Baseline circulating tumour DNA and total metabolic tumour volume as early outcome predictors in aggressive large B-cell lymphoma. A real-world 112-patient cohort.

Approximately 20%-50% of patients with large B-cell lymphoma (LBCL) experience poor outcomes. We aimed to evaluate the combined prognostic value of circulating tumour DNA (ctDNA) and total metabolic tumour volume (TMTV) in LBCL. This observational single-centre study included 112 newly diagnosed LBCL patients, receiving R-CHOP/R-CHOP-like chemotherapies. CtDNA load was calculated following next-generation sequencing of cell-free DNA (cfDNA) using a targeted 40-gene lymphopanel. TMTV was measured using a fully automated artificial intelligence-based method for lymphoma lesion segmentation. CtDNA was detected in cfDNA samples from 95 patients with a median concentration of 3.15 log haploid genome equivalents per mL. TMTV measurements were available for 102 patients. The median TMTV was 501 mL. High ctDNA load (>3.57 log hGE/mL) or high TMTV (>200 mL) were associated with shorter 1-year PFS (44% vs. 83%, p < 0.001 and 64% vs. 97%, p = 0.002, respectively). When combined, three prognostic groups were identified. The shortest PFS was observed when both TMTV and ctDNA load were high (p < 0.001). Even with a short follow up, combining ctDNA load with TMTV improved the risk stratification of patients with aggressive LBCL. In the near future, very high-risk patients could benefit from CAR T-cell therapy or bispecific antibodies as first-line treatments.

[18F]DCFPyL PET/CT versus [18F]fluoromethylcholine PET/CT in Biochemical Recurrence of Prostate Cancer (PYTHON): a prospective, open label, cross-over, comparative study.

PURPOSE: Primary objective was to compare the per-patient detection rates (DR) of [18F]DCFPyL versus [18F]fluoromethylcholine positron emission tomography/computed tomography (PET/CT), in patients with first prostate cancer (PCa) biochemical recurrence (BCR). Secondary endpoints included safety and impact on patient management (PM). METHODS: This was a prospective, open label, cross-over, comparative study with randomized treatment administration of [18F]DCFPyL (investigational medicinal product) or [18F]fluoromethylcholine (comparator). Men with rising prostate-specific antigen (PSA) after initial curative therapy were enrolled. [18F]DCFPyL and [18F]fluoromethylcholine PET/CTs were performed within a maximum time interval of 12 days. DR was defined as the percentage of positive PET/CT scans identified by 3 central imaging readers. PM was assessed by comparing the proposed pre-PET/CT treatment with the local treatment", defined after considering both PET/CTs. RESULTS: A total of 205 patients with first BCR after radical prostatectomy (73%; median PSA = 0.46 ng/ml [CI 0.16;27.0]) or radiation therapy (27%; median PSA = 4.23 ng/ml [CI 1.4;98.6]) underwent [18F]DCFPyL- and/or [18F]fluoromethylcholine -PET/CTs, between July and December 2020, at 22 European sites. 201 patients completed the study. The per-patient DR was significantly higher for [18F]DCFPyL- compared to [18F]fluoromethylcholine -PET/CTs (58% (117/201 patients) vs. 40% (81/201 patients), p < 0.0001). DR increased with higher PSA values for both tracers (PSA ≤ 0.5 ng/ml: 26/74 (35%) vs. 22/74 (30%); PSA 0.5 to ≤ 1.0 ng/ml: 17/31 (55%) vs. 10/31 (32%); PSA 1.01 to < 2.0 ng/ml: 13/19 (68%) vs. 6/19 (32%);PSA > 2.0: 50/57 (88%) vs. 39/57 (68%) for [18F]DCFPyL- and [18F]fluoromethylcholine -PET/CT, respectively). [18F]DCFPyL PET/CT had an impact on PM in 44% (90/204) of patients versus 29% (58/202) for [18F]fluoromethylcholine. Overall, no drug-related nor serious adverse events were observed. CONCLUSIONS: The primary endpoint of this study was achieved, confirming a significantly higher detection rate for [18F]DCFPyL compared to [18F]fluoromethylcholine, in men with first BCR of PCa, across a wide PSA range. [18F]DCFPyL was safe and well tolerated.

Optimization of whole-body 2-[18F]FDG-PET/MRI imaging protocol for the initial staging of patients with myeloma.

OBJECTIVE: To determine the optimal 2-[18F]FDG-PET/MRI imaging protocol for the initial staging of patients with suspected or confirmed multiple myeloma. METHODS: Radiologists and nuclear medicine specialists reviewed all PET/MRI exams of 104 patients with a monoclonal gammopathy (MG). The presence of focal and diffuse bone marrow involvement (BMI) was assessed using 4 different image datasets: WB-MRI, PET, WB-PET/MRI, and WB-DCE-PET/MRI. A reference standard was established by a panel review of all baseline and follow-up imaging, and biological and pathological information. The diagnostic performance for each image dataset to detect BMI was evaluated and compared (Fisher's exact test). RESULTS: Sensitivity, specificity, and accuracy for focal BMI of WB-MRI was 87%, 97%, and 92%; of PET was 78%, 97%, and 95%; of WB-PET/MRI was 93%, 97%, and 95%; and of WB-DCE-PET/MRI was 93%, 97%, and 95%, respectively. WB-PET/MRI and WB-DCE-PET/MRI were statistically superior to PET (p = 0.036) without decreasing specificity. The sensitivity, specificity, and accuracy of WB-MRI for diffuse BMI detection was 91%, 80%, and 85%; of 3DT1-PET was 53%, 89%, and 74%; of WB-PET/MRI was 98%, 66%, and 79%; and of WB-DCE-PET/MRI was 98%, 59%, and 75%, respectively. PET lacked sensitivity compared to all other dataset studies (p < 0.0001). WB-MRI had the best accuracy without reaching statistical significance when compared to the other datasets. CONCLUSION: The WB-PET/MRI dataset including T1 and T2 Dixon, WB-DWI, and PET images provides optimal diagnostic performance to detect both focal lesions and diffuse BMI, with limited added value of WB-DCE for baseline staging of patients with MG. Key Points • The combination of morphological and functional MRI sequences and metabolic (2-[18F]FDG-PET) images increases the diagnostic performance of PET/MRI to detect focal bone lesions. • The adjunction of dynamic contrast-enhanced sequences did not improve diagnostic performance.

Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.

PURPOSE: Lymphoma lesion detection and segmentation on whole-body FDG-PET/CT are a challenging task because of the diversity of involved nodes, organs or physiological uptakes. We sought to investigate the performances of a three-dimensional (3D) convolutional neural network (CNN) to automatically segment total metabolic tumour volume (TMTV) in large datasets of patients with diffuse large B cell lymphoma (DLBCL). METHODS: The dataset contained pre-therapy FDG-PET/CT from 733 DLBCL patients of 2 prospective LYmphoma Study Association (LYSA) trials. The first cohort (n = 639) was used for training using a 5-fold cross validation scheme. The second cohort (n = 94) was used for external validation of TMTV predictions. Ground truth masks were manually obtained after a 41% SUVmax adaptive thresholding of lymphoma lesions. A 3D U-net architecture with 2 input channels for PET and CT was trained on patches randomly sampled within PET/CTs with a summed cross entropy and Dice similarity coefficient (DSC) loss. Segmentation performance was assessed by the DSC and Jaccard coefficients. Finally, TMTV predictions were validated on the second independent cohort. RESULTS: Mean DSC and Jaccard coefficients (± standard deviation) in the validations set were 0.73 ± 0.20 and 0.68 ± 0.21, respectively. An underestimation of mean TMTV by - 12 mL (2.8%) ± 263 was found in the validation sets of the first cohort (P = 0.27). In the second cohort, an underestimation of mean TMTV by - 116 mL (20.8%) ± 425 was statistically significant (P = 0.01). CONCLUSION: Our CNN is a promising tool for automatic detection and segmentation of lymphoma lesions, despite slight underestimation of TMTV. The fully automatic and open-source features of this CNN will allow to increase both dissemination in routine practice and reproducibility of TMTV assessment in lymphoma patients.

Extracardiac soft tissue uptake, evidenced on early 99mTc-HMDP SPECT/CT, helps typing cardiac amyloidosis and demonstrates high prognostic value.

PURPOSE: Increased cardiac uptake (CU) on early-phase 99mTc-HMDP scintigraphy has demonstrated diagnostic and prognostic values in amyloid transthyretin (ATTR) cardiac amyloidosis (CA). Extracardiac uptake (ECU) has been poorly studied. We assessed the clinical value of ECU, in combination with CU, on 99mTc-HMDP scintigraphy using a novel Methodological Amyloidosis Diagnostic Index (MADI). METHODS: We reviewed all patients referred for suspicion of CA, who underwent 99mTc-HMDP scintigraphy over an 8-year period. ECU, CU, and MADI were determined: MADI0 = neither ECU or CU, MADI1 = ECU alone, MADI2 = CU alone, and MADI3 = ECU + CU. RESULTS: Of 308 eligible patients, 247 had CA, including 75 ATTRv, 107 ATTRwt, and 65 light-chain (AL), while 61 had another cardiopathy (controls). ECU was observed in 29% of CA and 3% of controls. Most frequent sites of ECU were pleuropulmonary (16% of CA, 3% of controls) followed by the digestive tract and subcutaneous tissues. The liver and spleen ECU was only observed in AL-CA (n = 8). CU was only observed in CA patients (n = 187), of whom 182 had ATTR-CA vs. 5 AL-CA, P < 0.001. MADI0 was only observed in controls (97%) and in AL-CA (60%). MADI1 was mainly observed in AL-CA (positive predictive value, PPV = 91%) while MADI2/3 were more frequent in ATTR-CA (PPV = 97%), P < 0.0001. MADI > 0 vs. MADI0 in AL and MADI3 vs. MADI2 in ATTR were associated with a worse prognosis (P = 0.03 and P = 0.002, respectively). CONCLUSIONS: ECU combined with CU demonstrates high diagnostic and prognostic values in CA patients. MADI seems an easy and reliable score in clinical practice.

Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer.

INTRODUCTION: The aim of the study was to extract anthropometric measures from CT by deep learning and to evaluate their prognostic value in patients with non-small-cell lung cancer (NSCLC). METHODS: A convolutional neural network was trained to perform automatic segmentation of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and muscular body mass (MBM) from low-dose CT images in 189 patients with NSCLC who underwent pretherapy PET/CT. After a fivefold cross-validation in a subset of 35 patients, anthropometric measures extracted by deep learning were normalized to the body surface area (BSA) to control the various patient morphologies. VAT/SAT ratio and clinical parameters were included in a Cox proportional-hazards model for progression-free survival (PFS) and overall survival (OS). RESULTS: Inference time for a whole volume was about 3 s. Mean Dice similarity coefficients in the validation set were 0.95, 0.93, and 0.91 for SAT, VAT, and MBM, respectively. For PFS prediction, T-stage, N-stage, chemotherapy, radiation therapy, and VAT/SAT ratio were associated with disease progression on univariate analysis. On multivariate analysis, only N-stage (HR = 1.7 [1.2-2.4]; p = 0.006), radiation therapy (HR = 2.4 [1.0-5.4]; p = 0.04), and VAT/SAT ratio (HR = 10.0 [2.7-37.9]; p < 0.001) remained significant prognosticators. For OS, male gender, smoking status, N-stage, a lower SAT/BSA ratio, and a higher VAT/SAT ratio were associated with mortality on univariate analysis. On multivariate analysis, male gender (HR = 2.8 [1.2-6.7]; p = 0.02), N-stage (HR = 2.1 [1.5-2.9]; p < 0.001), and the VAT/SAT ratio (HR = 7.9 [1.7-37.1]; p < 0.001) remained significant prognosticators. CONCLUSION: The BSA-normalized VAT/SAT ratio is an independent predictor of both PFS and OS in NSCLC patients. KEY POINTS: • Deep learning will make CT-derived anthropometric measures clinically usable as they are currently too time-consuming to calculate in routine practice. • Whole-body CT-derived anthropometrics in non-small-cell lung cancer are associated with progression-free survival and overall survival. • A priori medical knowledge can be implemented in the neural network loss function calculation.

Apical sparing pattern of left ventricular myocardial 99mTc-HMDP uptake in patients with transthyretin cardiac amyloidosis.

BACKGROUND: A decreased longitudinal strain in basal segments with a base-to-apex gradient has been described in patients with cardiac amyloidosis (CA). OBJECTIVES: Aim was to investigate the left ventricular (LV) regional distribution of early-phase 99mTc-Hydroxymethylene diphosphonate (99mTc-HMDP) uptake in patients with transthyretin-related cardiac amyloidosis (TTR-CA). METHODS: All patients underwent a whole-body planar 99mTc-HMDP scintigraphy acquired at 10-min post-injection (early-phase) followed by a thorax SPECT/CT. The segmental uptake (expressed as % of maximal myocardial HMDP uptake) was investigated on the AHA 17-segment model and 3-segment model (basal, mid-cavity, apical). RESULTS: Sixty-one TTR-CA patients were included of whom 29 were wild-type (wt-TTR-CA) and 32 had hereditary TTR-CA (m-TTR-CA). Early myocardial 99mTc-HMDP uptake occurred in all TTR-CA. In all patients, segmental analysis of the LV myocardial distribution of 99mTc-HMDP uptake showed an increased median uptake (interquartile range) in basal/mid-cavity segments compared to the lowest median uptake of apical segments (respectively, 79% [72%-86%] vs. 72% [64%-81%]; P < 10-6). This pattern was similar in wt-TTR-CA group (78% [70%-84%] vs. 70% [61%-81%]; P < 10-6), in m-TTR-CA group (80% [74%-86%] vs. 73 [66%-82%]; P < 10-7) and remained constant independently of the TTR mutation subtype with P ranging 10-5 to 0.03. CONCLUSIONS: Early-phase myocardial scintigraphy identified regional distribution of 99mTc-HMDP uptake characterized by a base-to-apex gradient, corroborating echocardiographic, and cardiac magnetic resonance findings. This apical sparing pattern was similar across TTR-CA and TTR mutation subtypes.

Prognostic Value of 18F-FDG PET Radiomics Features at Baseline in PET-Guided Consolidation Strategy in Diffuse Large B-Cell Lymphoma: A Machine-Learning Analysis from the GAINED Study.

The results of the GA in Newly Diagnosed Diffuse Large B-Cell Lymphoma (GAINED) study demonstrated the success of an 18F-FDG PET-driven approach to allow early identification-for intensification therapy-of diffuse large B-cell lymphoma patients with a high risk of relapse. Besides, some works have reported the prognostic value of baseline PET radiomics features (RFs). This work investigated the added value of such biomarkers on survival of patients involved in the GAINED protocol. Methods: Conventional PET features and RFs were computed from 18F-FDG PET at baseline and extracted using different volume definitions (patient level, largest lesion, and hottest lesion). Clinical features and the consolidation treatment information were also considered in the model. Two machine-learning pipelines were trained with 80% of patients and tested on the remaining 20%. The training was repeated 100 times to highlight the test set variability. For the 2-y progression-free survival (PFS) outcome, the pipeline included a data augmentation and an elastic net logistic regression model. Results for different feature groups were compared using the mean area under the curve (AUC). For the survival outcome, the pipeline included a Cox univariate model to select the features. Then, the model included a split between high- and low-risk patients using the median of a regression score based on the coefficients of a penalized Cox multivariate approach. The log-rank test P values over the 100 loops were compared with a Wilcoxon signed-ranked test. Results: In total, 545 patients were included for the 2-y PFS classification and 561 for survival analysis. Clinical features alone, consolidation features alone, conventional PET features, and RFs extracted at patient level achieved an AUC of, respectively, 0.65 ± 0.07, 0.64 ± 0.06, 0.60 ± 0.07, and 0.62 ± 0.07 (0.62 ± 0.07 for the largest lesion and 0.54 ± 0.07 for the hottest). Combining clinical features with the consolidation features led to the best AUC (0.72 ± 0.06). Adding conventional PET features or RFs did not improve the results. For survival, the log-rank P values of the model involving clinical and consolidation features together were significantly smaller than all combined-feature groups (P < 0.007). Conclusion: The results showed that a concatenation of multimodal features coupled with a simple machine-learning model does not seem to improve the results in terms of 2-y PFS classification and PFS prediction for patient treated according to the GAINED protocol.

Is There a Role of Artificial Intelligence in Preclinical Imaging?

This review provides an overview of the current opportunities for integrating artificial intelligence methods into the field of preclinical imaging research in nuclear medicine. The growing demand for imaging agents and therapeutics that are adapted to specific tumor phenotypes can be excellently served by the evolving multiple capabilities of molecular imaging and theranostics. However, the increasing demand for rapid development of novel, specific radioligands with minimal side effects that excel in diagnostic imaging and achieve significant therapeutic effects requires a challenging preclinical pipeline: from target identification through chemical, physical, and biological development to the conduct of clinical trials, coupled with dosimetry and various pre, interim, and post-treatment staging images to create a translational feedback loop for evaluating the efficacy of diagnostic or therapeutic ligands. In virtually all areas of this pipeline, the use of artificial intelligence and in particular deep-learning systems such as neural networks could not only address the above-mentioned challenges, but also provide insights that would not have been possible without their use. In the future, we expect that not only the clinical aspects of nuclear medicine will be supported by artificial intelligence, but that there will also be a general shift toward artificial intelligence-assisted in silico research that will address the increasingly complex nature of identifying targets for cancer patients and developing radioligands.

Validation of the ΔSUVmax for Interim PET Interpretation in Diffuse Large B-Cell Lymphoma on the Basis of the GAINED Clinical Trial.

The GAINED phase 3 trial (ClinicalTrials.gov identifier: NCT01659099) evaluated a PET-driven consolidative strategy in patients with diffuse large B-cell lymphoma. In this post hoc analysis, we aimed to compare the prognostic value of the per-protocol PET interpretation criteria (Menton 2011 consensus) with the change in the SUVmax (ΔSUVmax) alone. Methods: Real-time central review of 18F-FDG PET/CT was performed in 581 patients after 2 cycles (PET2) and 4 cycles (PET4) of immunochemotherapy using the Menton 2011 criteria, combining the ΔSUVmax (cutoffs of 66% and 70% at PET2 and PET4, respectively) and the Deauville scale. In "special cases," when the baseline SUVmax was less than 10.0 or the interim residual tumor SUVmax was greater than 5.0, the Menton 2011 experts' consensus agreed that the ΔSUVmax may not be reliable and that the Deauville score is preferable. Prognostic values of Menton 2011 and ΔSUVmax were evaluated by Kaplan-Meier analyses in terms of progression-free survival (PFS). Results: Seventeen percent of patients at PET2 (100/581) and 8% at PET4 (49/581) had PET-negative results by ΔSUVmax but were considered to have PET-positive results according to Menton 2011 with residual SUVmax of greater than 5.0. For the population with PET2-positive results, 2-y PFS was 70% (range, 58%-80%) with ΔSUVmax alone, whereas the outcome tended to be better for those who were considered to have PET-positive results by Menton 2011, 81% (range, 72%-87%). Conversely, all 10 patients with baseline SUVmax of less than 10.0 had PET2-positive results by ΔSUVmax but were considered to have PET2-negative results by Menton 2011. These patients had the same 2-y PFS as patients with PET2-negative/PET4-negative results, indicating that the ΔSUVmax yielded false-positive results in this situation. Conclusion: We recommend the use of the ΔSUVmax alone rather than the Menton 2011 criteria for assessing the interim metabolic response in patients with diffuse large B-cell lymphoma, except when the baseline SUVmax is less than 10.0.

Deep Learning on Bone Scintigraphy to Detect Abnormal Cardiac Uptake at Risk of Cardiac Amyloidosis.

BACKGROUND: Cardiac uptake on technetium-99m whole-body scintigraphy (WBS) is almost pathognomonic of transthyretin cardiac amyloidosis. The rare false positives are often related to light-chain cardiac amyloidosis. However, this scintigraphic feature remains largely unknown, leading to misdiagnosis despite characteristic images. A retrospective review of all WBSs in a hospital database to detect those with cardiac uptake may allow the identification of undiagnosed patients. OBJECTIVES: The authors sought to develop and validate a deep learning-based model that automatically detects significant cardiac uptake (Perugini grade ≥2) on WBS from large hospital databases in order to retrieve patients at risk of cardiac amyloidosis. METHODS: The model is based on a convolutional neural network with image-level labels. The performance evaluation was performed with C-statistics using a 5-fold cross-validation scheme stratified so that the proportion of positive and negative WBSs remained constant across folds and using an external validation data set. RESULTS: The training data set consisted of 3,048 images: 281 positives (Perugini grade ≥2) and 2,767 negatives. The external validation data set consisted of 1,633 images: 102 positives and 1,531 negatives. The performance of the 5-fold cross-validation and external validation was as follows: 98.9% (± 1.0) and 96.1% for sensitivity, 99.5% (± 0.4) and 99.5% for specificity, and 0.999 (SD = 0.000) and 0.999 for the area under the curve of the receiver-operating characteristic curves. Sex, age <90 years, body mass index, injection-acquisition delay, radionuclides, and the indication of WBS only slightly affected performances. CONCLUSIONS: The authors' detection model is effective at identifying patients with cardiac uptake Perugini grade ≥2 on WBS and may help in the diagnosis of patients with cardiac amyloidosis.

Axicabtagene ciloleucel as second-line therapy in large B cell lymphoma ineligible for autologous stem cell transplantation: a phase 2 trial.

Axicabtagene ciloleucel (axi-cel) demonstrated superior efficacy compared to standard of care as second-line therapy in patients with high-risk relapsed/refractory (R/R) large B cell lymphoma (LBCL) considered eligible for autologous stem cell transplantation (ASCT); however, in clinical practice, roughly half of patients with R/R LBCL are deemed unsuitable candidates for ASCT. The efficacy of axi-cel remains to be ascertained in transplant-ineligible patients. ALYCANTE, an open-label, phase 2 study, evaluated axi-cel as a second-line therapy in 62 patients with R/R LBCL who were considered ineligible for ASCT. The primary end point was investigator-assessed complete metabolic response at 3 months from the axi-cel infusion. Key secondary end points included progression-free survival, overall survival and safety. The study met its primary end point with a complete metabolic response of 71.0% (95% confidence interval, 58.1-81.8%) at 3 months. With a median follow-up of 12.0 months (range, 2.1-17.9), median progression-free survival was 11.8 months (95% confidence interval, 8.4-not reached) and overall survival was not reached. There was no unexpected toxicity. Grade 3-4 cytokine release syndrome and neurologic events occurred in 8.1% and 14.5% of patients, respectively. These results support axi-cel as second-line therapy in patients with R/R LBCL ineligible for ASCT. ClinicalTrials.gov Identifier: NCT04531046 .

Whole-Body Functional MRI and PET/MRI in Multiple Myeloma.

Bone disease is one of the major features of multiple myeloma (MM), and imaging has a pivotal role in both diagnosis and follow-up. Whole-body magnetic resonance imaging (MRI) is recognized as the gold standard for the detection of bone marrow involvement, owing to its high sensitivity. The use of functional MRI sequences further improved the performances of whole-body MRI in the setting of MM. Whole-body diffusion-weighted (DW) MRI is the most attractive functional technique and its systematic implementation in general clinical practice is now recommended by the International Myeloma Working Group. Whole-body dynamic contrast-enhanced (DCE) MRI might provide further information on lesions vascularity and help evaluate response to treatment. Whole Body PET/MRI is an emerging hybrid imaging technique that offers the opportunity to combine information on morphology, fat content of bone marrow, bone marrow cellularity and vascularization, and metabolic activity. Whole-body PET/MRI allows a one-stop-shop examination, including the most sensitive technique for detecting bone marrow involvement, and the most recognized technique for treatment response evaluation. This review aims at providing an overview on the value of whole-body MRI, including DW and DCE MRI, and combined whole-body 18F-FDG PET/MRI in diagnosis, staging, and response evaluation in patients with MM.

Prognostic Impact of Stromal Immune Infiltration before and after Neoadjuvant Chemotherapy (NAC) in Triple Negative Inflammatory Breast Cancers (TNIBC) Treated with Dose-Dense Dose-Intense NAC.

Inflammatory breast cancers are very aggressive, and among them, triple negative breast cancer (TNBC) has the worst prognosis. While many studies have investigated the association between tumor-infiltrating lymphocytes (TIL) before neoadjuvant chemotherapy (NAC) and outcome in TNBC, the impact of post-NAC TIL and TIL variation in triple negative inflammatory breast cancer (TNIBC) outcome is unknown. Between January 2010 to December 2018, all patients with TNIBC seen at the breast disease unit (Saint-Louis Hospital) were treated with dose-dense dose-intense NAC. The main objective of the study was to determine factors associated with event-free survival (EFS), particularly pathological complete response (pCR), pre- and post-NAC TIL, delta TIL and post-NAC lymphovascular invasion (LVI). After univariate analysis, post-NAC LVI (HR 2.06; CI 1.13-3.74; p = 0.02), high post-NAC TIL (HR 1.81; CI 1.07-3.06; p = 0.03) and positive delta TIL (HR 2.20; CI 1.36-3.52; p = 0.001) were significantly associated with impaired EFS. After multivariate analysis, only a positive TIL variation remained negatively associated with EFS (HR 1.88; CI 1.05-3.35; p = 0.01). TNIBC patients treated with intensive NAC who present TIL enrichment after NAC have a high risk of relapse, which could be used as a prognostic marker in TNIBC and could help to choose adjuvant post-NAC treatment.

18F-FDG Whole-body PET/MRI of a 30-Week Pregnant Woman With Breast Cancer Revealed Interesting Fetal Findings.

A 35-year-old 30-week pregnant woman was referred to our institution for initial staging of a triple negative invasive ductal carcinoma of the left breast. To avoid fetal radiation exposure from CT, a whole-body F-FDG PET/MRI was performed. Simultaneous acquisition of PET, T1-, T2-, and diffusion-weighted sequences revealed left axillary node extension and no distant metastases. Fetal radiation dose was estimated at 1.9 mGy. Interestingly, low fetal brain uptake and high symmetrical myocardial metabolism in both ventricles were found. Delivery was induced at 37 weeks of amenorrhea, and the patient underwent 4 cycles of neoadjuvant chemotherapy.

Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction.

One of the main technical challenges of PET/MRI is to achieve an accurate PET attenuation correction (AC) estimation. In current systems, AC is accomplished by generating an MRI-based surrogate computed tomography (CT) from which AC-maps are derived. Nevertheless, all techniques currently implemented in clinical routine suffer from bias. We present here a convolutional neural network (CNN) that generated AC-maps from Zero Echo Time (ZTE) MR images. Seventy patients referred to our institution for 18FDG-PET/MR exam (SIGNA PET/MR, GE Healthcare) as part of the investigation of suspected dementia, were included. 23 patients were added to the training set of the manufacturer and 47 were used for validation. Brain computed tomography (CT) scan, two-point LAVA-flex MRI (for atlas-based AC) and ZTE-MRI were available in all patients. Three AC methods were evaluated and compared to CT-based AC (CTAC): one based on a single head-atlas, one based on ZTE-segmentation and one CNN with a 3D U-net architecture to generate AC maps from ZTE MR images. Impact on brain metabolism was evaluated combining voxel and regions-of-interest based analyses with CTAC set as reference. The U-net AC method yielded the lowest bias, the lowest inter-individual and inter-regional variability compared to PET images reconstructed with ZTE and Atlas methods. The impact on brain metabolism was negligible with average errors of -0.2% in most cortical regions. These results suggest that the U-net AC is more reliable for correcting photon attenuation in brain FDG-PET/MR than atlas-AC and ZTE-AC methods.

Automatic lesion detection and segmentation of 18F-FET PET in gliomas: A full 3D U-Net convolutional neural network study.

INTRODUCTION: Amino-acids positron emission tomography (PET) is increasingly used in the diagnostic workup of patients with gliomas, including differential diagnosis, evaluation of tumor extension, treatment planning and follow-up. Recently, progresses of computer vision and machine learning have been translated for medical imaging. Aim was to demonstrate the feasibility of an automated 18F-fluoro-ethyl-tyrosine (18F-FET) PET lesion detection and segmentation relying on a full 3D U-Net Convolutional Neural Network (CNN). METHODS: All dynamic 18F-FET PET brain image volumes were temporally realigned to the first dynamic acquisition, coregistered and spatially normalized onto the Montreal Neurological Institute template. Ground truth segmentations were obtained using manual delineation and thresholding (1.3 x background). The volumetric CNN was implemented based on a modified Keras implementation of a U-Net library with 3 layers for the encoding and decoding paths. Dice similarity coefficient (DSC) was used as an accuracy measure of segmentation. RESULTS: Thirty-seven patients were included (26 [70%] in the training set and 11 [30%] in the validation set). All 11 lesions were accurately detected with no false positive, resulting in a sensitivity and a specificity for the detection at the tumor level of 100%. After 150 epochs, DSC reached 0.7924 in the training set and 0.7911 in the validation set. After morphological dilatation and fixed thresholding of the predicted U-Net mask a substantial improvement of the DSC to 0.8231 (+ 4.1%) was noted. At the voxel level, this segmentation led to a 0.88 sensitivity [95% CI, 87.1 to, 88.2%] a 0.99 specificity [99.9 to 99.9%], a 0.78 positive predictive value: [76.9 to 78.3%], and a 0.99 negative predictive value [99.9 to 99.9%]. CONCLUSIONS: With relatively high performance, it was proposed the first full 3D automated procedure for segmentation of 18F-FET PET brain images of patients with different gliomas using a U-Net CNN architecture.

Signature of survival: a 18F-FDG PET based whole-liver radiomic analysis predicts survival after 90Y-TARE for hepatocellular carcinoma.

PURPOSE: To generate a predictive whole-liver radiomics scoring system for progression-free survival (PFS) and overall survival (OS) in patients undergoing transarterial radioembolization using Yttrium-90 (90Y-TARE) for unresectable hepatocellular carcinoma (uHCC). RESULTS: The generated pPET-RadScores were significantly correlated with survival for PFS (median of 11.4 mo [95% confidence interval CI: 6.3-16.5 mo] in low-risk group [PFS-pPET-RadScore < 0.09] vs. 4.0 mo [95% CI: 2.3-5.7 mo] in high-risk group [PFS-pPET-RadScore > 0.09]; P = 0.0004) and OS (median of 20.3 mo [95% CI: 5.7-35 mo] in low-risk group [OS-pPET-RadScore < 0.11] vs. 7.7 mo [95% CI: 6.0-9.5 mo] in high-risk group [OS-pPET-RadScore > 0.11]; P = 0.007). The multivariate analysis confirmed PFS-pPET-RadScore (P = 0.006) and OS-pPET-RadScore (P = 0.001) as independent negative predictors. CONCLUSION: Pretreatment 18F-FDG PET whole-liver radiomics signature appears as an independent negative predictor for PFS and OS in patients undergoing 90Y-TARE for uHCC. METHODS: Pretreatment 18F-FDG PET of 47 consecutive patients undergoing 90Y-TARE for uHCC (31 resin spheres, 16 glass spheres) were retrospectively analyzed. For each patient, based on PET radiomics signature from whole-liver semi-automatic segmentation, PFS and OS predictive PET-radiomics scores (pPET-RadScores) were obtained using LASSO Cox regression. Using X-tile software, the optimal score to predict PFS (PFS-pPET-RadScore) and OS (OS-pPET-RadScore) served as cutoff to separate high and low-risk patients. Survival curves were estimated using the Kaplan-Meier method. The prognostic value of PFS and OS-pPET-RadScore, Barcelona-Clinic Liver Cancer staging system and serum alpha-fetoprotein level was analyzed to predict PFS and OS in multivariate analysis.