diagnostic management of the disease, tomographic imaging (e.g., CT, MRI, and hybrid PET imaging) is considered essential. The qualitative and subjective evaluation of tomographic images is the main approach used to obtain valuable clinical information, although this strategy suffers from both intrinsic and operator-dependent limitations. More recently, advanced imaging techniques have been developed with the aim of overcoming these issues. Such techniques, such as diffusion-weighted MRI and perfusion imaging, were designed for the “in vivo” evaluation of specific biological tissue features in order to describe them in terms of quantitative parameters, which could answer questions difficult to address with conventional imaging alone (e.g., questions related to tissue characterization and prognosis). Furthermore, it has been observed that a large amount of numerical and statistical information is buried inside tomographic images, resulting in their invisibility during conventional assessment. This information can be extracted and represented in terms of quantitative parameters through different processes (e.g., texture analysis). Numerous researchers have focused their work on the significance of these quantitative imaging parameters for the management of CRC patients. In this review, we aimed to focus on evidence reported in the academic literature regarding the application of parametric imaging to the diagnosis, staging and prognosis of CRC while discussing future perspectives and present limitations. While the transition from purely anatomical to quantitative tomographic imaging appears achievable for CRC diagnostics, some essential milestones, such as scanning and analysis standardization and the definition of robust cut-off values, must be achieved before quantitative tomographic imaging can be incorporated into daily clinical practice.

High Prevalence of Spinal Magnetic Resonance Imaging Findings in Asymptomatic Young Adults (18-22 Yrs) Candidate to Air Force Flight

STUDY DESIGN

Cross-sectional, retrospective, magnetic resonance (MR) imaging study, performed during cadets' selection procedures of the Italian Air Force Academy.

OBJECTIVE

To assess the prevalence of spinal MR imaging findings in asymptomatic young adults (18-22 yrs) candidate to Air Force Flight.

SUMMARY OF BACKGROUND DATA

Spinal MR imaging findings are frequently detected in asymptomatic subjects. Literature prevalence data come from studies that analyze different patient populations, in a wide age range and in different spinal tracts. Chronic degenerative disease of the vertebral column often occurs in pilots exposed to high flight acceleration forces, thus resulting crucial for Air Force Academy to exclude vertebral disease in cadets.

METHODS

Three hundred fifty asymptomatic young adults underwent a 3T MR examination of the entire spine. A structured radiological report was set up to classify and calculate the prevalence of spinal MR imaging findings.

RESULTS

Two hundred seventy of 350 subjects (77%) presented spinal MR findings, while 80 of 350 candidates (23%) had no detectable MR imaging findings. One hundred six of 350 (30%) candidates had at least one disc desiccation and 47 of 350 (13%) presented at least one disc narrowing. Disc bulging was found in 176 of 350 (49%) cadets. Sixty-two of 350 (18%) subjects showed disc protrusion while 28 of 350 (8%) had disc extrusion. Forty-five of 350 (13%) candidates presented low grade intervertebral spondylosis and of these 12 had also facet joints spondylosis. Asymptomatic vertebral fractures were observed in 2 of 350 (<1%) cadets.

CONCLUSION

A high rate of MR spinal imaging findings, similar to that of the adult population, was detected in our population of young asymptomatic subjects. Our results suggest that the process of aging spine, which is supposed to begin in the second decade of life, is morphologically appreciable in the immediate postadolescent period and this issue is of crucial importance when selecting military pilots.

LEVEL OF EVIDENCE

4.

Differential diagnosis of benign and malignant vertebral compression fractures using conventional and advanced MRI techniques

Atraumatic vertebral compression fractures (VCFs) are commonly encountered in clinical practice and often represent a diagnostic challenge. MRI plays a major role in the differential diagnosis of benign and malignant VCFs, due to its high contrast resolution and the possibility to obtain quantitative and functional data with the employment of advanced sequences. Computer-aided diagnosis systems are also applied on MRI images for this purpose, showing promising results. In this setting, aim of this pictorial review is to elucidate the role of MRI in the differential diagnosis of VCFs with a specific focus on advanced and post-processing imaging techniques.

The role of dynamic post-contrast T1-w MRI sequence to characterize lipid-rich and lipid-poor adrenal adenomas in comparison to non-adenoma lesions: preliminary results

PURPOSE

The purpose of the article is to compare the features of wash-out (WO) parameters between lipid-rich and lipid-poor adrenal adenomas as well as with a group of non-adenoma adrenal lesions.

METHODS

46 patients (36 F and 10 M, median age 58 years) with unilateral adrenal lesions (35 adenomas, 7 pheochromocytomas, 1 carcinoma, and 3 metastases) were prospectively evaluated; adrenal lesions were divided into adenomas (Group 1) and non-adenomas (Group 2). MR imaging was performed with a 3-Tesla scanner using pre- and post-contrast dedicated sequences. On the basis of the evaluation of qualitative chemical-shift (CS) signal intensity (SI) loss, adrenal adenomas were, respectively, divided in Group 1A (n = 25) as lipid-rich and Group 1B (n = 10) as lipid-poor; non-adenoma adrenal lesions were grouped in Group 2 (n = 11). The following parameters were evaluated: size (mm), CS SI index (%), early (5 min), and delayed (10 min) Relative (R) and Absolute (A) WO values (%).

RESULTS

The comparison of AWO and RWO showed significant (p ≤ 0.05) differences between Group 1A and Groups 1B and 2, both using 5- and 10-min images for calculation; conversely, no differences in these dynamic parameters were found between Group 1B and 2; AWO and RWO values were significantly lower in adrenal lesions of Groups 1B and 2 compared to Group 1A, both using 5- and 10-min images for calculation.

CONCLUSIONS

The quantitative evaluation of WO parameters could not be used to characterize lipid-poor adrenal adenomas for which alternative imaging modalities are required.

Clinical impact of coronal-STIR sequence in a routine lumbar spine MR imaging protocol to investigate low back pain

Aim of this study is to assess the clinical impact of coronal short tau inversion recovery (STIR)-weighted magnetic resonance (MR) sequence, when acquired in a lumbar spine MR imaging protocol, in detecting significant extraspinal imaging findings in patients with low back pain (LBP).We retrospectively evaluated 931 lumbar spine MR examinations of patients with LBP. Extraspinal MR imaging findings were categorized as: probably related to LBP (Category 1), not related to LBP but with relevant implications on patient's care (Category 2), and not related to LBP without significant implications on patient's care (Category 3). For each MR imaging finding was also assessed if it was detectable or not on the conventional sagittal and axial acquisition planes.Of the 931 evaluated MR examinations, 60 (6.4%) showed additional extraspinal MR imaging findings, categorized as follows: 55% (33/60) probably related to LBP (Category 1), 22% (13/60) not related to LBP but with relevant implications on patient's care (Category 2), and 23% (14/60) not related to LBP and without significant implications on patient's care (Category 3). Among categories 1 and 2 (n = 46), the 72% (33/46) of imaging findings were detected only on coronal plane. Coronal-STIR sequence significantly changed patients' diagnostic work-flow in 3.5% (33/931) of cases.Coronal STIR sequence, acquired in a lumbar spine MR imaging protocol to investigate LBP, may aid radiologists in detecting additional extraspinal MR imaging findings that could be related to LBP, addressing to the most appropriate clinical management.

Simultaneous PET/MRI assessment of response to cytotoxic and hormone neo-adjuvant chemotherapy in breast cancer: a preliminary report

The aim of this study was to assess the response to cytotoxic and hormone neo-adjuvant chemotherapy in four patients with locally advanced breast cancer by simultaneous PET/MRI. Four patients with locally advanced breast cancer underwent simultaneous PET/MRI of the breast using a 3 T Biograph mMR before and after neo-adjuvant chemotherapy (two patients were treated with hormone-therapy and two patients were treated with cytotoxic chemotherapy). Morpho-structural tumoral features and tumor size were assessed; area value, metabolic (SUV and MTV) and functional (ADC, K _trans, V _e, k _ep and iAUC) data were obtained by positioning regions of interest. A comparison of all parameters between the pre- and post-treatment PET/MRI examinations and between the two different therapeutic schedules was assessed. In patients treated with cytotoxic chemotherapy and classified as PR, there was a significant reduction of post-treatment morphological, metabolic and functional parameters. In a patient treated with hormone therapy, classified as SD, there was an increase of all post-treatment perfusion parameters, a substantially stable ADC value and a poor reduction of lesion size and of maximum SUV (SUV_max) values; the last patient, treated with hormone therapy and classified as PR, showed a significant reduction of lesion size and SUV_max values with a reduction of perfusion parameters and substantially stable ADC values. Multiparametric evaluation with simultaneous PET/MRI could be a useful tool to assess the response to cytotoxic and hormone neo-adjuvant chemotherapy in patients with breast cancer. Future studies in a larger cohort of patients are warranted to confirm the results of this preliminary study.