106 research outputs found
Virtual biopsy in abdominal pathology: where do we stand?
Get PDFIn recent years, researchers have explored new ways to obtain information from pathological tissues, also exploring non-invasive techniques, such as virtual biopsy (VB). VB can be defined as a test that provides promising outcomes compared to traditional biopsy by extracting quantitative information from radiological images not accessible through traditional visual inspection. Data are processed in such a way that they can be correlated with the patient’s phenotypic expression, or with molecular patterns and mutations, creating a bridge between traditional radiology, pathology, genomics, and artificial intelligence (AI). Radiomics is the backbone of VB, since it allows the extraction and selection of features from radiological images, feeding them into AI models in order to derive lesions' pathological characteristics and molecular status. Presently, the output of VB provides only a gross approximation of the findings of tissue biopsy. However, in the future, with the improvement of imaging resolution and processing techniques, VB could partially substitute the classical surgical or percutaneous biopsy, with the advantage of being non-invasive, comprehensive, accounting for lesion heterogeneity, and low cost. In this review, we investigate the concept of VB in abdominal pathology, focusing on its pipeline development and potential benefits
CT texture analysis to predict response to target therapy of hepatic metastases from colorectal cancer
No full textIntroduction Colorectal cancer (CRC), the 2nd cause of cancer death worldwide, is an indolent disease with 50% of patients eventually developing liver metastases (mCRC). Repeated cycles of different chemotherapies, combined with surgery in oligo-metastatic cases, are the therapeutic standard in mCRC. However, this strategy is resolutive in less than 15% of cases. Differentiating non- and short-term responders from potentially “cured” patients will spare patients needless toxicity and allow alternative treatments earlier, with conceivable cost and life savings. In this study we aimed to use CT texture analysis (CTTA) to identify specific imaging biomarkers of hepatic metastases, able to predict patient’s response to therapy and overall survival.
Methods We exploited the imaging data-set of the HERACLES trial (NCT03225937): 23 patients with amplified Human Epidermal growth factor Receptor 2 (HER2) mCRC were included in the study. All had received anti HER2 treatment, and underwent CT examination every 8 weeks, until disease progression. CT scans were semi-automatically segmented to extract for each patient all liver metastases. Texture analysis was performed on each segmented area, computing for each lesion 34 quantitative parameters. Both mono-parametric and multi-parametric analysis were assessed to identify features most correlated to therapy response. We also performed a correlative survival (OS) analysis, considering subjects with good survival those with OS > 9 months.
Results In 23 patients we found 124 metastases, 55 of which were classified as responding and 69 as non-responding. Nine parameters reached statistical significance in the mono-parametric analysis (best AUC=0.67, p=0.001), while in the multivariate regression ten parameters were used in the model, achieving and AUC equal to 0.82, with sensitivity of 82% and specificity 72%. For OS analysis, 12 patients were “good” and 11 “poor” survivors. In the mono-parametric analysis “cluster prominence” and “sum entropy” predicted OS with AUC equal to 0.78 and 0.83, respectively. The regression model with two variables (“cluster prominence” and “dissimilarity”) reached a sensitivity of 83% and a specificity of 82%.
Conclusions Our study demonstrated CTTA as a potential biomarker to predict response of hepatic metastases to chemotherapy treatment, possibly saving patients predicted as non-responder from toxicity. Moreover, CTTA could give indications on patients OS, without the need for additional tests
Comparison between PUN and Tofts models in the quantification of dynamic contrast-enhanced MR imaging
Get PDFDynamic contrast-enhanced study in magnetic resonance imaging (DCE-MRI) is an important tool in oncology to visualize tissues vascularization and to define tumour aggressiveness on the basis of an altered perfusion and permeability. Pharmacokinetic models are generally used to extract hemodynamic parameters, providing a quantitative description of the contrast uptake and wash-out. Empirical functions can also be used to fit experimental data without the need of any assumption about tumour physiology, as in pharmacokinetic models, increasing their diagnostic utility, in particular when automatic diagnosis systems are implemented on the basis of an MRI multi-parametric approach. Phenomenological universalities (PUN) represent a novel tool for experimental research and offer a simple and systematic method to represent a set of data independent of the application field. DCE-MRI acquisitions can thus be advantageously evaluated by the extended PUN class, providing a convenient diagnostic tool to analyse functional studies, adding a new set of features for the classification of malignant and benign lesions in computer aided detection systems. In this work the Tofts pharmacokinetic model and the class EU1 generated by the PUN description were compared in the study of DCE-MRI of the prostate, evaluating complexity of model implementation, goodness of fitting results, classification performances and computational cost. The mean R2 obtained with the EU1 and Tofts model were equal to 0.96 and 0.90, respectively, and the classification performances achieved by the EU1 model and the Tofts implementation discriminated malignant from benign tissues with an area under the receiver operating characteristic curve equal to 0.92 and 0.91, respectively. Furthermore, the EU1 model has a simpler functional form which reduces implementation complexity and computational time, requiring 6 min to complete a patient elaboration process, instead of 8 min needed for the Tofts model analysi
Heart Failure With Mid-range or Recovered Ejection Fraction: Differential Determinants of Transition
Get PDFThe recent definition of an intermediate clinical phenotype of heart failure (HF) based on an ejection fraction (EF) of between 40% and 49%, namely HF with mid-range EF (HFmrEF), has fuelled investigations into the clinical profile and prognosis of this patient group. HFmrEF shares common clinical features with other HF phenotypes, such as a high prevalence of ischaemic aetiology, as in HF with reduced EF (HFrEF), or hypertension and diabetes, as in HF with preserved EF (HFpEF), and benefits from the cornerstone drugs indicated for HFrEF. Among the HF phenotypes, HFmrEF is characterised by the highest rate of transition to either recovery or worsening of the severe systolic dysfunction profile that is the target of disease-modifying therapies, with opposite prognostic implications. This article focuses on the epidemiology, clinical characteristics and therapeutic approaches for HFmrEF, and discusses the major determinants of transition to HFpEF or HFrEF
Delphinium umbrosum Handel-Mazzetti 1931
No full textDelphinium umbrosum Handel-Mazzetti (1931: 278). Figs. 7–9. Type:— CHINA. Yunnan: Ngukala between Zhongdian town and Djitsung (= Qizong) village in Weixi, in Abies forest, alt. 3750–3800 m, 25 August 1915, H. Handel-Mazzetti 7809 (holotype WU!). Fig. 7. Description:—Perennial herbs. Rootstock thickened, woody. Stems erect, 30–110 cm tall, simple or branched, shortly retrorsely appressed strigose. Basal and proximal leaves withered by anthesis; cauline leaves long petiolate; petioles 8–20 cm long, sparsely retrorsely strigose, somewhat dilated at base; blades pentagonal-reniform, membranous, very sparsely strigose or subglabrous on both surfaces, 4–10 cm long, 5–12 cm broad, base cordate, 3-lobed to 5–10 mm from the base, central lobe rhombic or narrowly so, base cuneate, 3-lobulate, incised dentate, lateral lobes obliquely flabellate, often 2-lobulate; leaves gradually reduced upward, distal ones smaller, shortly petiolate. Inflorescence a terminal, simple or sometimes compound raceme, 10–30 cm long, 6–15-flowered, densely retrorsely strigose and yellowish glandular-puberulent; proximal bracts leaflike, distal ones small, linear; pedicels ascending or horizontal, densely retrorsely strigose and yellowish glandular-puberulent, 2–8 cm long; bracteoles distal, strigose, linear or linear-subulate, 0.8–1.9 cm long, 0.5–1.8 mm broad. Sepals blue-purple, abaxially puberulent; upper sepal obovate or broadly ovate, 8.5–15 mm long, ca. 8 mm broad, obtuse, spur subulate, 2–2.9 cm long, strongly recurved, base 2–3 mm in diameter, narrowed into a very slender tip; lateral and lower sepals obovate or broadly ovate, 8–16 mm long, ca. 8 mm broad, apex rounded-truncate. Petals bluish; lamina entire, obtuse at apex, glabrous, spur very slender, 2–2.9 mm long. Staminodes bluish, 2-lobed; lobes lanceolate-triangular, 3–6 mm long, white barbate; claws 3–8 mm long. Stamens glabrous. Carpels 3; ovaries sparsely puberulent. Follicles subglabrous. Distribution and habitat:— Delphinium umbrosum is distributed in northwestern Yunnan (Dali, Deqen, Eryuan, Fugong, Weixi, Zhongdian), China (Fig. 6). It grows in grassy places or thickets at margin of forests or along stream sides at altitudes of 2750–3900 m. Phenology:—Flowering from August to September; fruiting from September to October. Additional specimens examined:— CHINA. Yunnan: Dali, Q.E. Yang 9419 (PE00500052, PE00500053); Deqen, C.W. Wang 69050 (PE00500056, PE00500057), C.W. Wang 69052 (LBG00051744, KUN0686098, NAS00184160, PE00500058, PE00500059, WUK); Eryuan, J.M. Delavay 4109 (P00197190, P00197191, P00197192, PE00476715); Fugong, Q. Lin 791959 (KUN0686096, KUN0686097); Weixi, R.C. Ching 22022 (KUN0685180, PE0685181), H.T. Tsai 57615 (KUN0685728, LBG00051758, PE00476713), H.T. Tsai 59689 (KUN0685258, KUN0685260, LBG00051764, NAS00183753, PE00500051), H.T. Tsai 59749 (KUN0685259, KUN0685261, LBG00051757, NAS00183752, PE00500050), H.T. Tsai 59814 (KUN0685262, LBG 00051763, NAS00183751, PE00476707); Zhongdian, Zhongdian Exped. 1426 (HITBC-herbarium no. 074577, KUN0686093, KUN0686094), Z.D. Fang 0996 (SABG004066). Notes:— Handel-Mazzetti (1931) described Delphinium umbrosum on the basis of a single collection, HandelMazzetti 7809 (WU; Fig. 7), from Zhongdian county in northwestern Yunnan, China. In the protologue, the author stated that this species was related to D. delavayi Franchet (1886: 379), but the latter species differed by having thicker leaves, lanceolate and sessile bracts, much less reflexed spur, much more obliquely truncate upper petals, densely ciliate lateral petals, and very pilose ovaries (“Affine D. Delavayi Franch. quod differt foliis crassioribus, bracteis lanceolatis sessilibus, calcare multo minus reflexo, petalis superioribus multo obliquius truncates, lateralibus crebre ciliatis, ovaries valde pilosis”). Since its description, D. umbrosum has been recognized as an independent species by Wang (1962, 1979 a, 1993, 1997, 2000, 2020), Munz (1968), and Wang & Warnock (2001). Wang (1962) referred ten collections to Delphinium umbrosum, including C.W. Wang 69050 (PE), C.W. Wang 69052 (PE) from Deqen county in northwestern Yunnan and T.T. Yu 979 (PE), J.Q. Hu et al. 3824 (PE), J.Q. Hu et al. 3859 (PE) all from Yuexi county, J.Q. Hu et al. 1132 (PE), J.Q. Hu et al. 1554 (PE), J.Q. Hu et al. 1607 (PE), J.Q. Hu et al. 4248 (PE) all from Hongxi county (now Meigu county), and T.T. Yu 3417 (PE) from Leibo county all in southwestern Sichuan. Wang (1965) considered that only the two Yunnan collections (Fig. 8) belonged to D. umbrosum. He thus described a new variety, D. umbrosum var. hispidum, to accommodate the Sichuan collections, with J.Q. Hu et al. 1607 (PE; Fig. 12A) designated as holotype. He stated that the new variety differed from the type variety, var. umbrosum, by having stem proximally spreading hispid (vs. retrorsely puberulent) and thinner (vs. thicker) (2‒3.5 mm vs. 5‒10 mm in diameter). This treatment was accepted by Wang (1979a, 2020) and Wang & Warnock (2001). We have examined all the type material of Delphinium umbrosum var. hispidum and determined that this variety is identical with D. omeiense, a species described on the basis of six collections also from southwestern Sichuan and since its description generally recognized by Wang (1979 a, 1984, 2000, 2016, 2020), Wang & Wang (2000), Wang & Warnock (2001), and Xie (2016). In fact, the four sheets of Sichuan Econ. Pl. Exped. 1607 (PE; Fig. 12A–D), which constitute the type collection of D. umbrosum var. hispidum, belong also to a paratype collection of D. omeiense (Wang 1979b; Fig. 12B), because J.Q. Hu et al. 1607 and Sichuan Econ. Pl. Exped. 1607 are actually the same collection, with Hu as the leading participant of the expedition (Fig. 12C). Our critical examination indicates that this collection is not mixed, comprising only one taxon. We will deal with D. omeiense in detail elsewhere; in addition to D. umbrosum var. hispidum, several other names should also be reduced to the synonymy of this species.Published as part of Yuan, Qiong & Yang, Qin-Er, 2022, Taxonomic studies on the genus Delphinium (Ranunculaceae) from China (XXII): Clarifying morphological distinction between D. drepanocentrum and D. umbrosum and synonymizing D. umbrosoides with D. drepanocentrum, pp. 243-258 in Phytotaxa 572 (3) on pages 250-257, DOI: 10.11646/phytotaxa.572.3.3, http://zenodo.org/record/732218
An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images
Get PDFLiver metastases (mts) from colorectal cancer (CRC) can have different responses to chemotherapy in the same patient. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts. 22 radiomic features (RF) were computed on pretreatment portal CT scans following a manual segmentation of mts. RFs were extracted from 7x7 Region of Interests (ROIs) that moved across the image by step of 2 pixels. Liver mts were classified as non-responder (R-) if their largest diameter increased more than 3 mm after 3 months of treatment and responder (R+), otherwise. Features selection (FS) was performed by a genetic algorithm and classification by a Support Vector Machine (SVM) classifier. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values were evaluated for all lesions in the training and validation sets, separately. On the training set, we obtained sensitivity of 86%, specificity of 67%, PPV of 89% and NPV of 61%, while, on the validation set, we reached a sensitivity of 73%, specificity of 47%, PPV of 64% and NPV of 57%. Specificity was biased by the low number of R- lesions on the validation set. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance— to personalize treatment of patients with metastastic colorectal cancer, based on the likelihood of response to chemotherapy of each liver metastasis
Un ritaglio di Seicento fiorentino in Piemonte
No full textIl contributo identifica due autori di dipinti del Seicento fiorentino esposti nelle collezioni civiche di Palazzo Mazzetti ad Asti. Quindi contestualizza e discute le due opere all'interno del corpus dei rispettivi pittori: Simone Pignoni (Firenze, 1611-1698) e Alessandro Rosi (Firenze, 1627-1696)
Virtual biopsy in prostate cancer: can machine learning distinguish low and high aggressive tumors on MRI?
Get PDFIn the last decades, MRI was proven a useful tool for the diagnosis and characterization of Prostate Cancer (PCa). In the literature, many studies focused on characterizing PCa aggressiveness, but a few have distinguished between low-aggressive (Gleason Grade Group (GG) =3) PCas based on biparametric MRI (bpMRI). In this study, 108 PCas were collected from two different centers and were divided into training, testing, and validation set. From Apparent Diffusion Coefficient (ADC) map and T2-Weighted Image (T2WI), we extracted texture features, both 3D and 2D, and we implemented three different methods of Feature Selection (FS): Minimum Redundance Maximum Relevance (MRMR), Affinity Propagation (AP), and Genetic Algorithm (GA). From the resulting subsets of predictors, we trained Support Vector Machine (SVM), Decision Tree, and Ensemble Learning classifiers on the training set, and we evaluated their prediction ability on the testing set. Then, for each FS method, we chose the best classifier, based on both training and testing performances, and we further assessed their generalization capability on the validation set. Between the three best models, a Decision Tree was trained using only two features extracted from the ADC map and selected by MRMR, achieving, on the validation set, an Area Under the ROC (AUC) equal to 81%, with sensitivity and specificity of 77% and 93%, respectively.Clinical Relevance- Our best model demonstrated to be able to distinguish low-aggressive from high-aggressive PCas with high accuracy. Potentially, this approach could help clinician to noninvasively distinguish between PCas that might need active treatment and those that could potentially benefit from active surveillance, avoiding biopsy-related complications
EditorialReply to Anwar R. Padhani, Ivo G. Schoots, Jelle O. Barentsz. Fast Magnetic Resonance Imaging as a Viable Method for Directing the Prostate Cancer Diagnostic Pathway. Eur Urol Oncol 2021;4:863-5 Fast-MRI Feasibility in Biopsy-naive Patients: Clarifications on the Study Methods and Results
No full textA Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images
Get PDFThe aim of the study is to present a new Convolutional Neural Network (CNN) based system for the automatic segmentation of the colorectal cancer. The algorithm implemented consists of several steps: a pre-processing to normalize and highlights the tumoral area, the classification based on CNNs, and a post-processing aimed at reducing false positive elements. The classification is performed using three CNNs: each of them classifies the same regions of interest acquired from three different MR sequences. The final segmentation mask is obtained by a majority voting. Performances were evaluated using a semi-automatic segmentation revised by an experienced radiologist as reference standard. The system obtained Dice Similarity Coefficient (DSC) of 0.60, Precision (Pr) of 0.76 and Recall (Re) of 0.55 on the testing set. After applying the leave-one-out validation, we obtained a median DSC=0.58, Pr=0.74, Re=0.54. The promising results obtained by this system, if validated on a larger dataset, could strongly improve personalized medicine
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