Dynamisk MR-avbildning av pasienter med brystkreft : etablering og sammenligning av ulike analysemetoder for vevsperfusjon og kapillær permeabilitet

Globalt er brystkreft den kreftformen som rammer flest kvinner, og radiologisk avbildning ved bruk av mammografi og ultralyd er i dag primære utredningsmodaliteter for deteksjon og karaterisering av denne kreftformen. Imidlertid demonstrerer disse radiologiske bildemodalitetene begrensninger med hensyn til den diagnostiske prestasjonen og viser generelt en lav spesifisitet, spesielt ved vurdering av unge kvinner. Dynamisk kontrastforsterket MR-avbildning har de siste tiårene fremstått som en lovende metode for evalueringen av pasienter med brystkreft. Denne suksessen skyldes metodenes evne til å identifisere fysiologiske forskjeller i ulike cancervev gjennom beskrivelsen av kontrastmiddelets distrubusjon i vevet over tid. Ved å analysere denne dynamiske distribusjonen gjennom relevante kinetiske modeller, kan en estimere fysiologisk-assosierte biomarkører som kan bidra til en forbedret karakterisering av brystlesjoner. Bildebaserte biomarkører som demonstrerer en signifikant evne til å differensiere mellom ulike typer lesjoner, kan da bistå den diagnostiske vurderingen av pasienter med brystkreft og dermed øke den diagnostiske prestasjonen sammenlignet med konvensjonelle utredningsmodaliteter. I denne oppgaven ble det utført en høy temporal dynamisk kontrastforsterket MR-avbildning av 40 pasienter med totalt 41 brystlesjoner. Ulike akvisisjonsmetoder og kinetikkmodeller ble evaluert. Det dynamiske kontrastforløpet ble beskrevet med to ulike metoder: Deskriptiv evaluering av signalintensitetens dynamiske forandringsmønster og ved en kvantitativ evaluering av kontrastmiddelets tidsavhengige distribusjon. Histopatologisk diagnose forelå i alle pasienter. I den kvantitative beskrivelsen av brystlesjonenes fysiologiske og anatomiske egenskaper ble det utført en matematisk modellering av det observerte kontrastforløpet ved å bruke en farmakokinetisk to-roms modell. Denne modelleringen forutsetter bestemmelsen av det individuelle kontrastforløpet i pasientens blodplasma ved hjelp av en arteriell inputfunksjon (AIF). Modellen tillater en kvantitativ estimering av overføringskonstantene Ktrans og kep, samt volumfraksjonene til plasma og det ekstravaskulære ekstracellulære rom (vp og ve). Da det viste seg at en AIF-estimering var vanskelig å oppnå i alle pasienter, ble det i tillegg gjort analyse med bruk av en idealisert pasient-uavhengig AIF. I den deskriptive evalueringen identifiseres egenskaper ved lesjonenes kontrastforløp gjennom fem forskjellige biomarkører. Disse inkluderer den dynamiske signalkurvens tid til maksimumverdi (TTP), arealet under kurven (AUC), maksimal signalforsterkning (Peakenh), samt signalkurvens innvaskings og utvaskingsrate (Wash-in og Wash-out). Bruken av et dobbelt-ekko opptak tillater estimeringen av vevets transversale relaksasjonsrate, R2*, under forutsetning av en mono-eksponentiell signalendring som funksjon av ekkotid. I denne oppgaven ble den dynamiske R2*-kurven evaluert ved å estimere den maksimale R2*-forsterkningen basert på vevets prekontrastverdi. De forskjellige biomarkørene ble analysert mot histologisk patologi ved å anvende statistiske signifikanstester med hensyn på deres prediktive styrke, samt diagnostiske tester med hensyn på deres sensitivitet, spesifisitet og diagnostiske nøyaktighet. I tillegg ble logistisk regresjon utført for å oppnå en optimal tilpasning mellom pasientenes histologi og biomarkørenes estimerte verdier. En viktig hypotese i denne oppgaven var at tumors heterogenitet er en betydningsfull faktor i den diagnostiske evauleringen av brystlesjoner. I en heterogen 8 tumor kan den estimerte gjennomsnittlige biomarkørverdien overse regionale forskjeller, og da de mest aggressive og klinisk avgjørende regionene av tumor. I denne oppgaven ble derfor de forskjellige biomarkørene estimert fra ulike deler av tumorvolumets verdifordeling. Dette innebærer estimeringen av tumorvolumets gjennomsnittlige biomarkørverdi samt en rekke persentilverdier. På denne måten kan mulige små maligne områder i en større heterogen tumor identifiseres. Det ble her funnet at en signifikant høyere prediktiv evne kan oppnås ved å identifisere de regionene i tumor som demonstrerer mest abnormale egenskaper. I denne oppgaven ble det funnet at maligne brystlesjoner demonstrerte en signifikant kortere TTP sammenlignet med benigne brystlesjoner. I tillegg demonstrerte den kvantitative biomarkøren vp en signifikant høyere verdi i maligne brystlesjoner sammenlignet med benigne brystlesjoner. Ved å identifisere tumorvolumenes 95-persnetil av den kvantitative biomarkøren kep, demonstrerte denne en signifikant høyere verdi i maligne brystlesjoner sammenlignet med benigne brystlesjoner. Blant de kvalitative biomarkørene demonstrerte kep en signifikant høyere verdi i maligne brystlesjoner sammenlignet med benigne brystlesjoner. Dersom alle fibroadenom (FA) og invasive duktale karsinom (IDC) betraktes separat demonstrerte den kvalitative ve signifikant høyere verdi i FA. Videre ble det funnet at maligne brystlesjoner demonstrerte en signifikant høyere R2*-forsterkningen sammenlignet med benigne brystlesjoner. Resultatet viser at disse biomarkørene kan anvendes som diagnostiske prediktorer ved evaluering av pasienter med brystkreft. Basert på tumorvolumets 95-persentil ble det etablert multivariate regresjonsmodeller gjennom en baklengs trinnvis elimineringsprosess av signifikante biomarkører. Denne analysen viste at kombinasjonen av de tre biomarkørene R2*-peakenh, TTP og den kvantitative vp gav den mest optimale diagnostiske prestasjonen. Regresjonen demonstrerte en diagnostisk nøyaktighet på 93 % med hensyn til å differensiere mellom maligne og benigne brystlesjoner. Dette korresponderer med sensitivitet og spesifisitet på henholdsvis 80 % og 100 %. Dersom FA og IDC betraktes separat ble det oppnådd en diagnostisk nøyaktighet på 98 %. Dette korresponderer med en sensitivitet og spesifisitet på henholdsvis 93 % og 93 %. Kvantitativ estimering av farmakokinetiske biomarkører forutsetter en nøyaktig måling av den arterielle inputfunksjonen (AIF). Denne viste seg å være svært vanskelig å måle i alle pasienter, noe som førte til en spredning og unøyaktighet i de kvantitative biomarkørene. En normaliseringsmetode ble derfor introdusert hvor det farmakokinetiske forholdet mellom de forskjellige pasientenes parenkymvev og tumorvev ble identifisert. Denne metoden ble utviklet med det formål å redusere eventuelle feil ved den farmakokinetiske analysen som følge av en insuffisient AIF. Den normaliserte Ktrans ble identifisert som den mest prediktive biomarkøren, og demonstrerte en signifikant høyere verdi i maligne brystlesjoner sammenlignet med benigne brystlesjoner. Ved å introdusere den normaliserte Ktrans som en erstatning for det kvantitative farmakokinetiske bidraget i den logistiske regresjonsmodellen, ble det oppnådd en diagnostisk nøyaktighet på 96 % vedrørende differensieringen av benigne og maligne brystlesjoner. Dette korresponderer med en sensitivitet og spesifisitet på henholdsvis 90 % og 94 %. Dersom FA og IDC betraktes separat, differensierte den multivariate regresjonsmodellen suksessfult samtlige av disse. Resultatene viste at den dynamiske bildeinformasjonen som er ervervet fra de høyt temporale bildene, introduserer en verdifull informasjon som kan bistå den diagnostiske vurderingen av pasienter med brystkreft

2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data

IntroductionManagement of patients with brain metastases is often based on manual lesion detection and segmentation by an expert reader. This is a time- and labor-intensive process, and to that end, this work proposes an end-to-end deep learning segmentation network for a varying number of available MRI available sequences.MethodsWe adapt and evaluate a 2.5D and a 3D convolution neural network trained and tested on a retrospective multinational study from two independent centers, in addition, nnU-Net was adapted as a comparative benchmark. Segmentation and detection performance was evaluated by: (1) the dice similarity coefficient, (2) a per-metastases and the average detection sensitivity, and (3) the number of false positives.ResultsThe 2.5D and 3D models achieved similar results, albeit the 2.5D model had better detection rate, whereas the 3D model had fewer false positive predictions, and nnU-Net had fewest false positives, but with the lowest detection rate. On MRI data from center 1, the 2.5D, 3D, and nnU-Net detected 79%, 71%, and 65% of all metastases; had an average per patient sensitivity of 0.88, 0.84, and 0.76; and had on average 6.2, 3.2, and 1.7 false positive predictions per patient, respectively. For center 2, the 2.5D, 3D, and nnU-Net detected 88%, 86%, and 78% of all metastases; had an average per patient sensitivity of 0.92, 0.91, and 0.85; and had on average 1.0, 0.4, and 0.1 false positive predictions per patient, respectively.Discussion/ConclusionOur results show that deep learning can yield highly accurate segmentations of brain metastases with few false positives in multinational data, but the accuracy degrades for metastases with an area smaller than 0.4 cm2

Multimodal Dynamic MRI for Structural and Functional Assessment of Cancer

In this work, a novel dynamic contrast-based magnetic resonance imaging (MRI) method, called ‘split dynamic MRI’, is presented, which show to improve the differential diagnosis of breast cancer, as compared to conventional MRI interpretations, and facilitate staging of primary rectal cancer and lymph node metastasis. Our results showed a diagnostic accuracy of 96% for distinguishing malignant from benign breast cancer, and 90% when differentiating rectal cancer patients with and without lymph node metastasis. The proposed method has a unique advantage over conventional dynamic MRI, as it provides comprehensive information about both structural and physiological characteristics in the tumour. Consequently, combining these information channels may provide a more extensive characterization of tumour aggressiveness, and thus identify patients in need of either intensified or reduced treatment (personalized treatment). Through mathematical simulations, this work show that the split dynamic MRI method provides reliable information of physiological properties in tumours, as compared to conventional dynamic MRI, without compromising international guidelines concerning image resolution for structural interpretation of breast cancer. Reliable identification of individual tumour aggressiveness may represent a major breakthrough in treatment stratification, as this may enable a more personalized treatment. However, conventional MRI findings are associated with substantial misinterpretation and poor diagnostic accuracy. In this context, our results suggest that the split dynamic MRI method can provide physicians with valuable information for clinical decision making in breast cancer and rectal cancer

Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol

Abstract Background To study deep learning segmentation of knee anatomy with 13 anatomical classes by using a magnetic resonance (MR) protocol of four three-dimensional (3D) pulse sequences, and evaluate possible clinical usefulness. Methods The sample selection involved 40 healthy right knee volumes from adult participants. Further, a recently injured single left knee with previous known ACL reconstruction was included as a test subject. The MR protocol consisted of the following 3D pulse sequences: T1 TSE, PD TSE, PD FS TSE, and Angio GE. The DenseVNet neural network was considered for these experiments. Five input combinations of sequences (i) T1, (ii) T1 and FS, (iii) PD and FS, (iv) T1, PD, and FS and (v) T1, PD, FS and Angio were trained using the deep learning algorithm. The Dice similarity coefficient (DSC), Jaccard index and Hausdorff were used to compare the performance of the networks. Results Combining all sequences collectively performed significantly better than other alternatives. The following DSCs (±standard deviation) were obtained for the test dataset: Bone medulla 0.997 (±0.002), PCL 0.973 (±0.015), ACL 0.964 (±0.022), muscle 0.998 (±0.001), cartilage 0.966 (±0.018), bone cortex 0.980 (±0.010), arteries 0.943 (±0.038), collateral ligaments 0.919 (± 0.069), tendons 0.982 (±0.005), meniscus 0.955 (±0.032), adipose tissue 0.998 (±0.001), veins 0.980 (±0.010) and nerves 0.921 (±0.071). The deep learning network correctly identified the anterior cruciate ligament (ACL) tear of the left knee, thus indicating a future aid to orthopaedics. Conclusions The convolutional neural network proves highly capable of correctly labeling all anatomical structures of the knee joint when applied to 3D MR sequences. We have demonstrated that this deep learning model is capable of automatized segmentation that may give 3D models and discover pathology. Both useful for a preoperative evaluation

Sex differences and tumor blood flow from dynamic susceptibility contrast MRI are associated with treatment response after chemoradiation and long-term survival in rectal cancer

Sex differences, tumor vascularity, and blood flow seen on multiecho dynamic contrast–based MRI scans enabled prediction of treatment response and overall survival in patients with rectal cancer. Background: MRI is the standard tool for rectal cancer staging. However, more precise diagnostic tests that can assess biologic tumor features decisive for treatment outcome are necessary. Tumor perfusion and hypoxia are two important features; however, no reference methods that measure these exist in clinical use. Purpose: To assess the potential predictive and prognostic value of MRI-assessed rectal cancer perfusion, as a surrogate measure of hypoxia, for local treatment response and survival. Materials and Methods: In this prospective observational cohort study, 94 study participants were enrolled from October 2013 to December 2017 (ClinicalTrials.gov: NCT01816607). Participants had histologically confirmed rectal cancer and underwent routine diagnostic MRI, an extended diffusion-weighted sequence, and a multiecho dynamic contrast agent–based sequence. Predictive and prognostic values of dynamic contrast-enhanced, dynamic susceptibility contrast (DSC), and intravoxel incoherent motion MRI were investigated with response to neoadjuvant treatment, progression-free survival, and overall survival as end points. Secondary objectives investigated potential sex differences in MRI parameters and relationship with lymph node stage. Statistical methods used were Cox regression, Student t test, and Mann-Whitney U test.Results: A total of 94 study participants (mean age, 64 years ± 11 [standard deviation]; 61 men) were evaluated. Baseline tumor blood flow from DSC MRI was lower in patients who had poor local tumor response to neoadjuvant treatment (96 mL/min/100 g ± 33 for ypT2–4, 120 mL/min/100 g ± 21 for ypT0–1; P = .01), shorter progression-free survival (hazard ratio = 0.97; 95% confidence interval: 0.96, 0.98; P < .001), and shorter overall survival (hazard ratio = 0.98; 95% confidence interval: 0.98, 0.99; P < .001). Women had higher blood flow (125 mL/min/100 g ± 27) than men (74 mL/min/100 g ± 26, P < .001) at stage 4. Volume transfer constant and plasma volume from dynamic contrast-enhanced MRI as well as ΔR2* peak and area under the curve for 30 and 60 seconds from DSC MRI were associated with local malignant lymph nodes (pN status). Median area under the curve for 30 seconds was 0.09 arbitrary units (au) ± 0.03 for pN1–2 and 0.19 au ± 0.12 for pN0 (P = .001). Conclusion:Low tumor blood flow from dynamic susceptibility contrast MRI was associated with poor treatment response in study participants with rectal cancer

Dynamic multi-echo DCE- and DSC-MRI in rectal cancer: Low primary tumor Ktrans and ΔR2* peak are significantly associated with lymph node metastasis

Purpose: To implement a dynamic contrast-based multi-echo MRI sequence in assessment of rectal cancer and evaluate associations between histopathologic data and the acquired dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) -MRI parameters. Materials and Methods: This pilot study reports results from 17 patients with resectable rectal cancer. Dynamic contrast-based multi-echo MRI (1.5T) was acquired using a three-dimensional multi-shot EPI sequence, yielding both DCE- and DSC-data following a single injection of contrast agent. The Institutional Review Board approved the study and all patients provided written informed consent. Quantitative analysis was performed by pharmacokinetic modeling on DCE data and tracer kinetic modeling on DSC data. Mann-Whitney U-test and receiver operating characteristics curve statistics was used to evaluate associations between histopathologic data and the acquired DCE- and DSC-MRI parameters. Results: For patients with histologically confirmed nodal metastasis, the primary tumor demonstrated a significantly lower Ktrans and peak change in R 2, R 2-peakenh, than patients without nodal metastasis, showing a P-value of 0.010 and 0.005 for reader 1, and 0.043 and 0.019 for reader 2, respectively. Conclusion: This study shows the feasibility of acquiring DCE- and DSC-MRI in rectal cancer by dynamic multi-echo MRI. A significant association was found between both Ktrans and R 2-peakenh in the primary tumor and histological nodal status of the surgical specimen, which may improve stratification of patients to intensified multimodal treatment

Responses in the diffusivity and vascular function of the irradiated normal brain are seen up until 18 months following SRS of brain metastases

Background MRI may provide insights into longitudinal responses in the diffusivity and vascular function of the irradiated normal-appearing brain following stereotactic radiosurgery (SRS) of brain metastases. Methods Forty patients with brain metastases from non-small cell lung cancer (N = 26) and malignant melanoma (N = 14) received SRS (15–25 Gy). Longitudinal MRI was performed pre-SRS and at 3, 6, 9, 12, and 18 months post-SRS. Measures of tissue diffusivity and vascularity were assessed by diffusion-weighted and perfusion MRI, respectively. All maps were normalized to white matter receiving less than 1 Gy. Longitudinal responses were assessed in normal-appearing brain, excluding tumor and edema, in the LowDose (1–10 Gy) and HighDose (>10 Gy) regions. The Eastern Cooperative Oncology Group (ECOG) performance status was recorded pre-SRS. Results Following SRS, the diffusivity in the LowDose region increased continuously for 1 year (105.1% ± 6.2%; P < .001), before reversing toward pre-SRS levels at 18 months. Transient reductions in microvascular cerebral blood volume (P < .05), blood flow (P < .05), and vessel densities (P < .05) were observed in LowDose at 6–9 months post-SRS. Correspondingly, vessel calibers in LowDose transiently increased at 3–9 months (P < .01). The responses in HighDose displayed similar trends as in LowDose, but with larger interpatient variations. Vascular responses followed pre-SRS ECOG status. Conclusions Our results imply that even low doses of radiation to normal-appearing brain following cerebral SRS induce increased diffusivity and reduced vascular function for up until 18 months. In particular, the vascular responses indicate the reduced ability of the normal-appearing brain tissue to form new capillaries. Assessing the potential long-term neurologic effects of SRS on the normal-appearing brain is warranted